1 Introduction
The set of analyses performed in tmrc3 sample estimation has become unorganized and difficult to follow. This is intended to start at the level of broad organization before stepping into the analyses.
As of 20220224, this document is attempting to synchronize with the google doc named ‘TMRC3_Aug18_2021’. I am hoping therefore to have blocks named according to the figures/etc in that document.
1.1 Goals
These samples are from patients who either successfully cleared a Leishmania panamensis infection following treatment, or did not. They include biopsies from each patient along with purifications for Monocytes, Neutrophils, and Eosinophils. When possible, this process was repeated over three visits; but some patients did not return for the second or third visit.
The over-arching goal is to look for attributes(most likely genes) which distinguish patients who do and do not cure the infection after treatment. If possible, these will be apparent on the first visit.
1.2 Relevant Metadata
The metadata factors (in no particular order) of the experiment are:
- Visit. 1, 2, or 3.
- Patient.
- Clinical outcome: cure or fail. This includes the population of patients who did not return and are labeled ‘lost’. This is, by definition, confounded with patient.
- Drug treatment. Most of the patients were treated with an antimonial, but a few were treated with miltefosine.
- Cell type or biopsy.
Metadata was also collected for the patients and there are many factors which have potential to affect the outcome. These analyses will generally remain agnostic for these factors, which include (among other things):
- sex
- ethnicity
- age
- ulcer/lesion attributes (by visit)
- amount of time spent with infection
1.3 Libraries, sequencing, mapping, quantification
The samples used in these analyses were all collected, purified, and libraries generated by the scientists/doctors at CIDEIM. The sequencing libraries were generated via the TruSeq non-stranded library kit and sequenced either at JHU or UMD; earlier samples were single-ended, but most were paired.
All samples were trimmed with trimomatic using the same set of parameters. All mapping was performed with hisat2 version 2.2.1. Quantifications were also performed with salmon version 1.2.0. The reference genome used was hg38 100 (released 202004). When samples were mapped against L.panamensis, the TriTrypDB version 36 reference was used. The set of annotations were therefore limited to ensembl’s 2020 release. The early samples were actually first mapped with hg38 91 and later redone.
1.4 Types of analyses included
This document will limit itself to a set of canonical RNAseq analyses. It must therefore create files containing the raw data to facilitate sharing the data. It will plot metrics of the data to demonstrate the sequencing quality and clustering of the samples under the various conditions examined and normalizations employed. It will perform differential expression analyses for the metadata factors of interest alongside likelihood ratio tests for factors like celltype and time. Given the sets of over/under expressed genes observed in the various DE methods, it will perform the likely gene set tests for over represented gene ontology groups, reactome, etc. Simultaneously, the raw data will be passed to gene set variance analyses to see if there are groups of genes overrepresented in other experiments.
1.5 Metadata collection
There are two metadata sources:
- The online sample sheet, which I periodically update and download into the ‘sample_sheets/’ directory.
- The crf metadata, describing the individual patients.
samplesheet <- "sample_sheets/tmrc3_samples_202203.xlsx"
crf_metadata <- "sample_sheets/20210825_EXP_ESPECIAL_TMRC3_VERSION_2.xlsx"2 Annotation Collection
The primary annotation sources are:
- Ensembl’s biomart archive from 2020 for human annotations.
- The TriTrypDB release 36 for parasite annotations.
Both provide GO data. They also provide helpful links to other data sources. For the moment, we are focusing on the human annotations.
2.1 Gene annotations
These analyses have focused on gene-level abundances/differences. Thus, when htseq-count was invoked against the hisat2-based mappings, parameters were chosen to count genes rather than transcripts. Similarly, when salmon counts were used via tximport, a mapping of genes to transcripts was used to collapse the matrix to gene-level abundances. This decision may be revisited.
hs_annot <- load_biomart_annotations(year="2020", month="jan")## The biomart annotations file already exists, loading from it.hs_annot <- hs_annot[["annotation"]]
hs_annot[["transcript"]] <- paste0(rownames(hs_annot), ".", hs_annot[["version"]])
rownames(hs_annot) <- make.names(hs_annot[["ensembl_gene_id"]], unique=TRUE)
tx_gene_map <- hs_annot[, c("transcript", "ensembl_gene_id")]
summary(hs_annot)## ensembl_transcript_id ensembl_gene_id version transcript_version
## Length:227784 Length:227784 Min. : 1.0 Min. : 1.00
## Class :character Class :character 1st Qu.: 6.0 1st Qu.: 1.00
## Mode :character Mode :character Median :12.0 Median : 1.00
## Mean :10.7 Mean : 3.08
## 3rd Qu.:15.0 3rd Qu.: 5.00
## Max. :28.0 Max. :17.00
##
## hgnc_symbol description gene_biotype cds_length
## Length:227784 Length:227784 Length:227784 Min. : 3
## Class :character Class :character Class :character 1st Qu.: 357
## Mode :character Mode :character Mode :character Median : 694
## Mean : 1140
## 3rd Qu.: 1446
## Max. :107976
## NA's :127222
## chromosome_name strand start_position end_position
## Length:227784 Length:227784 Min. :5.77e+02 Min. :6.47e+02
## Class :character Class :character 1st Qu.:3.11e+07 1st Qu.:3.12e+07
## Mode :character Mode :character Median :6.04e+07 Median :6.06e+07
## Mean :7.41e+07 Mean :7.42e+07
## 3rd Qu.:1.09e+08 3rd Qu.:1.09e+08
## Max. :2.49e+08 Max. :2.49e+08
##
## transcript
## Length:227784
## Class :character
## Mode :character
##
##
##
## 2.2 Gene ontology data
The set of GO categories has not been limited to the 2020 data at the time of this writing.
hs_go <- load_biomart_go()[["go"]]## The biomart annotations file already exists, loading from it.hs_length <- hs_annot[, c("ensembl_gene_id", "cds_length")]
colnames(hs_length) <- c("ID", "length")3 The full dataset
Before we do any of the following subsets/analyses of the data, we need to collect it all in one place. Let’s do that here and name it ‘hs_valid’ meaning it is the set of valid data for Homo sapiens.
sanitize_columns <- c("visitnumber", "clinicaloutcome", "donor",
"typeofcells", "clinicalpresentation", "drug",
"condition", "batch")
hs_expt <- create_expt(samplesheet,
file_column="hg38100hisatfile",
savefile=glue::glue("rda/hs_expt_all-v{ver}.rda"),
gene_info=hs_annot) %>%
exclude_genes_expt(column="gene_biotype", method="keep",
pattern="protein_coding", meta_column="ncrna_lost") %>%
sanitize_expt_metadata(columns=sanitize_columns) %>%
set_expt_factors(columns=sanitize_columns, class="factor") %>%
set_expt_conditions(fact="clinicaloutcome") %>%
set_expt_batches(fact="visitnumber")## Reading the sample metadata.## Dropped 69 rows from the sample metadata because the sample ID is blank.## The sample definitions comprises: 254 rows(samples) and 84 columns(metadata fields).## Warning in create_expt(samplesheet, file_column = "hg38100hisatfile", savefile =
## glue::glue("rda/hs_expt_all-v{ver}.rda"), : Some samples were removed when cross
## referencing the samples against the count data.## Matched 21447 annotations and counts.## Bringing together the count matrix and gene information.## Some annotations were lost in merging, setting them to 'undefined'.## The final expressionset has 21481 features and 246 samples.## remove_genes_expt(), before removal, there were 21481 genes, now there are 19923.## There are 8 samples which kept less than 90 percent counts.## TMRC30044 TMRC30045 TMRC30154 TMRC30017 TMRC30019 TMRC30241 TMRC30015 TMRC30269
## 80.29 73.28 83.16 85.66 89.69 89.37 79.19 89.18## The following should make visit 1 the largest if one uses that column as a size factor when plotting.
meta <- pData(hs_expt) %>%
mutate(visitnumber = fct_relevel(visitnumber, c("notapplicable", "3", "2", "1")))
pData(hs_expt) <- metaThe above block does the following:
- Creates an expressionset using the ‘hg38100hisatfile’ column from the most recently downloaded sample sheet (the column’s name has any puctuation/spaces/capitals/etc removed) and the set of human annotations downloaded above.
- This expressionset is passed to a filter which pulls out only the protein_coding genes and uses the information from that process to add a new metadata column called ‘ncrna_lost’. Thus it keeps a tally of the number of reads lost in the filter and adds it to the sample sheet.
- It is passed again to a function which sanitizes the metadata (there were a couple of entries which said ‘cure’ instead of ‘Cure’ or vice versa) and similarly removes problematic characters.
- Passed to a function which sets some columns explicitly to factors instead of characters.
- Sets the experimental ‘condition’ to the factor of cure vs. fail.
- Sets the experimental ‘batch’ to visit number.
- Resets the levels of the visit number so that the samples which were ‘notapplicable’ are logically before visit 1 which is before 2 before 3.
3.1 Add the CRF patient metadata
Let us also merge in the clinician’s metadata. I worry a little that this might not be allowed for dbGap data, but if it is a problem I suspect we can just remove the bad columns from it. Also note that I rarely use the join function, but it is somewhat required here because I do not want to risk shuffling the metadata when I add the new metadata, which comes from a spreadsheet sorted by patient, not sample. In doing this I therefore just created a new column ‘join’ which contains the shared information, e.g. the patient ID from the existing metadata and the same ID from the CRF file which has been coerced into lowercase.
hs_pd <- pData(hs_expt)
start <- rownames(hs_pd)
hs_crf <- openxlsx::read.xlsx(crf_metadata)
hs_crf[["join"]] <- tolower(hs_crf[["codigo_paciente"]])
hs_pd[["join"]] <- hs_pd[["tubelabelorigin"]]
test <- plyr::join(hs_pd, hs_crf, by="join")
test[["join"]] <- NULL
rownames(test) <- rownames(hs_pd)
na_idx <- is.na(test)
test[na_idx] <- "undefined"
pData(hs_expt) <- testThe above block does the following:
- Extracts the metadata and makes a character vector of the rownames (e.g. the sample IDs).
- Creates a table of the clinical metadata from the file I downloaded.
- Creates a ‘join’ column from both the existing metadata and the clinical metadata which is the patient code, using whatever the column names were in each sheet.
- Joins the two tables using this information. The join function is explicitly used in this context to make sure that the row-order of the entries does not change.
- Gets rid of any entries in the new table which are NA.
- Replaces the metadata of the expressionset with this new, larger table.
4 Sanity check, clinical outcome vs. CRF data
In our shared online sample sheet there is a clinical outcome column which should match the final CRF column ‘ef_lc_estado_final_estudio’ with the caveat that the CRF data is numeric:
0: curacion definitiva 1: fall terapeutica 2: perdida druante el seguimiento 3: excludio durante el estudio
two_columns <- pData(hs_expt)[, c("clinicaloutcome", "ef_lc_estado_final_estudio")]
undef_idx <- two_columns[[2]] == "undefined"
two_columns <- two_columns[!undef_idx, ]
two_columns[["rewritten"]] <- "undef"
cure_idx <- two_columns[[2]] == 0
two_columns[cure_idx, "rewritten"] <- "cure"
fail_idx <- two_columns[[2]] == 1
two_columns[fail_idx, "rewritten"] <- "failure"
lost_idx <- two_columns[[2]] == 2
two_columns[lost_idx, "rewritten"] <- "lost"
same_idx <- two_columns[["clinicaloutcome"]] == two_columns[["rewritten"]]
broken <- two_columns[!same_idx, ]\
broken## Error: <text>:12:35: unexpected '\\'
## 11: same_idx <- two_columns[["clinicaloutcome"]] == two_columns[["rewritten"]]
## 12: broken <- two_columns[!same_idx, ]\
## ^4.1 Collect sample numbers before filtering
dim(pData(hs_expt))## [1] 246 144table(pData(hs_expt)$drug)##
## antimony miltefosine none
## 216 8 22table(pData(hs_expt)$clinic)##
## Cali Tumaco undefined
## 75 143 28table(pData(hs_expt)$typeofcells)##
## biopsy eosinophils macrophages monocytes neutrophils pbmcs
## 21 45 28 74 72 6table(pData(hs_expt)$visit)##
## notapplicable 3 2 1
## 28 54 56 108summary(as.numeric(pData(hs_expt)$eb_lc_tiempo_evolucion))## Warning in summary(as.numeric(pData(hs_expt)$eb_lc_tiempo_evolucion)): NAs
## introduced by coercion## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 2.00 4.00 6.00 7.37 8.00 21.00 60summary(as.numeric(pData(hs_expt)$eb_lc_tto_mcto_glucan_dosis))## Warning in summary(as.numeric(pData(hs_expt)$eb_lc_tto_mcto_glucan_dosis)): NAs
## introduced by coercion## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 13.0 14.0 19.0 59.5 20.0 999.0 60summary(as.numeric(pData(hs_expt)$v3_lc_ejey_lesion_mm_1))## Warning in summary(as.numeric(pData(hs_expt)$v3_lc_ejey_lesion_mm_1)): NAs
## introduced by coercion## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0 7.2 32.4 300.0 999.0 999.0 60summary(as.numeric(pData(hs_expt)$v3_lc_lesion_area_1))## Warning in summary(as.numeric(pData(hs_expt)$v3_lc_lesion_area_1)): NAs
## introduced by coercion## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0 223 999 2133 3016 11487 60summary(as.numeric(pData(hs_expt)$v3_lc_ejex_ulcera_mm_1))## Warning in summary(as.numeric(pData(hs_expt)$v3_lc_ejex_ulcera_mm_1)): NAs
## introduced by coercion## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0 0.0 12.5 292.8 999.0 999.0 60table(pData(hs_expt)$eb_lc_sexo)##
## 1 2 undefined
## 155 31 60table(pData(hs_expt)$eb_lc_etnia)##
## 1 2 3 undefined
## 99 53 34 60summary(as.numeric(pData(hs_expt)$edad))## Warning in summary(as.numeric(pData(hs_expt)$edad)): NAs introduced by coercion## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 18.0 24.0 28.0 29.9 36.0 51.0 60table(pData(hs_expt)$eb_lc_peso)##
## 100.8 53.9 55.9 57.9 58.1 58.3 58.6 59
## 2 9 2 2 7 10 11 14
## 59.6 62 63 67 69.4 74.7 75.6 76.5
## 1 8 6 6 10 3 3 3
## 77 78 79.2 82 83.3 83.4 86.4 87
## 18 10 10 9 4 10 9 3
## 89 93.3 undefined
## 9 7 60table(pData(hs_expt)$eb_lc_estatura)##
## 145 152 154 158 159 160 162 163
## 6 1 10 17 2 6 10 9
## 164 165 166 167 172 173 174 175
## 15 12 19 3 10 4 30 2
## 176 177 182 183 undefined
## 1 10 9 10 60table(pData(hs_expt)$clinic)##
## Cali Tumaco undefined
## 75 143 28table(pData(hs_expt)$ef_lc_estado_final_estudio)##
## 0 1 2 undefined
## 75 69 16 86table(pData(hs_expt)$clinicaloutcome)##
## cure failure lost notapplicable
## 104 60 18 644.2 Define desired colors for the various subsets
There are lots of ways which we will categorize the data, here are some potential color choices for them.
cf_colors <- list(
"cure" = "#998EC3",
"failure" = "#F1A340")
type_visit_colors <- list(
"monocytes_v1" = "#DD1C77",
"monocytes_v2" = "#C994C7",
"monocytes_v3" = "#E7E1EF",
"eosinophils_v1" = "#31A354",
"eosinophils_v2" = "#ADDD8E",
"eosinophils_v3" = "#F7FCD9",
"neutrophils_v1" = "#3182BD",
"neutrophils_v2" = "#9ECAE1",
"neutrophils_v3" = "#DEEBF7",
"biopsy_v1" = "#D95F0E")
type_colors <- list(
"monocytes" = "#DD1C77",
"eosinophils" = "#31A354",
"neutrophils" = "#3182BD",
"biopsy" = "#D95F0E")
visit_colors <- list()
cf_type_colors <- list(
cure_biopsy = "#D95F0E",
failure_biopsy = "#FEC44F",
cure_monocytes = "#DD1C77",
failure_monocytes = "#C994C7",
cure_eosinophils = "#31A354",
failure_eosinophils = "#ADDD8E",
cure_neutrophils = "#3182BD",
failure_neutrophils = "#9ECAE1")Note from Maria Adelaida: Some chemokines are suggestive of Eosinophil recruitment.
4.3 Define the starting data
The following block contains the primary dataset which is the parent of everything which follows.
4.3.1 Set our initial coverage goal
There exists a baseline coverage below which we do not wish to fall. One likely way to approach it heuristically is to assume we should observe some number of genes. With that in mind, I arbitrarily chose 11,000 non-zero genes as the minimum.
With this in mind, here is a non-zero plot before a cutoff of 11,000 genes.
all_nz <- plot_nonzero(hs_expt)
all_nz$plot## Warning: ggrepel: 229 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
To my eyes, there are 3 or 4 samples which are likely candidates for removal.
hs_valid <- subset_expt(hs_expt, nonzero=11000) %>%
subset_expt(subset="clinicaloutcome!='lost'") %>%
subset_expt(subset="clinicaloutcome!='notapplicable'") %>%
subset_expt(subset="clinicaloutcome!='null'") %>%
set_expt_colors(cf_colors)## The samples (and read coverage) removed when filtering 11000 non-zero genes are:## TMRC30050 TMRC30052 TMRC30010
## 807571 3086349 52429## subset_expt(): There were 246, now there are 243 samples.## subset_expt(): There were 243, now there are 226 samples.## subset_expt(): There were 226, now there are 162 samples.## subset_expt(): There were 162, now there are 162 samples.4.3.2 After removal
Here is the distribution of samples after dropping the less than 11,000 gene-samples.
nz_post <- plot_nonzero(hs_valid)
nz_post$plot## Warning: ggrepel: 143 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
4.4 Count up sample types
I recently convinced myself that there is a difference between the data when it does and does not include the miltefosine treated patients.
However, when I actually counted up the samples by a few criteria I immediately relized this is spurious.
table(pData(hs_valid)$drug)##
## antimony miltefosine
## 159 3table(pData(hs_valid)$clinic)##
## Cali Tumaco
## 38 124table(pData(hs_valid)$clinicaloutcome)##
## cure failure
## 104 58table(pData(hs_valid)$typeofcells)##
## biopsy eosinophils monocytes neutrophils
## 17 34 56 55table(pData(hs_valid)$visit)##
## 3 2 1
## 44 45 73summary(as.numeric(pData(hs_valid)$eb_lc_tiempo_evolucion))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.00 4.00 5.00 7.14 8.00 21.00summary(as.numeric(pData(hs_valid)$eb_lc_tto_mcto_glucan_dosis))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 13.0 15.0 19.0 35.7 20.0 999.0summary(as.numeric(pData(hs_valid)$v3_lc_ejey_lesion_mm_1))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 7.2 32.7 340.2 999.0 999.0summary(as.numeric(pData(hs_valid)$v3_lc_lesion_area_1))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 222 999 2000 2448 11487summary(as.numeric(pData(hs_valid)$v3_lc_ejex_ulcera_mm_1))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 35 336 999 999table(pData(hs_valid)$eb_lc_sexo)##
## 1 2
## 137 25table(pData(hs_valid)$eb_lc_etnia)##
## 1 2 3
## 91 37 34summary(as.numeric(pData(hs_valid)$edad))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 18.0 25.0 28.0 29.9 35.0 51.0table(pData(hs_valid)$eb_lc_peso)##
## 100.8 53.9 57.9 58.1 58.3 58.6 59 59.6 62 63 67 69.4 75.6
## 2 9 2 7 10 1 8 1 6 6 6 10 3
## 76.5 77 78 79.2 82 83.3 83.4 86.4 87 89 93.3
## 3 18 10 10 9 4 10 9 3 8 7table(pData(hs_valid)$eb_lc_estatura)##
## 152 154 158 159 160 163 164 165 166 167 172 173 174 176 177 182 183
## 1 10 15 2 6 9 15 12 19 3 10 4 29 1 7 9 10table(pData(hs_valid)$clinic)##
## Cali Tumaco
## 38 124The above block does what it says on the tin, subsets the data to exclude any sample with less than 11,000 observed genes.
4.5 Set up initial data subsets of interest
One of the first global metrics I would like to provide is the set of library sizes. Unfortunately, we have too many samples to fit on a screen now. Therefore, I am going to do an early split of the data by cell type in the hopes that doing so will make it possible to visualize the library sizes/nonzero genes.
The initial factor for this is ‘typeofcells’.
table(pData(hs_valid)[["typeofcells"]])##
## biopsy eosinophils monocytes neutrophils
## 17 34 56 55biopsy_samples <- subset_expt(hs_valid, subset="typeofcells=='biopsy'")## subset_expt(): There were 162, now there are 17 samples.eosinophil_samples <- subset_expt(hs_valid, subset="typeofcells=='eosinophils'")## subset_expt(): There were 162, now there are 34 samples.monocyte_samples <- subset_expt(hs_valid, subset="typeofcells=='monocytes'")## subset_expt(): There were 162, now there are 56 samples.neutrophil_samples <- subset_expt(hs_valid, subset="typeofcells=='neutrophils'")## subset_expt(): There were 162, now there are 55 samples.## Currently, these are not used, but instead I pulled the samples from the hs_clinical
## which means the biopsies are not included.
v1_samples <- subset_expt(hs_valid, subset="visitnumber=='1'")## subset_expt(): There were 162, now there are 73 samples.v1_monocytes <- subset_expt(v1_samples, subset="typeofcells=='monocytes'")## subset_expt(): There were 73, now there are 22 samples.v1_neutrophils <- subset_expt(v1_samples, subset="typeofcells=='neutrophils'")## subset_expt(): There were 73, now there are 22 samples.v1_eosinophils <- subset_expt(v1_samples, subset="typeofcells=='eosinophils'")## subset_expt(): There were 73, now there are 12 samples.v2_samples <- subset_expt(hs_valid, subset="visitnumber=='2'")## subset_expt(): There were 162, now there are 45 samples.v2_monocytes <- subset_expt(v2_samples, subset="typeofcells=='monocytes'")## subset_expt(): There were 45, now there are 17 samples.v2_neutrophils <- subset_expt(v2_samples, subset="typeofcells=='neutrophils'")## subset_expt(): There were 45, now there are 17 samples.v2_eosinophils <- subset_expt(v2_samples, subset="typeofcells=='eosinophils'")## subset_expt(): There were 45, now there are 11 samples.v3_samples <- subset_expt(hs_valid, subset="visitnumber=='3'")## subset_expt(): There were 162, now there are 44 samples.v3_monocytes <- subset_expt(v3_samples, subset="typeofcells=='monocytes'")## subset_expt(): There were 44, now there are 17 samples.v3_neutrophils <- subset_expt(v3_samples, subset="typeofcells=='neutrophils'")## subset_expt(): There were 44, now there are 16 samples.v3_eosinophils <- subset_expt(v3_samples, subset="typeofcells=='eosinophils'")## subset_expt(): There were 44, now there are 11 samples.The above block does a lot of subset operations to create separate data structures on a per-celltype and per-visit basis. Ergo, our large data structure is now joined by ~21 new, smaller data structures.
5 Parasite reads
Let us see if we can make an expressionset of the parasite reads in the TMRC3 samples and distinguish between the faux and real reads. E.g: Are there samples which definitively contain parasites?
lp_expt <- create_expt("sample_sheets/tmrc3_samples_20211207.xlsx",
file_column="lpanamensisv36hisatfile", gene_info = NULL) %>%
subset_expt(coverage=1000) %>%
set_expt_conditions(fact="typeofcells")
visit_fact <- pData(lp_expt)[["visitnumber"]]
batch_na <- is.na(visit_fact)
visit_fact[batch_na] <- "undefined"
lp_expt <- set_expt_batches(lp_expt, fact = visit_fact)
lp_norm <- normalize_expt(lp_expt, filter="simple", norm="quant",
convert="cpm", transform="log2")
plotted <- plot_pca(lp_norm, plot_labels=FALSE)
plotted$plot
plotted_3d <- plot_3d_pca(plotted)The above block is similar in concept to the previous expressionset creation. It uses a different column and currently ignores the gene annotations. Given that many of the samples have essentially 0 reads, I set a cutoff of only 20 observed genes. Finally, I did a quick and dirty PCA plot of this peculiar data structure in the hopes of being able to ‘see’ the difference between what are assumed to be ‘real’ samples with a significant number of ‘real’ parasite reads vs. those samples which just have a couple of potentially spurious reads.
6 Contrasts and colors of interest
This might be a bit early to consider the contrasts, but I think we should consider this question immediately. The main thing we will be comparing is of course cure vs. fail; but we may also look at the visits and compare cell types.
cf_contrasts <- list(
"fail_vs_cure" = c("failure", "cure"))
visit_contrasts <- list(
"v2v1" = c("c2", "c1"),
"v3v1" = c("c3", "c1"),
"v3v2" = c("c3", "c2"))
type_contrasts <- list(
"mono_biopsy" = c("monocytes", "biopsy"),
"eosinophil_biopsy" = c("eosinophils", "biopsy"),
"neutrophil_biopsy" = c("neutrophils", "biopsy"))
visit_cf_contrasts <- list(
"v1fail_vs_cure" = c("v1failure", "v1cure"),
"v2fail_vs_cure" = c("v2failure", "v2cure"),
"v3fail_vs_cure" = c("v3failure", "v3cure"))
clinic_contrasts <- list(
"clinics" = c("Cali", "Tumaco"))Sample IDs starting with 1: Cali IDs starting with 2: Tumaco
7 Distributions/Visualizations of interest
The sets of samples used to visualize the data will also comprise the sets used when later performing the various differential expression analyses.
7.1 Global metrics
Start out with some initial metrics of all samples. The most obvious are plots of the numbers of non-zero genes observed, heatmaps showing the relative relationships among the samples, the relative library sizes, and some PCA. It might be smart to split the library sizes up across subsets of the data, because they have expanded too far to see well on a computer screen.
The most likely factors to query when considering the entire dataset are cure/fail, visit, and cell type. This is the level at which we will choose samples to exclude from future analyses.
plot_legend(biopsy_samples)$plot
plot_libsize(biopsy_samples)$plot
plot_nonzero(biopsy_samples)$plot
biopsy_prepost <- plot_libsize_prepost(biopsy_samples)
biopsy_prepost$count_plot
biopsy_prepost$lowgene_plot## Warning: Using alpha for a discrete variable is not advised.
## Minimum number of biopsy genes: ~ 14,000
plot_libsize(eosinophil_samples)$plot
plot_nonzero(eosinophil_samples)$plot## Warning: ggrepel: 12 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
eosinophil_prepost <- plot_libsize_prepost(eosinophil_samples)
eosinophil_prepost$count_plot
eosinophil_prepost$lowgene_plot## Warning: Using alpha for a discrete variable is not advised.
## Minimum number of eosinophil genes: ~ 13,500
plot_libsize(monocyte_samples)$plot
plot_nonzero(monocyte_samples)$plot## Warning: ggrepel: 43 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
monocyte_prepost <- plot_libsize_prepost(monocyte_samples)
monocyte_prepost$count_plot
monocyte_prepost$lowgene_plot## Warning: Using alpha for a discrete variable is not advised.
## Minimum number of monocyte genes: ~ 7,500 before setting the minimum.
plot_libsize(neutrophil_samples)$plot
plot_nonzero(neutrophil_samples)$plot## Warning: ggrepel: 37 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
neutrophil_prepost <- plot_libsize_prepost(neutrophil_samples)
neutrophil_prepost$count_plot
neutrophil_prepost$lowgene_plot## Warning: Using alpha for a discrete variable is not advised.
## Minimum number of neutrophil genes: ~ 10,000 before setting minimum coverage.The above block just repeats the same two plots on a per-celltype basis: the number of reads observed / sample and a plot of observed genes with respect to coverage. I made some comments with my observations about the number of genes.
7.2 Global PCA
Now that those ‘global’ metrics are out of the way, lets look at some global metrics of the data following normalization; the most likely plots are of course PCA but also a couple of heatmaps.
7.2.1 Figure 1
In the google doc TMRC3_Aug18_2021, there is an example of an image for the first figure:
“Transcriptomic profiles of primary innate cells of CL patients show unique transcriptional signatures - Remove PBMCs and M0, maybe biopsies as well (but Remove WT samples)”
While we were talking in a meeting however, it sounded like there was some desire to keep all cell types. Therefore the following block will have one image with everything and one following the above.
type_valid <- set_expt_conditions(hs_valid, fact="typeofcells") %>%
set_expt_batches(fact="clinicaloutcome") %>%
set_expt_colors(type_colors)
all_norm <- sm(normalize_expt(type_valid, transform="log2", norm="quant",
convert="cpm", filter=TRUE))
all_pca <- plot_pca(all_norm, plot_labels=FALSE,
plot_title="PCA - Cell type", size_column="visitnumber")
dev <- pp(file=glue("images/tmrc3_pca_nolabels-v{ver}.png"))
all_pca$plot
closed <- dev.off()
all_pca$plot
all_pca_nosize <- plot_pca(all_norm, plot_labels=FALSE)
all_pca_nosize$plot
write.csv(all_pca$table, file="coords/hs_donor_pca_coords.csv")
all_cf_norm <- set_expt_batches(all_norm,
fact="visitnumber")
all_cf_corheat <- plot_corheat(all_cf_norm, plot_title="Heirarchical clustering:
cell types")
dev <- pp(file=glue("images/tmrc3_corheat_cf-v{ver}.png"))
all_cf_corheat$plot
closed <- dev.off()
all_cf_corheat$plot
all_cf_disheat <- plot_disheat(all_cf_norm, plot_title="Heirarchical clustering:
cell types")
dev <- pp(file=glue("images/tmrc3_disheat_cf-v{ver}.png"))
all_cf_disheat$plot
closed <- dev.off()
all_cf_disheat$plot
7.3 Figure 1B: Transcriptomic profiles of primary innate
The biggest caveat for this is to ensure that there are no Wellcome Trust samples.
A potential figure legend for the following images might include:
The observed counts per gene for all of the clinical samples were filtered, log transformed, cpm converted, and quantile normalized. The colors were defined by cell types and shapes by patient visit. When the first two principle components were plotted, clustering was observed by cell type. The biopsy samples were significantly different from the innate immune cell types.
fig1v2_norm <- normalize_expt(type_valid, transform="log2",
convert="cpm", norm="quant", filter=TRUE)## Removing 5663 low-count genes (14260 remaining).## transform_counts: Found 506 values equal to 0, adding 1 to the matrix.fig1v2_pca <- plot_pca(fig1v2_norm, cis=FALSE)## plot labels was not set and there are more than 100 samples, disabling it.dev <- pp(file=glue("images/tmrc3_fig1v2.png"))
fig1v2_pca$plot
closed <- dev.off()
fig1v2_pca$plot
fig1v3_samples <- subset_expt(type_valid, subset="condition!='biopsy'")## subset_expt(): There were 162, now there are 145 samples.fig1v3_norm <- normalize_expt(fig1v3_samples, transform="log2",
convert="cpm", norm="quant", filter=TRUE)## Removing 7868 low-count genes (12055 remaining).## transform_counts: Found 110 values equal to 0, adding 1 to the matrix.fig1v3_pca <- plot_pca(fig1v3_norm, cis=FALSE)## plot labels was not set and there are more than 100 samples, disabling it.dev <- pp(file="images/tmrc3_fig1v3.png")
fig1v3_pca$plot
closed <- dev.off()
fig1v3_pca$plot
Continue looking, but switch the conditions/colors so that the clinical outcome becomes the focus and get rid of a few samples which are not actually a part of the TMRC3 focus (e.g. the PBMC and macrophage samples, which are all from the Wellcome Trust).
7.3.1 Clinically relevant samples
Included in this group will be the samples from patients who were lost.
7.3.1.1 Remove the lost samples
In our 20220218 meeting, it was decided that we would focus explicitly on the cure and fail samples, ignoring lost/NA/null samples.
Thus I am adding explicit filters right at the top to exclude them.
7.3.1.2 Samples by clinic
hs_clinical <- hs_valid %>%
set_expt_conditions(fact="clinicaloutcome") %>%
set_expt_batches(fact="typeofcells") %>%
set_expt_colors(cf_colors)
hs_clinical_nobiop <- subset_expt(hs_clinical, subset="typeofcells!='biopsy'")## subset_expt(): There were 162, now there are 145 samples.7.3.1.3 View biopsies
clinic_biopsy <- hs_valid %>%
set_expt_conditions(fact="clinic") %>%
set_expt_batches(fact="clinicaloutcome") %>%
subset_expt(subset="typeofcells=='biopsy'")## subset_expt(): There were 162, now there are 17 samples.clinic_cf <- paste0(pData(clinic_biopsy)$condition, "_",
pData(clinic_biopsy)$batch)
table(clinic_cf)## clinic_cf
## Cali_cure Tumaco_cure Tumaco_failure
## 3 9 5clinic_biopsy <- set_expt_conditions(clinic_biopsy, fact=clinic_cf) %>%
set_expt_batches(fact="visitnumber")
clinic_biopsy_norm <- normalize_expt(clinic_biopsy, transform="log2",
convert="cpm", norm="quant", filter=TRUE)## Removing 6321 low-count genes (13602 remaining).## transform_counts: Found 169 values equal to 0, adding 1 to the matrix.clinic_biopsy_pca <- plot_pca(clinic_biopsy_norm, plot_labels=FALSE)
dev <- pp(file="images/biopsy_place.png")
clinic_biopsy_pca$plot
closed <- dev.off()
clinic_biopsy_pca$plot
clinic_biopsy_nb <- normalize_expt(clinic_biopsy, transform="log2",
convert="cpm", batch="svaseq", filter=TRUE)## Removing 6321 low-count genes (13602 remaining).## Setting 275 low elements to zero.## transform_counts: Found 275 values equal to 0, adding 1 to the matrix.clinic_biopsy_nb_pca <- plot_pca(clinic_biopsy_nb, plot_labels=FALSE)
dev <- pp(file="images/biopsy_place_nb.png")
clinic_biopsy_nb_pca$plot
closed <- dev.off()
clinic_biopsy_nb_pca$plot
7.3.1.4 Clinic Eosinophils
clinic_eosinophil <- hs_clinical %>%
set_expt_conditions(fact="clinic") %>%
set_expt_batches(fact="clinicaloutcome") %>%
subset_expt(subset="typeofcells=='eosinophils'")## subset_expt(): There were 162, now there are 34 samples.clinic_cf <- paste0(pData(clinic_eosinophil)$condition, "_",
pData(clinic_eosinophil)$batch)
table(clinic_cf)## clinic_cf
## Cali_cure Tumaco_cure Tumaco_failure
## 7 18 9clinic_eosinophil <- set_expt_conditions(clinic_eosinophil, fact=clinic_cf) %>%
set_expt_batches(fact="visitnumber")
clinic_eosinophil_norm <- normalize_expt(clinic_eosinophil, transform="log2",
convert="cpm", norm="quant", filter=TRUE)## Removing 9238 low-count genes (10685 remaining).## transform_counts: Found 4 values equal to 0, adding 1 to the matrix.clinic_eosinophil_pca <- plot_pca(clinic_eosinophil_norm, plot_labels=FALSE)
dev <- pp(file="images/eosinophil_place.png")
clinic_eosinophil_pca$plot
closed <- dev.off()
clinic_eosinophil_pca$plot
clinic_eosinophil_nb <- normalize_expt(clinic_eosinophil, transform="log2",
convert="cpm", batch="svaseq", filter=TRUE)## Removing 9238 low-count genes (10685 remaining).## Setting 581 low elements to zero.## transform_counts: Found 581 values equal to 0, adding 1 to the matrix.clinic_eosinophil_nb_pca <- plot_pca(clinic_eosinophil_nb, plot_labels=FALSE)
dev <- pp(file="images/eosinophil_place_nb.png")
clinic_eosinophil_nb_pca$plot
closed <- dev.off()
clinic_eosinophil_nb_pca$plot
7.3.1.5 Clinic Monocyte
clinic_monocyte <- hs_clinical %>%
set_expt_conditions(fact="clinic") %>%
set_expt_batches(fact="clinicaloutcome") %>%
subset_expt(subset="typeofcells=='monocytes'")## subset_expt(): There were 162, now there are 56 samples.clinic_cf <- paste0(pData(clinic_monocyte)$condition, "_",
pData(clinic_monocyte)$batch)
table(clinic_cf)## clinic_cf
## Cali_cure Cali_failure Tumaco_cure Tumaco_failure
## 11 3 23 19clinic_monocyte <- set_expt_conditions(clinic_monocyte, fact=clinic_cf) %>%
set_expt_batches(fact="visitnumber")
clinic_monocyte_norm <- normalize_expt(clinic_monocyte, transform="log2",
convert="cpm", norm="quant", filter=TRUE)## Removing 8867 low-count genes (11056 remaining).## transform_counts: Found 5 values equal to 0, adding 1 to the matrix.clinic_monocyte_pca <- plot_pca(clinic_monocyte_norm, plot_labels=FALSE)
dev <- pp(file="images/monocytes_place.png")
clinic_monocyte_pca$plot
closed <- dev.off()
clinic_monocyte_pca$plot
clinic_monocyte_nb <- normalize_expt(clinic_monocyte, transform="log2",
convert="cpm", batch="svaseq", filter=TRUE)## Removing 8867 low-count genes (11056 remaining).## Setting 1134 low elements to zero.## transform_counts: Found 1134 values equal to 0, adding 1 to the matrix.clinic_monocyte_nb_pca <- plot_pca(clinic_monocyte_nb, plot_labels=FALSE)
dev <- pp(file="images/monocytes_place_nb.png")
clinic_monocyte_nb_pca$plot
closed <- dev.off()
clinic_monocyte_nb_pca$plot
7.4 Clinic Neutrophils
clinic_neutrophil <- hs_clinical %>%
set_expt_conditions(fact="clinic") %>%
set_expt_batches(fact="clinicaloutcome") %>%
subset_expt(subset="typeofcells=='neutrophils'")## subset_expt(): There were 162, now there are 55 samples.clinic_cf <- paste0(pData(clinic_neutrophil)$condition, "_",
pData(clinic_neutrophil)$batch)
table(clinic_cf)## clinic_cf
## Cali_cure Cali_failure Tumaco_cure Tumaco_failure
## 11 3 22 19clinic_neutrophil <- set_expt_conditions(clinic_neutrophil, fact=clinic_cf) %>%
set_expt_batches(fact="visitnumber")
clinic_neutrophil_norm <- normalize_expt(clinic_neutrophil, transform="log2",
convert="cpm", norm="quant", filter=TRUE)## Removing 10713 low-count genes (9210 remaining).## transform_counts: Found 1 values equal to 0, adding 1 to the matrix.clinic_neutrophil_pca <- plot_pca(clinic_neutrophil_norm, plot_labels=FALSE)
dev <- pp(file="images/neutrophil_place.png")
clinic_neutrophil_pca$plot
closed <- dev.off()
clinic_neutrophil_pca$plot
clinic_neutrophil_nb <- normalize_expt(clinic_neutrophil, transform="log2",
convert="cpm", batch="svaseq", filter=TRUE)## Removing 10713 low-count genes (9210 remaining).## Setting 1119 low elements to zero.## transform_counts: Found 1119 values equal to 0, adding 1 to the matrix.clinic_neutrophil_nb_pca <- plot_pca(clinic_neutrophil_nb, plot_labels=FALSE)
dev <- pp(file="images/neutrophil_place_nb.png")
clinic_neutrophil_nb_pca$plot
closed <- dev.off()
clinic_neutrophil_nb_pca$plot
7.4.1 Only Tumaco samples
A majority of samples came from tumaco, let us see what happens if we limit the data to them.
Let us therefore consider a couple of different view of this portion of the data.
tumaco_clinical <- hs_clinical %>%
subset_expt(subset="clinic=='Tumaco'")## subset_expt(): There were 162, now there are 124 samples.tumaco_monocyte <- tumaco_clinical %>%
subset_expt(subset="typeofcells=='monocytes'") %>%
set_expt_conditions(fact="clinicaloutcome") %>%
set_expt_batches(fact="visitnumber") %>%
set_expt_samplenames("tubelabelorigin")## subset_expt(): There were 124, now there are 42 samples.tumaco_monocyte_norm <- normalize_expt(tumaco_monocyte, transform="log2", convert="cpm",
norm="quant", filter=TRUE)## Removing 9057 low-count genes (10866 remaining).## transform_counts: Found 5 values equal to 0, adding 1 to the matrix.tumaco_monocyte_pca <- plot_pca(tumaco_monocyte_norm, plot_labels=FALSE)
dev <- pp(file="images/monocytes_oneplace_norm.png")
tumaco_monocyte_pca$plot
closed <- dev.off()
tumaco_monocyte_pca$plot
tumaco_monocyte_nb <- normalize_expt(tumaco_monocyte, transform="log2", convert="cpm",
batch="svaseq", filter=TRUE)## Removing 9057 low-count genes (10866 remaining).## Setting 705 low elements to zero.## transform_counts: Found 705 values equal to 0, adding 1 to the matrix.tumaco_monocyte_nb_pca <- plot_pca(tumaco_monocyte_nb, plot_labels=FALSE)
dev <- pp(file="images/monocytes_oneplace_norm_sva.png")
tumaco_monocyte_nb_pca$plot
closed <- dev.off()
tumaco_monocyte_nb_pca$plot
7.5 Compare clinics
Let us move a direct comparison of the two clinics right to the top.
hs_clinic <- hs_valid %>%
set_expt_conditions(fact="clinic") %>%
set_expt_batches(fact="typeofcells")
hs_clinic_norm <- normalize_expt(hs_clinic, transform="log2", convert="cpm",
norm="quant", filter=TRUE)## Removing 5663 low-count genes (14260 remaining).## transform_counts: Found 506 values equal to 0, adding 1 to the matrix.hs_clinic_pca <- plot_pca(hs_clinic_norm)## plot labels was not set and there are more than 100 samples, disabling it.hs_clinic_pca$plot
hs_clinic_nb <- normalize_expt(hs_clinic, transform="log2", convert="cpm",
batch="svaseq", filter=TRUE)## Removing 5663 low-count genes (14260 remaining).## Setting 24623 low elements to zero.## transform_counts: Found 24623 values equal to 0, adding 1 to the matrix.hs_clinic_nb_pca <- plot_pca(hs_clinic_nb)## plot labels was not set and there are more than 100 samples, disabling it.hs_clinic_nb_pca$plot
7.5.1 Compare clinics, all samples
hs_clinic_de <- all_pairwise(hs_clinic, model_batch="svaseq", filter=TRUE)## Removing 0 low-count genes (14260 remaining).## Setting 24623 low elements to zero.## transform_counts: Found 24623 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.hs_clinic_table <- combine_de_tables(
hs_clinic_de, keepers=clinic_contrasts,
excel=glue::glue("excel/hs_clinic_table-v{ver}.xlsx"))## Deleting the file excel/hs_clinic_table-v202203.xlsx before writing the tables.hs_clinic_sig <- extract_significant_genes(
hs_clinic_table,
excel=glue::glue("excel/hs_clinic_sig-v{ver}.xlsx"))## Deleting the file excel/hs_clinic_sig-v202203.xlsx before writing the tables.7.5.2 Clinic biopsy comparison
clinic_biopsy_de <- all_pairwise(clinic_biopsy, model_batch="svaseq", filter=TRUE)## Removing 0 low-count genes (13602 remaining).## Setting 275 low elements to zero.## transform_counts: Found 275 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
clinic_biopsy_table <- combine_de_tables(
clinic_biopsy_de,
excel=glue::glue("excel/clinic_biopsy_table-v{ver}.xlsx"))## Deleting the file excel/clinic_biopsy_table-v202203.xlsx before writing the tables.clinic_biopsy_sig <- extract_significant_genes(
clinic_biopsy_table,
excel=glue::glue("excel/clinic_biopsy_sig-v{ver}.xlsx"))## Deleting the file excel/clinic_biopsy_sig-v202203.xlsx before writing the tables.7.5.3 Clinic eosinophil comparison
clinic_eosinophil_de <- all_pairwise(clinic_eosinophil, model_batch=FALSE, filter=TRUE)## Finished running DE analyses, collecting outputs.## Comparing analyses.
clinic_eosinophil_table <- combine_de_tables(
clinic_eosinophil_de,
excel=glue::glue("excel/clinic_eosinophil_table-v{ver}.xlsx"))## Deleting the file excel/clinic_eosinophil_table-v202203.xlsx before writing the tables.clinic_eosinophil_sig <- extract_significant_genes(
clinic_eosinophil_table,
excel=glue::glue("excel/clinic_eosinophil_sig-v{ver}.xlsx"))## Deleting the file excel/clinic_eosinophil_sig-v202203.xlsx before writing the tables.7.5.4 Clinic monocyte comparison
clinic_monocyte_de <- all_pairwise(clinic_monocyte, model_batch=FALSE, filter=TRUE)## Finished running DE analyses, collecting outputs.## Comparing analyses.
clinic_monocyte_table <- combine_de_tables(
clinic_monocyte_de,
excel=glue::glue("excel/clinic_monocyte_table-v{ver}.xlsx"))## Deleting the file excel/clinic_monocyte_table-v202203.xlsx before writing the tables.clinic_monocyte_sig <- extract_significant_genes(
clinic_monocyte_table,
excel=glue::glue("excel/clinic_monocyte_sig-v{ver}.xlsx"))## Deleting the file excel/clinic_monocyte_sig-v202203.xlsx before writing the tables.7.5.5 Clinic neutrophil comparison
clinic_neutrophil_de <- all_pairwise(clinic_neutrophil, model_batch=FALSE, filter=TRUE)## Finished running DE analyses, collecting outputs.## Comparing analyses.
clinic_neutrophil_table <- combine_de_tables(
clinic_neutrophil_de,
excel=glue::glue("excel/clinic_neutrophil_table-v{ver}.xlsx"))## Deleting the file excel/clinic_neutrophil_table-v202203.xlsx before writing the tables.clinic_neutrophil_sig <- extract_significant_genes(
clinic_neutrophil_table,
excel=glue::glue("excel/clinic_neutrophil_sig-v{ver}.xlsx"))## Deleting the file excel/clinic_neutrophil_sig-v202203.xlsx before writing the tables.7.5.6 Compare clinic genes
clinic_sigenes_up <- rownames(hs_clinic_sig[["deseq"]][["ups"]][[1]])
clinic_sigenes_down <- rownames(hs_clinic_sig[["deseq"]][["downs"]][[1]])
clinic_sigenes <- c(clinic_sigenes_up, clinic_sigenes_down)
contrast <- "Tumacocure_vs_Calicure"
clinic_biopsy_sigenes <- c(rownames(clinic_biopsy_sig[["deseq"]][["ups"]][[contrast]]),
rownames(clinic_biopsy_sig[["deseq"]][["downs"]][[contrast]]))
clinic_eosinophil_sigenes_up <- rownames(clinic_eosinophil_sig[["deseq"]][["ups"]][[contrast]])
clinic_eosinophil_sigenes_down <- rownames(clinic_eosinophil_sig[["deseq"]][["downs"]][[contrast]])
clinic_monocyte_sigenes_up <- rownames(clinic_monocyte_sig[["deseq"]][["ups"]][[contrast]])
clinic_monocyte_sigenes_down <- rownames(clinic_monocyte_sig[["deseq"]][["downs"]][[contrast]])
clinic_neutrophil_sigenes_up <- rownames(clinic_neutrophil_sig[["deseq"]][["ups"]][[contrast]])
clinic_neutrophil_sigenes_down <- rownames(clinic_neutrophil_sig[["deseq"]][["downs"]][[contrast]])
clinic_eosinophil_sigenes <- c(clinic_eosinophil_sigenes_up,
clinic_eosinophil_sigenes_down)
clinic_monocyte_sigenes <- c(clinic_monocyte_sigenes_up,
clinic_monocyte_sigenes_down)
clinic_neutrophil_sigenes <- c(clinic_neutrophil_sigenes_up,
clinic_neutrophil_sigenes_down)7.5.7 Place gprofiler
clinic_gp <- simple_gprofiler(clinic_sigenes)## Performing gProfiler GO search of 915 genes against hsapiens.## GO search found 106 hits.## Performing gProfiler KEGG search of 915 genes against hsapiens.## KEGG search found 5 hits.## Performing gProfiler REAC search of 915 genes against hsapiens.## REAC search found 12 hits.## Performing gProfiler MI search of 915 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 915 genes against hsapiens.## TF search found 33 hits.## Performing gProfiler CORUM search of 915 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 915 genes against hsapiens.## HP search found 8 hits.clinic_gp$pvalue_plots$kegg_plot_over
clinic_gp$pvalue_plots$reactome_plot_over
clinic_gp <- simple_gprofiler(clinic_sigenes)## Performing gProfiler GO search of 915 genes against hsapiens.## GO search found 106 hits.## Performing gProfiler KEGG search of 915 genes against hsapiens.## KEGG search found 5 hits.## Performing gProfiler REAC search of 915 genes against hsapiens.## REAC search found 12 hits.## Performing gProfiler MI search of 915 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 915 genes against hsapiens.## TF search found 33 hits.## Performing gProfiler CORUM search of 915 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 915 genes against hsapiens.## HP search found 8 hits.clinic_gp$pvalue_plots$kegg_plot_over
clinic_gp$pvalue_plots$reactome_plot_over
clinic_gp <- simple_gprofiler(clinic_sigenes)## Performing gProfiler GO search of 915 genes against hsapiens.## GO search found 106 hits.## Performing gProfiler KEGG search of 915 genes against hsapiens.## KEGG search found 5 hits.## Performing gProfiler REAC search of 915 genes against hsapiens.## REAC search found 12 hits.## Performing gProfiler MI search of 915 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 915 genes against hsapiens.## TF search found 33 hits.## Performing gProfiler CORUM search of 915 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 915 genes against hsapiens.## HP search found 8 hits.clinic_gp$pvalue_plots$kegg_plot_over
clinic_gp$pvalue_plots$reactome_plot_over
clinic_eosinophil_gp <- simple_gprofiler(clinic_eosinophil_sigenes)## Performing gProfiler GO search of 1136 genes against hsapiens.## GO search found 255 hits.## Performing gProfiler KEGG search of 1136 genes against hsapiens.## KEGG search found 22 hits.## Performing gProfiler REAC search of 1136 genes against hsapiens.## REAC search found 6 hits.## Performing gProfiler MI search of 1136 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 1136 genes against hsapiens.## TF search found 360 hits.## Performing gProfiler CORUM search of 1136 genes against hsapiens.## CORUM search found 6 hits.## Performing gProfiler HP search of 1136 genes against hsapiens.## HP search found 0 hits.clinic_eosinophil_gp$pvalue_plots$kegg_plot_over
clinic_eosinophil_gp$pvalue_plots$reactome_plot_over
clinic_eosinophil_up_gp <- simple_gprofiler(clinic_eosinophil_sigenes_up)## Performing gProfiler GO search of 590 genes against hsapiens.## GO search found 232 hits.## Performing gProfiler KEGG search of 590 genes against hsapiens.## KEGG search found 19 hits.## Performing gProfiler REAC search of 590 genes against hsapiens.## REAC search found 3 hits.## Performing gProfiler MI search of 590 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 590 genes against hsapiens.## TF search found 351 hits.## Performing gProfiler CORUM search of 590 genes against hsapiens.## CORUM search found 6 hits.## Performing gProfiler HP search of 590 genes against hsapiens.## HP search found 0 hits.clinic_eosinophil_up_gp$pvalue_plots$kegg_plot_over
clinic_eosinophil_up_gp$pvalue_plots$reactome_plot_over
clinic_eosinophil_down_gp <- simple_gprofiler(clinic_eosinophil_sigenes_down)## Performing gProfiler GO search of 546 genes against hsapiens.## GO search found 73 hits.## Performing gProfiler KEGG search of 546 genes against hsapiens.## KEGG search found 4 hits.## Performing gProfiler REAC search of 546 genes against hsapiens.## REAC search found 11 hits.## Performing gProfiler MI search of 546 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 546 genes against hsapiens.## TF search found 22 hits.## Performing gProfiler CORUM search of 546 genes against hsapiens.## CORUM search found 6 hits.## Performing gProfiler HP search of 546 genes against hsapiens.## HP search found 0 hits.clinic_eosinophil_down_gp$pvalue_plots$kegg_plot_over
clinic_eosinophil_down_gp$pvalue_plots$reactome_plot_over
clinic_monocyte_gp <- simple_gprofiler(clinic_monocyte_sigenes)## Performing gProfiler GO search of 1241 genes against hsapiens.## GO search found 408 hits.## Performing gProfiler KEGG search of 1241 genes against hsapiens.## KEGG search found 16 hits.## Performing gProfiler REAC search of 1241 genes against hsapiens.## REAC search found 8 hits.## Performing gProfiler MI search of 1241 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 1241 genes against hsapiens.## TF search found 347 hits.## Performing gProfiler CORUM search of 1241 genes against hsapiens.## CORUM search found 6 hits.## Performing gProfiler HP search of 1241 genes against hsapiens.## HP search found 6 hits.clinic_monocyte_gp$pvalue_plots$kegg_plot_over
clinic_monocyte_gp$pvalue_plots$reactome_plot_over
clinic_monocyte_up_gp <- simple_gprofiler(clinic_monocyte_sigenes_up)## Performing gProfiler GO search of 596 genes against hsapiens.## GO search found 369 hits.## Performing gProfiler KEGG search of 596 genes against hsapiens.## KEGG search found 16 hits.## Performing gProfiler REAC search of 596 genes against hsapiens.## REAC search found 5 hits.## Performing gProfiler MI search of 596 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 596 genes against hsapiens.## TF search found 342 hits.## Performing gProfiler CORUM search of 596 genes against hsapiens.## CORUM search found 5 hits.## Performing gProfiler HP search of 596 genes against hsapiens.## HP search found 3 hits.clinic_monocyte_up_gp$pvalue_plots$kegg_plot_over
clinic_monocyte_up_gp$pvalue_plots$reactome_plot_over
clinic_monocyte_down_gp <- simple_gprofiler(clinic_monocyte_sigenes_down)## Performing gProfiler GO search of 645 genes against hsapiens.## GO search found 60 hits.## Performing gProfiler KEGG search of 645 genes against hsapiens.## KEGG search found 2 hits.## Performing gProfiler REAC search of 645 genes against hsapiens.## REAC search found 4 hits.## Performing gProfiler MI search of 645 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 645 genes against hsapiens.## TF search found 206 hits.## Performing gProfiler CORUM search of 645 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 645 genes against hsapiens.## HP search found 4 hits.clinic_monocyte_down_gp$pvalue_plots$kegg_plot_over
clinic_monocyte_down_gp$pvalue_plots$reactome_plot_over
clinic_neutrophil_gp <- simple_gprofiler(clinic_neutrophil_sigenes)## Performing gProfiler GO search of 981 genes against hsapiens.## GO search found 185 hits.## Performing gProfiler KEGG search of 981 genes against hsapiens.## KEGG search found 10 hits.## Performing gProfiler REAC search of 981 genes against hsapiens.## REAC search found 25 hits.## Performing gProfiler MI search of 981 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 981 genes against hsapiens.## TF search found 402 hits.## Performing gProfiler CORUM search of 981 genes against hsapiens.## CORUM search found 1 hits.## Performing gProfiler HP search of 981 genes against hsapiens.## HP search found 1 hits.clinic_neutrophil_gp$pvalue_plots$kegg_plot_over
clinic_neutrophil_gp$pvalue_plots$reactome_plot_over
clinic_neutrophil_up_gp <- simple_gprofiler(clinic_neutrophil_sigenes_up)## Performing gProfiler GO search of 542 genes against hsapiens.## GO search found 157 hits.## Performing gProfiler KEGG search of 542 genes against hsapiens.## KEGG search found 7 hits.## Performing gProfiler REAC search of 542 genes against hsapiens.## REAC search found 2 hits.## Performing gProfiler MI search of 542 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 542 genes against hsapiens.## TF search found 391 hits.## Performing gProfiler CORUM search of 542 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 542 genes against hsapiens.## HP search found 0 hits.clinic_neutrophil_up_gp$pvalue_plots$kegg_plot_over
clinic_neutrophil_up_gp$pvalue_plots$reactome_plot_over
clinic_neutrophil_down_gp <- simple_gprofiler(clinic_neutrophil_sigenes_down)## Performing gProfiler GO search of 439 genes against hsapiens.## GO search found 39 hits.## Performing gProfiler KEGG search of 439 genes against hsapiens.## KEGG search found 8 hits.## Performing gProfiler REAC search of 439 genes against hsapiens.## REAC search found 75 hits.## Performing gProfiler MI search of 439 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 439 genes against hsapiens.## TF search found 15 hits.## Performing gProfiler CORUM search of 439 genes against hsapiens.## CORUM search found 9 hits.## Performing gProfiler HP search of 439 genes against hsapiens.## HP search found 7 hits.clinic_neutrophil_down_gp$pvalue_plots$kegg_plot_over
clinic_neutrophil_down_gp$pvalue_plots$reactome_plot_over
7.5.8 Plot the clinical samples
Let us recolor the same plot by cure/fail followed by a concatenation of the cell type and cure/fail. In the following block, the clinical samples are plotted once with the most common normalization (log,cpm,quant,filtered) method followed by a plot of the data using svaseq adjusted values and without quantile normalization.
Thus, the following block switches the colors of the groups to the clinical state (cure/fail) and shapes by cell type.
hs_clinical_norm <- sm(normalize_expt(hs_clinical, filter="simple", transform="log2",
norm="quant", convert="cpm"))
clinical_pca <- plot_pca(hs_clinical_norm, plot_labels=FALSE,
cis=NULL,
plot_title="PCA - clinical samples")
dev <- pp(file=glue("images/all_clinical_nobatch_pca-v{ver}.png"), height=8, width=16)
clinical_pca$plot
closed <- dev.off()
clinical_pca$plot
hs_clinical_nb <- normalize_expt(hs_clinical, filter="simple", transform="log2",
batch="svaseq", convert="cpm")## Removing 1894 low-count genes (18029 remaining).## Setting 137385 low elements to zero.## transform_counts: Found 137385 values equal to 0, adding 1 to the matrix.hs_clinical_nb_pca <- plot_pca(hs_clinical_nb)## plot labels was not set and there are more than 100 samples, disabling it.dev <- pp(file=glue("images/all_clinical_svaseqbatch_pca-v{ver}.png"), height=6, width=8)
hs_clinical_nb_pca$plot
closed <- dev.off()
hs_clinical_nb_pca$plot
clinical_pca_info <- pca_information(
hs_clinical_norm, plot_pcas=TRUE, num_components = 30,
expt_factors=c("visitnumber", "typeofcells", "clinicaloutcome",
"clinic"))## plot labels was not set and there are more than 100 samples, disabling it.dev <- pp(file="images/clinical_samples_neglogp_pcs.png")
clinical_pca_info$anova_neglogp_heatmap
closed <- dev.off()
clinical_pca_info$anova_neglogp_heatmap
clinical_pca_info$pca_plots$PC20_PC29## Warning: ggrepel: 115 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
clinical_scores <- pca_highscores(hs_clinical_norm)
clinical_scores[["highest"]][,"Comp.20"]## [1] "7.756:ENSG00000266302" "5.744:ENSG00000067048" "4.721:ENSG00000129824"
## [4] "4.715:ENSG00000171860" "4.551:ENSG00000154529" "4.2:ENSG00000162722"
## [7] "4.154:ENSG00000179344" "4.071:ENSG00000163993" "3.937:ENSG00000198692"
## [10] "3.934:ENSG00000196735" "3.901:ENSG00000164125" "3.83:ENSG00000276070"
## [13] "3.771:ENSG00000076716" "3.754:ENSG00000196664" "3.672:ENSG00000115607"
## [16] "3.598:ENSG00000105991" "3.444:ENSG00000104361" "3.263:ENSG00000198502"
## [19] "3.234:ENSG00000184350" "3.213:ENSG00000121807"clinical_scores[["highest"]][,"Comp.27"]## [1] "7.498:ENSG00000137801" "7.383:ENSG00000110203" "7.085:ENSG00000106565"
## [4] "6.72:ENSG00000002933" "5.895:ENSG00000276085" "5.525:ENSG00000049247"
## [7] "5.313:ENSG00000147408" "4.428:ENSG00000115607" "4.021:ENSG00000124882"
## [10] "3.846:ENSG00000154153" "3.613:ENSG00000050820" "3.591:ENSG00000196126"
## [13] "3.419:ENSG00000179344" "3.355:ENSG00000002726" "3.326:ENSG00000168280"
## [16] "3.294:ENSG00000276070" "3.155:ENSG00000090382" "3.117:ENSG00000171916"
## [19] "3.104:ENSG00000149591" "2.972:ENSG00000269028"first <- normalize_expt(hs_clinical, transform="log2", convert="cpm",
filter = TRUE, batch="svaseq", surrogates=1)## Removing 5663 low-count genes (14260 remaining).## Setting 170679 low elements to zero.## transform_counts: Found 170679 values equal to 0, adding 1 to the matrix.first_info <- pca_information(
first, plot_pcas=TRUE, num_components = 30,
expt_factors=c("visitnumber", "typeofcells", "clinicaloutcome",
"clinic"))## plot labels was not set and there are more than 100 samples, disabling it.first_info$anova_neglogp_heatmap
first_info$pca_plots[["PC1_PC2"]]## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure
## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure## Warning: ggrepel: 152 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
second <- normalize_expt(hs_clinical, transform="log2", convert="cpm",
filter = TRUE, batch="svaseq", surrogates=2) %>%
set_expt_batches(fact="clinic")## Removing 5663 low-count genes (14260 remaining).## Setting 28688 low elements to zero.## transform_counts: Found 28688 values equal to 0, adding 1 to the matrix.second_info <- pca_information(
second, plot_pcas=TRUE, num_components = 30,
expt_factors=c("visitnumber", "typeofcells", "clinicaloutcome",
"clinic"))## plot labels was not set and there are more than 100 samples, disabling it.second_info$anova_neglogp_heatmap
third <- normalize_expt(hs_clinical, transform="log2", convert="cpm",
filter = TRUE, batch="svaseq", surrogates=3) %>%
set_expt_batches(fact="clinic")## Removing 5663 low-count genes (14260 remaining).## Setting 23196 low elements to zero.## transform_counts: Found 23196 values equal to 0, adding 1 to the matrix.third_info <- pca_information(
third, plot_pcas=TRUE, num_components = 30,
expt_factors=c("visitnumber", "typeofcells", "clinicaloutcome",
"clinic"))## plot labels was not set and there are more than 100 samples, disabling it.third_info$anova_neglogp_heatmap
fourth <- normalize_expt(hs_clinical, transform="log2", convert="cpm",
filter = TRUE, batch="svaseq", surrogates=4) %>%
set_expt_batches(fact="clinic")## Removing 5663 low-count genes (14260 remaining).## Setting 22153 low elements to zero.## transform_counts: Found 22153 values equal to 0, adding 1 to the matrix.fourth_info <- pca_information(
fourth, plot_pcas=TRUE, num_components = 30,
expt_factors=c("visitnumber", "typeofcells", "clinicaloutcome",
"clinic"))## plot labels was not set and there are more than 100 samples, disabling it.fourth_info$anova_neglogp_heatmap
fourth_info[["pca_plots"]][["PC1_PC2"]]## Warning: ggrepel: 76 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
fifth <- normalize_expt(hs_clinical, transform="log2", convert="cpm",
filter = TRUE, batch="svaseq", surrogates=5) %>%
set_expt_batches(fact="clinic")## Removing 5663 low-count genes (14260 remaining).## Setting 23511 low elements to zero.## transform_counts: Found 23511 values equal to 0, adding 1 to the matrix.fifth_info <- pca_information(
fifth, plot_pcas=TRUE, num_components = 30,
expt_factors=c("visitnumber", "typeofcells", "clinicaloutcome",
"clinic"))## plot labels was not set and there are more than 100 samples, disabling it.fifth_info$anova_neglogp_heatmap
fifth_info[["pca_plots"]][["PC1_PC12"]]## Warning: ggrepel: 111 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
sixth <- normalize_expt(hs_clinical, transform="log2", convert="cpm",
filter = TRUE, batch="svaseq", surrogates=6) %>%
set_expt_batches(fact="clinic")## Removing 5663 low-count genes (14260 remaining).## Setting 21292 low elements to zero.## transform_counts: Found 21292 values equal to 0, adding 1 to the matrix.sixth_info <- pca_information(
sixth, plot_pcas=TRUE, num_components = 30,
expt_factors=c("visitnumber", "typeofcells", "clinicaloutcome",
"clinic"))## plot labels was not set and there are more than 100 samples, disabling it.sixth_info$anova_neglogp_heatmap
seventh <- normalize_expt(hs_clinical, transform="log2", convert="cpm",
filter = TRUE, batch="svaseq", surrogates=7) %>%
set_expt_batches(fact="clinic")## Removing 5663 low-count genes (14260 remaining).## Setting 19835 low elements to zero.## transform_counts: Found 19835 values equal to 0, adding 1 to the matrix.seventh_info <- pca_information(
seventh, plot_pcas=TRUE, num_components = 30,
expt_factors=c("visitnumber", "typeofcells", "clinicaloutcome",
"clinic"))## plot labels was not set and there are more than 100 samples, disabling it.seventh_info$anova_neglogp_heatmap
eighth <- normalize_expt(hs_clinical, transform="log2", convert="cpm",
filter = TRUE, batch="svaseq", surrogates=8)## Removing 5663 low-count genes (14260 remaining).## Setting 19967 low elements to zero.## transform_counts: Found 19967 values equal to 0, adding 1 to the matrix.eighth_info <- pca_information(
eighth, plot_pcas=TRUE, num_components = 30,
expt_factors=c("visitnumber", "typeofcells", "clinicaloutcome",
"clinic"))## plot labels was not set and there are more than 100 samples, disabling it.eighth_info$anova_neglogp_heatmap
7.5.9 Concatenate cure/fail and cell type
In the following block the experimental condition was reset to the concatenation of clinical outcome and type of cells. There are an insufficient number of biopsy samples for them to be useful in this visualization, so they are ignored.
desired_levels <- c("cure_biopsy", "failure_biopsy", "cure_eosinophils", "failure_eosinophils",
"cure_monocytes", "failure_monocytes", "cure_neutrophils", "failure_neutrophils")
new_fact <- factor(
paste0(pData(hs_clinical)[["condition"]], "_",
pData(hs_clinical)[["batch"]]),
levels=desired_levels)
hs_clinical_concat <- set_expt_conditions(hs_clinical, fact = new_fact) %>%
set_expt_batches(fact = "visitnumber") %>%
set_expt_colors(cf_type_colors) %>%
subset_expt(subset="typeofcells!='biopsy'")## subset_expt(): There were 162, now there are 145 samples.## Try to ensure that the levels stay in the order I want
meta <- pData(hs_clinical_concat) %>%
mutate(condition = fct_relevel(condition, desired_levels))## Warning: Unknown levels in `f`: cure_biopsy, failure_biopsypData(hs_expt) <- meta
clinical_concat_norm <- normalize_expt(hs_clinical_concat, transform = "log2", convert = "cpm",
norm = "quant", filter = TRUE)## Removing 7868 low-count genes (12055 remaining).## transform_counts: Found 110 values equal to 0, adding 1 to the matrix.clinical_concat_norm_pca <- plot_pca(clinical_concat_norm)## plot labels was not set and there are more than 100 samples, disabling it.dev <- pp(file=glue("images/clinical_concatenated_normalized_pca-v{ver}.png"), height=6, width=10)
clinical_concat_norm_pca$plot
closed <- dev.off()
clinical_concat_norm_pca$plot
clinical_concat_nb <- normalize_expt(hs_clinical_concat, transform = "log2", convert = "cpm",
batch = "svaseq", filter = TRUE)## Removing 7868 low-count genes (12055 remaining).## Setting 16075 low elements to zero.## transform_counts: Found 16075 values equal to 0, adding 1 to the matrix.clinical_concat_nb_pca <- plot_pca(clinical_concat_nb)## plot labels was not set and there are more than 100 samples, disabling it.dev <- pp(file=glue("images/clinical_concatenated_svaseqbatch_pca-v{ver}.png"), height=6, width=12)
clinical_concat_nb_pca$plot
closed <- dev.off()
clinical_concat_nb_pca$plot
tumaco_concat <- subset_expt(hs_clinical_concat, subset="clinic=='Tumaco'")## subset_expt(): There were 145, now there are 110 samples.tumaco_concat_norm <- normalize_expt(tumaco_concat, transform = "log2", convert = "cpm",
norm = "quant", filter = TRUE)## Removing 8006 low-count genes (11917 remaining).## transform_counts: Found 95 values equal to 0, adding 1 to the matrix.tumaco_concat_norm_pca <- plot_pca(tumaco_concat_norm)## plot labels was not set and there are more than 100 samples, disabling it.dev <- pp(file=glue("images/tumaco_concatenated_normalized_pca-v{ver}.png"), height=6, width=10)
tumaco_concat_norm_pca$plot
closed <- dev.off()
tumaco_concat_norm_pca$plot
tumaco_concat_nb <- normalize_expt(tumaco_concat, transform = "log2", convert = "cpm",
batch = "svaseq", filter = TRUE)## Removing 8006 low-count genes (11917 remaining).## Setting 19028 low elements to zero.## transform_counts: Found 19028 values equal to 0, adding 1 to the matrix.tumaco_concat_nb_pca <- plot_pca(tumaco_concat_nb)## plot labels was not set and there are more than 100 samples, disabling it.dev <- pp(file=glue("images/clinical_concatenated_svaseqbatch_pca-v{ver}.png"), height=6, width=12)
tumaco_concat_nb_pca$plot## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure## Warning in MASS::cov.trob(data[, vars]): Probable convergence failureclosed <- dev.off()
tumaco_concat_nb_pca$plot## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure
## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure
library(ggplot2)
wanted_legend_order <- c("cure_eosinophils","cure_monocytes","cure_neutrophils",
"failure_eosinophils","failure_monocytes","failure_neutrophils")
test <- plot_pca(clinical_concat_norm)## plot labels was not set and there are more than 100 samples, disabling it.test$plot
test$plot +
scale_color_discrete(breaks=wanted_legend_order) +
guides(color=guide_legend(ncol=2))## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
test2 <- plot_legend(clinical_concat_norm)## plot labels was not set and there are more than 100 samples, disabling it.7.5.10 Samples separated by visit
Separate the samples by visit in order to more easily see what patterns emerge across cell type and clinical outcome.
The most likely data set for this is hs_clinical.
7.5.10.1 All visits together
Now let us shift the view slightly to focus on changes observed over time.
visit_expt <- set_expt_conditions(hs_clinical, fact = "visitnumber") %>%
set_expt_batches(fact = "clinicaloutcome") %>%
subset_expt(subset="typeofcells!='biopsy'")## subset_expt(): There were 162, now there are 145 samples.visit_norm <- normalize_expt(visit_expt, transform="log2", convert="cpm",
norm="quant", filter=TRUE)## Removing 7868 low-count genes (12055 remaining).## transform_counts: Found 110 values equal to 0, adding 1 to the matrix.plot_pca(visit_norm)$plot## plot labels was not set and there are more than 100 samples, disabling it.
visit_nb <- normalize_expt(visit_expt, transform = "log2", convert="cpm",
filter = TRUE, batch = "svaseq")## Removing 7868 low-count genes (12055 remaining).## Setting 13664 low elements to zero.## transform_counts: Found 13664 values equal to 0, adding 1 to the matrix.visit_nb_pca <- plot_pca(visit_nb)## plot labels was not set and there are more than 100 samples, disabling it.dev <- pp(file=glue("images/visit_svaseqbatch_pca-v{ver}.png"), height=7, width=9)
visit_nb_pca$plot
closed <- dev.off()
visit_nb_pca$plot
7.6 Top 1000 genes
repeat with only the most expressed genes from one cell type.
rpkm_values <- normalize_expt(type_valid, transform="log2", convert="rpkm",
filter=TRUE, column="cds_length")## Removing 5663 low-count genes (14260 remaining).## transform_counts: Found 178981 values equal to 0, adding 1 to the matrix.rpkm_median <- median_by_factor(rpkm_values)## The factor biopsy has 17 rows.## The factor eosinophils has 34 rows.## The factor monocytes has 56 rows.## The factor neutrophils has 55 rows.rpkm_tumaco_monocyte_idx <- order(rpkm_median[["medians"]][["monocytes"]], decreasing=TRUE)
rpkm_tumaco_monocyte <- rpkm_median[["medians"]][rpkm_tumaco_monocyte_idx, ]
tumaco_monocyte_gene <- head(rownames(rpkm_tumaco_monocyte), n=1000)7.6.0.1 All visits, separated by cell type
Now separate the visits by cell type and whether or not the drug used was the antimonial.
visit_monocyte <- subset_expt(visit_expt, subset = "typeofcells=='monocytes'")## subset_expt(): There were 145, now there are 56 samples.visit_neutrophil <- subset_expt(visit_expt, subset = "typeofcells=='neutrophils'")## subset_expt(): There were 145, now there are 55 samples.visit_eosinophil <- subset_expt(visit_expt, subset = "typeofcells=='eosinophils'")## subset_expt(): There were 145, now there are 34 samples.7.6.0.2 Visit 1 alone
v1_clinical <- subset_expt(visit_expt, subset = "visitnumber=='1'") %>%
set_expt_conditions(fact = "clinicaloutcome") %>%
set_expt_batches(fact = "typeofcells")## subset_expt(): There were 145, now there are 56 samples.v1_norm <- normalize_expt(v1_clinical, transform="log2", convert="cpm", norm="quant", filter=TRUE)## Removing 8242 low-count genes (11681 remaining).## transform_counts: Found 53 values equal to 0, adding 1 to the matrix.plot_pca(v1_norm)$plot## Warning: ggrepel: 55 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
v1_nb <- normalize_expt(v1_clinical, transform = "log2", convert = "cpm",
filter = TRUE, batch = "svaseq")## Removing 8242 low-count genes (11681 remaining).## Setting 3870 low elements to zero.## transform_counts: Found 3870 values equal to 0, adding 1 to the matrix.plot_pca(v1_nb, plot_labels = FALSE)$plot
So, given the data on hand, there is slight ability to discern cure and fail among the visit 1 samples.
7.6.0.3 Visit 2 alone
v2_clinical <- subset_expt(visit_expt, subset="visitnumber=='2'") %>%
set_expt_conditions(fact = "clinicaloutcome") %>%
set_expt_batches(fact = "typeofcells")## subset_expt(): There were 145, now there are 45 samples.v2_nb <- normalize_expt(v2_clinical, transform = "log2", convert = "cpm", norm = "quant",
filter = TRUE, batch = "svaseq")## Warning in normalize_expt(v2_clinical, transform = "log2", convert = "cpm", :
## Quantile normalization and sva do not always play well together.## Removing 8243 low-count genes (11680 remaining).## Setting 2461 low elements to zero.## transform_counts: Found 2461 values equal to 0, adding 1 to the matrix.plot_pca(v2_nb, plot_labels = FALSE)$plot
There might be some variance among the visit 2 samples which are cure/fail-ish.
7.6.0.4 Visit 3 alone
v3_clinical <- subset_expt(visit_expt, subset="visitnumber=='3'") %>%
set_expt_conditions(fact = "clinicaloutcome") %>%
set_expt_batches(fact = "typeofcells")## subset_expt(): There were 145, now there are 44 samples.v3_nb <- normalize_expt(v3_clinical, transform = "log2", convert = "cpm", norm = "quant",
filter = TRUE, batch = "svaseq")## Warning in normalize_expt(v3_clinical, transform = "log2", convert = "cpm", :
## Quantile normalization and sva do not always play well together.## Removing 8329 low-count genes (11594 remaining).## Setting 2018 low elements to zero.## transform_counts: Found 2018 values equal to 0, adding 1 to the matrix.plot_pca(v3_nb, plot_labels = FALSE)$plot
In a similar fashion, it seems that the visit 3 samples are not particularly informative.
7.6.1 Samples separated by cell type
Separate the samples by cell type in order to more easily observe patterns with respect to visit and clinical outcome.
7.6.1.1 Biopsies
biopsy_norm <- normalize_expt(biopsy_samples, norm = "quant", convert = "cpm",
transform = "log2", filter = TRUE)## Removing 6321 low-count genes (13602 remaining).## transform_counts: Found 169 values equal to 0, adding 1 to the matrix.plot_pca(biopsy_norm, plot_labels = FALSE)$plot
biopsy_nb <- normalize_expt(biopsy_samples, convert = "cpm",
transform = "log2", filter = TRUE, batch = "svaseq")## Removing 6321 low-count genes (13602 remaining).## Setting 208 low elements to zero.## transform_counts: Found 208 values equal to 0, adding 1 to the matrix.plot_pca(biopsy_nb, plot_labels = FALSE)$plot
7.6.1.2 Monocytes
monocyte_norm <- normalize_expt(monocyte_samples, norm = "quant", convert = "cpm",
transform = "log2", filter = TRUE)## Removing 8867 low-count genes (11056 remaining).## transform_counts: Found 5 values equal to 0, adding 1 to the matrix.monocyte_pca <- plot_pca(monocyte_norm, plot_labels = FALSE)
dev <- pp(file="images/monocytes_cf_norm_pca.png")
monocyte_pca$plot
closed <- dev.off()
monocyte_pca$plot
monocyte_disheat <- plot_disheat(monocyte_norm)
dev <- pp(file="images/monocytes_cf_norm_disheat.png")
monocyte_disheat$plot
closed <- dev.off()
monocyte_disheat$plot
monocyte_nb <- normalize_expt(monocyte_samples, convert = "cpm",
transform = "log2", filter = TRUE, batch = "svaseq")## Removing 8867 low-count genes (11056 remaining).## Setting 1086 low elements to zero.## transform_counts: Found 1086 values equal to 0, adding 1 to the matrix.monocyte_nb_pca <- plot_pca(monocyte_nb, plot_labels = FALSE)
dev <- pp(file="images/monocytes_cf_norm_sva_pca.png")
monocyte_nb_pca$plot
closed <- dev.off()
monocyte_nb_pca$plot
7.6.1.2.1 Monocytes, visit 1
monocyte_v1 <- subset_expt(monocyte_samples, subset = "visitnumber=='1'")## subset_expt(): There were 56, now there are 22 samples.monocyte_v1_norm <- normalize_expt(monocyte_v1, norm = "quant", convert = "cpm",
transform = "log2", filter = TRUE)## Removing 9179 low-count genes (10744 remaining).## transform_counts: Found 2 values equal to 0, adding 1 to the matrix.monocyte_v1_pca <- plot_pca(monocyte_v1_norm, plot_labels = FALSE)
dev <- pp(file="images/monocytes_v1_cf_norm_pca.png")
monocyte_v1_pca$plot## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure
## Warning in MASS::cov.trob(data[, vars]): Probable convergence failureclosed <- dev.off()
monocyte_v1_pca$plot## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure
## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure
monocyte_v1_nb <- normalize_expt(monocyte_v1, convert = "cpm",
transform = "log2", filter = TRUE, batch = "svaseq")## Removing 9179 low-count genes (10744 remaining).## Setting 344 low elements to zero.## transform_counts: Found 344 values equal to 0, adding 1 to the matrix.monocyte_v1_nb_pca <- plot_pca(monocyte_v1_nb, plot_labels = FALSE)
dev <- pp(file="images/monocytes_v1_cf_norm_sva_pca.png")
monocyte_v1_nb_pca$plot
closed <- dev.off()
monocyte_v1_nb_pca$plot
The monocytes, on the other hand, appear to have some real information lurking in them.
7.6.1.3 Neutrophils
neutrophil_norm <- normalize_expt(neutrophil_samples, norm = "quant", convert = "cpm",
transform = "log2", filter = TRUE)## Removing 10713 low-count genes (9210 remaining).## transform_counts: Found 1 values equal to 0, adding 1 to the matrix.plot_pca(neutrophil_norm, plot_labels = FALSE)$plot
neutrophil_nb <- normalize_expt(neutrophil_samples, convert = "cpm",
transform = "log2", filter = TRUE, batch = "svaseq")## Removing 10713 low-count genes (9210 remaining).## Setting 1238 low elements to zero.## transform_counts: Found 1238 values equal to 0, adding 1 to the matrix.plot_pca(neutrophil_nb, plot_labels = FALSE)$plot
This appears also to be the case for the neutrophil samples.
7.6.1.4 Eosinophils
eosinophil_norm <- normalize_expt(eosinophil_samples, norm = "quant", convert = "cpm",
transform = "log2", filter = TRUE)## Removing 9238 low-count genes (10685 remaining).## transform_counts: Found 4 values equal to 0, adding 1 to the matrix.plot_pca(eosinophil_norm, plot_labels = FALSE)$plot
eosinophil_nb <- normalize_expt(eosinophil_samples, convert = "cpm",
transform = "log2", filter = TRUE, batch = "svaseq")## Removing 9238 low-count genes (10685 remaining).## Setting 614 low elements to zero.## transform_counts: Found 614 values equal to 0, adding 1 to the matrix.plot_pca(eosinophil_nb, plot_labels = FALSE)$plot
We have fewer eosinophil samples currently, but they appear to have some real differences.
7.6.1.5 Monocytes, Neutrophils, and Eosinophils
8 Differential expression analyses
The primary goal is to learn about cure vs. fail.
8.1 Cure/Fail, all samples
Now let us start performing the various differential expression analyses, starting with the set of all/most clinical samples.
cf_clinical_de <- all_pairwise(hs_clinical, model_batch = "svaseq", filter = TRUE)## Removing 0 low-count genes (14260 remaining).## Setting 23511 low elements to zero.## transform_counts: Found 23511 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.cf_clinical_tables <- combine_de_tables(
cf_clinical_de,
keepers = cf_contrasts,
excel = glue::glue("excel/cf_clinical_tables-v{ver}.xlsx"))## Deleting the file excel/cf_clinical_tables-v202203.xlsx before writing the tables.cf_clinical_sig <- extract_significant_genes(
cf_clinical_tables,
excel = glue::glue("excel/cf_clinical_sig-v{ver}.xlsx"))## Deleting the file excel/cf_clinical_sig-v202203.xlsx before writing the tables.8.1.1 By cell type
8.1.1.1 Cure/Fail, Biopsies
cf_biopsy_de <- all_pairwise(biopsy_samples, model_batch = "svaseq", filter = TRUE)## Removing 0 low-count genes (13602 remaining).## Setting 208 low elements to zero.## transform_counts: Found 208 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.cf_biopsy_tables <- combine_de_tables(
cf_biopsy_de,
keepers = cf_contrasts,
excel = glue::glue("excel/cf_biopsy_tables-v{ver}.xlsx"))## Deleting the file excel/cf_biopsy_tables-v202203.xlsx before writing the tables.cf_biopsy_sig <- extract_significant_genes(
cf_biopsy_tables,
excel = glue::glue("excel/cf_biopsy_sig-v{ver}.xlsx"))## Deleting the file excel/cf_biopsy_sig-v202203.xlsx before writing the tables.8.1.1.2 Cure/Fail, Monocytes
cf_monocyte_sva_de <- all_pairwise(monocyte_samples, model_batch = "svaseq", filter = TRUE)## Removing 0 low-count genes (11056 remaining).## Setting 1086 low elements to zero.## transform_counts: Found 1086 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.cf_monocyte_sva_tables <- combine_de_tables(
cf_monocyte_sva_de,
keepers = cf_contrasts,
excel = glue::glue("excel/cf_monocyte_sva_tables-v{ver}.xlsx"))## Deleting the file excel/cf_monocyte_sva_tables-v202203.xlsx before writing the tables.cf_monocyte_sva_sig <- extract_significant_genes(
cf_monocyte_sva_tables,
excel = glue::glue("excel/cf_monocyte_sva_sig-v{ver}.xlsx"))## Deleting the file excel/cf_monocyte_sva_sig-v202203.xlsx before writing the tables.cf_monocyte_batchvisit_de <- all_pairwise(monocyte_samples, model_batch = TRUE, filter = TRUE)## Using limma's removeBatchEffect to visualize with(out) batch inclusion.## Finished running DE analyses, collecting outputs.## Comparing analyses.cf_monocyte_batchvisit_tables <- combine_de_tables(
cf_monocyte_batchvisit_de,
keepers = cf_contrasts,
excel = glue::glue("excel/cf_monocyte_batchvisit_tables-v{ver}.xlsx"))## Deleting the file excel/cf_monocyte_batchvisit_tables-v202203.xlsx before writing the tables.cf_monocyte_batchvisit_sig <- extract_significant_genes(
cf_monocyte_batchvisit_tables,
excel = glue::glue("excel/cf_monocyte_batchvisit_sig-v{ver}.xlsx"))## Deleting the file excel/cf_monocyte_batchvisit_sig-v202203.xlsx before writing the tables.8.1.1.3 Cure/Fail, only the Tumaco samples
cf_tumaco_monocyte_sva_de <- all_pairwise(tumaco_monocyte, model_batch = "svaseq", filter = TRUE)## Removing 0 low-count genes (10866 remaining).## Setting 705 low elements to zero.## transform_counts: Found 705 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.cf_tumaco_monocyte_sva_tables <- combine_de_tables(
cf_tumaco_monocyte_sva_de,
keepers = cf_contrasts,
excel = glue::glue("excel/cf_monocyte_tables_sva_tumaco-v{ver}.xlsx"))## Deleting the file excel/cf_monocyte_tables_sva_tumaco-v202203.xlsx before writing the tables.cf_tumaco_monocyte_sva_sig <- extract_significant_genes(
cf_tumaco_monocyte_sva_tables,
excel = glue::glue("excel/cf_monocyte_sva_tumaco_sig-v{ver}.xlsx"))## Deleting the file excel/cf_monocyte_sva_tumaco_sig-v202203.xlsx before writing the tables.cf_tumaco_monocyte_batchvisit_de <- all_pairwise(tumaco_monocyte, model_batch = TRUE, filter = TRUE)## Using limma's removeBatchEffect to visualize with(out) batch inclusion.## Finished running DE analyses, collecting outputs.## Comparing analyses.cf_tumaco_monocyte_batchvisit_tables <- combine_de_tables(
cf_tumaco_monocyte_batchvisit_de,
keepers = cf_contrasts,
excel = glue::glue("excel/cf_monocyte_tables_batchvisit_tumaco-v{ver}.xlsx"))## Deleting the file excel/cf_monocyte_tables_batchvisit_tumaco-v202203.xlsx before writing the tables.cf_tumaco_monocyte_batchvisit_sig <- extract_significant_genes(
cf_tumaco_monocyte_batchvisit_tables,
excel = glue::glue("excel/cf_monocyte_batchvisit_tumaco_sig-v{ver}.xlsx"))## Deleting the file excel/cf_monocyte_batchvisit_tumaco_sig-v202203.xlsx before writing the tables.tumaco_sva_aucc <- calculate_aucc(cf_tumaco_monocyte_sva_tables[["data"]][["fail_vs_cure"]],
tbl2=cf_monocyte_sva_tables[["data"]][["fail_vs_cure"]],
py="deseq_adjp", ly="deseq_logfc")
tumaco_sva_aucc## $aucc
## [1] 0.388
##
## $plot
shared_ids <- rownames(cf_tumaco_monocyte_sva_tables[["data"]][["fail_vs_cure"]]) %in%
rownames(cf_monocyte_sva_tables[["data"]][["fail_vs_cure"]])
first <- cf_monocyte_sva_tables[["data"]][["fail_vs_cure"]][shared_ids, ]
second <- cf_tumaco_monocyte_sva_tables[["data"]][["fail_vs_cure"]][rownames(first), ]
cor.test(first[["deseq_logfc"]], second[["deseq_logfc"]])##
## Pearson's product-moment correlation
##
## data: first[["deseq_logfc"]] and second[["deseq_logfc"]]
## t = 188, df = 10864, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8703 0.8791
## sample estimates:
## cor
## 0.8748tumaco_batch_aucc <- calculate_aucc(cf_tumaco_monocyte_batchvisit_tables[["data"]][["fail_vs_cure"]],
tbl2=cf_monocyte_batchvisit_tables[["data"]][["fail_vs_cure"]],
py="deseq_adjp", ly="deseq_logfc")
tumaco_batch_aucc## $aucc
## [1] 0.5309
##
## $plot
shared_ids <- rownames(cf_tumaco_monocyte_batchvisit_tables[["data"]][["fail_vs_cure"]]) %in%
rownames(cf_monocyte_batchvisit_tables[["data"]][["fail_vs_cure"]])
first <- cf_monocyte_batchvisit_tables[["data"]][["fail_vs_cure"]][shared_ids, ]
second <- cf_tumaco_monocyte_batchvisit_tables[["data"]][["fail_vs_cure"]][rownames(first), ]
cor.test(first[["deseq_logfc"]], second[["deseq_logfc"]])##
## Pearson's product-moment correlation
##
## data: first[["deseq_logfc"]] and second[["deseq_logfc"]]
## t = 220, df = 10864, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9005 0.9074
## sample estimates:
## cor
## 0.904first_sig_names <- rownames(cf_monocyte_batchvisit_sig[["deseq"]][["ups"]][["fail_vs_cure"]])
second_sig_names <- rownames(cf_tumaco_monocyte_batchvisit_sig[["deseq"]][["ups"]][["fail_vs_cure"]])
batch_venn_lst <- list(all_batch = first_sig_names, tumaco_batch = second_sig_names)
batch_venn <- Vennerable::Venn(batch_venn_lst)
Vennerable::plot(batch_venn)
8.1.1.4 Cure/Fail, Neutrophils
cf_neutrophil_de <- all_pairwise(neutrophil_samples, model_batch = "svaseq", filter = TRUE)## Removing 0 low-count genes (9210 remaining).## Setting 1238 low elements to zero.## transform_counts: Found 1238 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.cf_neutrophil_tables <- combine_de_tables(
cf_neutrophil_de,
keepers = cf_contrasts,
excel = glue::glue("excel/cf_neutrophil_tables-v{ver}.xlsx"))## Deleting the file excel/cf_neutrophil_tables-v202203.xlsx before writing the tables.cf_neutrophil_sig <- extract_significant_genes(
cf_neutrophil_tables,
excel = glue::glue("excel/cf_neutrophil_sig-v{ver}.xlsx"))## Deleting the file excel/cf_neutrophil_sig-v202203.xlsx before writing the tables.8.1.1.5 Cure/Fail, Eosinophils
cf_eosinophil_de <- all_pairwise(eosinophil_samples, model_batch = "svaseq", filter = TRUE)## Removing 0 low-count genes (10685 remaining).## Setting 614 low elements to zero.## transform_counts: Found 614 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.cf_eosinophil_tables <- combine_de_tables(
cf_eosinophil_de,
keepers = cf_contrasts,
excel = glue::glue("excel/cf_eosinophil_tables-v{ver}.xlsx"))## Deleting the file excel/cf_eosinophil_tables-v202203.xlsx before writing the tables.cf_eosinophil_sig <- extract_significant_genes(
cf_eosinophil_tables,
excel = glue::glue("excel/cf_eosinophil_sig-v{ver}.xlsx"))## Deleting the file excel/cf_eosinophil_sig-v202203.xlsx before writing the tables.8.1.1.6 Cure/Fail clinical (Not biopsies)
cf_nobiopsy_de <- all_pairwise(hs_clinical_nobiop, model_batch = "svaseq", filter = TRUE)## Removing 0 low-count genes (12055 remaining).## Setting 13520 low elements to zero.## transform_counts: Found 13520 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.cf_nobiopsy_tables <- combine_de_tables(
cf_nobiopsy_de,
keepers = cf_contrasts,
excel = glue::glue("excel/cf_nobiopsy_tables-v{ver}.xlsx"))## Deleting the file excel/cf_nobiopsy_tables-v202203.xlsx before writing the tables.cf_nobiopsy_sig <- extract_significant_genes(
cf_nobiopsy_tables,
excel = glue::glue("excel/cf_nobiopsy_sig-v{ver}.xlsx"))## Deleting the file excel/cf_nobiopsy_sig-v202203.xlsx before writing the tables.8.1.2 By visit
For these contrasts, we want to see fail_v1 vs. cure_v1, fail_v2 vs. cure_v2 etc. As a result, we will need to juggle the data slightly and add another set of contrasts.
8.1.3 Setting up
visit_cf_expt_factor <- paste0("v", pData(hs_clinical)[["visitnumber"]],
pData(hs_clinical)[["condition"]])
visit_cf_expt <- set_expt_conditions(hs_clinical, fact = visit_cf_expt_factor)
visit_cf_eosinophil <- subset_expt(visit_cf_expt, subset="typeofcells=='eosinophils'")## subset_expt(): There were 162, now there are 34 samples.visit_cf_tumaco_eosinophil <- subset_expt(visit_cf_eosinophil, subset="clinic=='Tumaco'")## subset_expt(): There were 34, now there are 27 samples.visit_cf_monocyte <- subset_expt(visit_cf_expt, subset="typeofcells=='monocytes'")## subset_expt(): There were 162, now there are 56 samples.visit_cf_tumaco_monocyte <- subset_expt(visit_cf_monocyte, subset="clinic=='Tumaco'")## subset_expt(): There were 56, now there are 42 samples.visit_cf_neutrophil <- subset_expt(visit_cf_expt, subset="typeofcells=='neutrophils'")## subset_expt(): There were 162, now there are 55 samples.visit_cf_tumaco_neutrophil <- subset_expt(visit_cf_neutrophil, subset="clinic=='Tumaco'")## subset_expt(): There were 55, now there are 41 samples.8.1.3.1 Cure/Fail by visits, all cell types
visit_cf_all_de <- all_pairwise(visit_cf_expt, model_batch = "svaseq", filter = TRUE)## Removing 0 low-count genes (14260 remaining).## Setting 23481 low elements to zero.## transform_counts: Found 23481 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
visit_cf_all_tables <- combine_de_tables(
visit_cf_all_de,
keepers = visit_cf_contrasts,
excel = glue::glue("excel/visit_cf_all_tables-v{ver}.xlsx"))## Deleting the file excel/visit_cf_all_tables-v202203.xlsx before writing the tables.visit_cf_all_sig <- extract_significant_genes(
visit_cf_all_tables,
excel = glue::glue("excel/visit_cf_all_sig-v{ver}.xlsx"))## Deleting the file excel/visit_cf_all_sig-v202203.xlsx before writing the tables.8.1.3.2 Cure/Fail by visit, Monocytes
visit_cf_monocyte_de <- all_pairwise(visit_cf_monocyte, model_batch = "svaseq", filter = TRUE)## Removing 0 low-count genes (11056 remaining).## Setting 1147 low elements to zero.## transform_counts: Found 1147 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
visit_cf_monocyte_tables <- combine_de_tables(
visit_cf_monocyte_de,
keepers = visit_cf_contrasts,
excel = glue::glue("excel/visit_cf_monocyte_tables-v{ver}.xlsx"))## Deleting the file excel/visit_cf_monocyte_tables-v202203.xlsx before writing the tables.visit_cf_monocyte_sig <- extract_significant_genes(
visit_cf_monocyte_tables,
excel = glue::glue("excel/visit_cf_monocyte_sig-v{ver}.xlsx"))## Deleting the file excel/visit_cf_monocyte_sig-v202203.xlsx before writing the tables.v1fc_deseq_ma <- visit_cf_monocyte_tables[["plots"]][["v1fail_vs_cure"]][["deseq_ma_plots"]][["plot"]]
dev <- pp(file="images/monocyte_cf_de_v1_maplot.png")
v1fc_deseq_ma
closed <- dev.off()
v1fc_deseq_ma
v2fc_deseq_ma <- visit_cf_monocyte_tables[["plots"]][["v2fail_vs_cure"]][["deseq_ma_plots"]][["plot"]]
dev <- pp(file="images/monocyte_cf_de_v2_maplot.png")
v2fc_deseq_ma
closed <- dev.off()
v2fc_deseq_ma
v3fc_deseq_ma <- visit_cf_monocyte_tables[["plots"]][["v3fail_vs_cure"]][["deseq_ma_plots"]][["plot"]]
dev <- pp(file="images/monocyte_cf_de_v3_maplot.png")
v3fc_deseq_ma
closed <- dev.off()
v3fc_deseq_ma
## Repeat for the tumaco subset
visit_cf_tumaco_monocyte_de <- all_pairwise(visit_cf_tumaco_monocyte,
model_batch = "svaseq", filter = TRUE)## Removing 0 low-count genes (10866 remaining).## Setting 682 low elements to zero.## transform_counts: Found 682 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
visit_cf_tumaco_monocyte_tables <- combine_de_tables(
visit_cf_tumaco_monocyte_de,
keepers = visit_cf_contrasts,
excel = glue::glue("excel/visit_cf_tumaco_monocyte_tables-v{ver}.xlsx"))## Deleting the file excel/visit_cf_tumaco_monocyte_tables-v202203.xlsx before writing the tables.visit_cf_tumaco_monocyte_sig <- extract_significant_genes(
visit_cf_tumaco_monocyte_tables,
excel = glue::glue("excel/visit_cf_tumaco_monocyte_sig-v{ver}.xlsx"))## Deleting the file excel/visit_cf_tumaco_monocyte_sig-v202203.xlsx before writing the tables.One query from Alejandro is to look at the genes shared up/down across visits. I am not entirely certain we have enough samples for this to work, but let us find out.
I am thinking this is a good place to use the AUCC curves I learned about thanks to Julie Cridland.
Note that the following is all monocyte samples, this should therefore potentially be moved up and a version of this with only the Tumaco samples put here?
v1fc <- visit_cf_monocyte_tables[["data"]][["v1fail_vs_cure"]]
v2fc <- visit_cf_monocyte_tables[["data"]][["v2fail_vs_cure"]]
v3fc <- visit_cf_monocyte_tables[["data"]][["v3fail_vs_cure"]]
v1_sig <- c(
rownames(visit_cf_monocyte_sig[["deseq"]][["ups"]][["v1fail_vs_cure"]]),
rownames(visit_cf_monocyte_sig[["deseq"]][["downs"]][["v1fail_vs_cure"]]))
v2_sig <- c(
rownames(visit_cf_monocyte_sig[["deseq"]][["ups"]][["v2fail_vs_cure"]]),
rownames(visit_cf_monocyte_sig[["deseq"]][["downs"]][["v2fail_vs_cure"]]))
v3_sig <- c(
rownames(visit_cf_monocyte_sig[["deseq"]][["ups"]][["v2fail_vs_cure"]]),
rownames(visit_cf_monocyte_sig[["deseq"]][["downs"]][["v2fail_vs_cure"]]))
monocyte_visit_aucc_v2v1 <- calculate_aucc(v1fc, tbl2=v2fc, py="deseq_adjp", ly="deseq_logfc")
dev <- pp(file="images/monocyte_visit_v2v1_aucc.png")
monocyte_visit_aucc_v2v1[["plot"]]
closed <- dev.off()
monocyte_visit_aucc_v2v1[["plot"]]
monocyte_visit_aucc_v3v1 <- calculate_aucc(v1fc, tbl2=v3fc, py="deseq_adjp", ly="deseq_logfc")
dev <- pp(file="images/monocyte_visit_v3v1_aucc.png")
monocyte_visit_aucc_v3v1[["plot"]]
closed <- dev.off()
monocyte_visit_aucc_v3v1[["plot"]]
8.1.3.3 Repeat for only the Tumaco samples
v1fc_tumaco <- visit_cf_tumaco_monocyte_tables[["data"]][["v1fail_vs_cure"]]
v2fc_tumaco <- visit_cf_tumaco_monocyte_tables[["data"]][["v2fail_vs_cure"]]
v3fc_tumaco <- visit_cf_tumaco_monocyte_tables[["data"]][["v3fail_vs_cure"]]
v1_tumaco_sig <- c(
rownames(visit_cf_tumaco_monocyte_sig[["deseq"]][["ups"]][["v1fail_vs_cure"]]),
rownames(visit_cf_tumaco_monocyte_sig[["deseq"]][["downs"]][["v1fail_vs_cure"]]))
v2_tumaco_sig <- c(
rownames(visit_cf_tumaco_monocyte_sig[["deseq"]][["ups"]][["v2fail_vs_cure"]]),
rownames(visit_cf_tumaco_monocyte_sig[["deseq"]][["downs"]][["v2fail_vs_cure"]]))
v3_tumaco_sig <- c(
rownames(visit_cf_tumaco_monocyte_sig[["deseq"]][["ups"]][["v2fail_vs_cure"]]),
rownames(visit_cf_tumaco_monocyte_sig[["deseq"]][["downs"]][["v2fail_vs_cure"]]))
monocyte_tumaco_visit_aucc_v2v1 <- calculate_aucc(v1fc_tumaco, tbl2=v2fc_tumaco,
py="deseq_adjp", ly="deseq_logfc")
dev <- pp(file="images/monocyte_visit_tumaco_v2v1_aucc.png")
monocyte_tumaco_visit_aucc_v2v1[["plot"]]
closed <- dev.off()
monocyte_tumaco_visit_aucc_v2v1[["plot"]]
monocyte_tumaco_visit_aucc_v3v1 <- calculate_aucc(v1fc_tumaco, tbl2=v3fc_tumaco,
py="deseq_adjp", ly="deseq_logfc")
dev <- pp(file="images/monocyte_visit_tumaco_v3v1_aucc.png")
monocyte_tumaco_visit_aucc_v3v1[["plot"]]
closed <- dev.off()
monocyte_tumaco_visit_aucc_v3v1[["plot"]]
monocyte_tumaco_visit_aucc_v3v2 <- calculate_aucc(v3fc_tumaco, tbl2=v2fc_tumaco,
py="deseq_adjp", ly="deseq_logfc")
dev <- pp(file="images/monocyte_visit_tumaco_v3v2_aucc.png")
monocyte_tumaco_visit_aucc_v3v2[["plot"]]
closed <- dev.off()
monocyte_tumaco_visit_aucc_v3v2[["plot"]]
8.1.3.4 Cure/Fail by visit, Neutrophils
a
visit_cf_neutrophil_de <- all_pairwise(visit_cf_neutrophil, model_batch = "svaseq", filter = TRUE)## Removing 0 low-count genes (9210 remaining).## Setting 1086 low elements to zero.## transform_counts: Found 1086 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
visit_cf_neutrophil_tables <- combine_de_tables(
visit_cf_neutrophil_de,
keepers = visit_cf_contrasts,
excel = glue::glue("excel/visit_cf_neutrophil_tables-v{ver}.xlsx"))## Deleting the file excel/visit_cf_neutrophil_tables-v202203.xlsx before writing the tables.visit_cf_neutrophil_sig <- extract_significant_genes(
visit_cf_neutrophil_tables,
excel = glue::glue("excel/visit_cf_neutrophil_sig-v{ver}.xlsx"))## Deleting the file excel/visit_cf_neutrophil_sig-v202203.xlsx before writing the tables.8.1.3.5 Cure/Fail by visit, Eosinophils
visit_cf_eosinophil_de <- all_pairwise(visit_cf_eosinophil, model_batch = "svaseq", filter = TRUE)## Removing 0 low-count genes (10685 remaining).## Setting 596 low elements to zero.## transform_counts: Found 596 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
visit_cf_eosinophil_tables <- combine_de_tables(
visit_cf_eosinophil_de,
keepers = visit_cf_contrasts,
excel = glue::glue("excel/visit_cf_eosinophil_tables-v{ver}.xlsx"))## Deleting the file excel/visit_cf_eosinophil_tables-v202203.xlsx before writing the tables.visit_cf_eosinophil_sig <- extract_significant_genes(
visit_cf_eosinophil_tables,
excel = glue::glue("excel/visit_cf_eosinophil_sig-v{ver}.xlsx"))## Deleting the file excel/visit_cf_eosinophil_sig-v202203.xlsx before writing the tables.8.2 Persistence in visit 3
Having put some SL read mapping information in the sample sheet, Maria Adelaida added a new column using it with the putative persistence state on a per-sample basis. One question which arised from that: what differences are observable between the persistent yes vs. no samples on a per-cell-type basis among the visit 3 samples.
8.2.1 Setting up
First things first, create the datasets.
persistence_expt <- subset_expt(hs_clinical, subset = "persistence=='Y'|persistence=='N'") %>%
subset_expt(subset = 'visitnumber==3') %>%
set_expt_conditions(fact = 'persistence')## subset_expt(): There were 162, now there are 94 samples.## subset_expt(): There were 94, now there are 25 samples.## persistence_biopsy <- subset_expt(persistence_expt, subset = "typeofcells=='biopsy'")
persistence_monocyte <- subset_expt(persistence_expt, subset = "typeofcells=='monocytes'")## subset_expt(): There were 25, now there are 10 samples.persistence_neutrophil <- subset_expt(persistence_expt, subset = "typeofcells=='neutrophils'")## subset_expt(): There were 25, now there are 9 samples.persistence_eosinophil <- subset_expt(persistence_expt, subset = "typeofcells=='eosinophils'")## subset_expt(): There were 25, now there are 6 samples.8.2.2 Take a look
See if there are any patterns which look usable.
## All
persistence_norm <- normalize_expt(persistence_expt, transform = "log2", convert = "cpm",
norm = "quant", filter = TRUE)## Removing 8563 low-count genes (11360 remaining).## transform_counts: Found 35 values equal to 0, adding 1 to the matrix.plot_pca(persistence_norm)$plot
persistence_nb <- normalize_expt(persistence_expt, transform = "log2", convert = "cpm",
batch = "svaseq", filter = TRUE)## Removing 8563 low-count genes (11360 remaining).## Setting 1234 low elements to zero.## transform_counts: Found 1234 values equal to 0, adding 1 to the matrix.plot_pca(persistence_nb)$plot
## Biopsies
##persistence_biopsy_norm <- normalize_expt(persistence_biopsy, transform = "log2", convert = "cpm",
## norm = "quant", filter = TRUE)
##plot_pca(persistence_biopsy_norm)$plot
## Insufficient data
## Monocytes
persistence_monocyte_norm <- normalize_expt(persistence_monocyte, transform = "log2", convert = "cpm",
norm = "quant", filter = TRUE)## Removing 9552 low-count genes (10371 remaining).## transform_counts: Found 3 values equal to 0, adding 1 to the matrix.plot_pca(persistence_monocyte_norm)$plot
persistence_monocyte_nb <- normalize_expt(persistence_monocyte, transform = "log2", convert = "cpm",
batch = "svaseq", filter = TRUE)## Removing 9552 low-count genes (10371 remaining).## Setting 44 low elements to zero.## transform_counts: Found 44 values equal to 0, adding 1 to the matrix.plot_pca(persistence_monocyte_nb)$plot
## Neutrophils
persistence_neutrophil_norm <- normalize_expt(persistence_neutrophil, transform = "log2", convert = "cpm",
norm = "quant", filter = TRUE)## Removing 11563 low-count genes (8360 remaining).## transform_counts: Found 2 values equal to 0, adding 1 to the matrix.plot_pca(persistence_neutrophil_norm)$plot
persistence_neutrophil_nb <- normalize_expt(persistence_neutrophil, transform = "log2", convert = "cpm",
batch = "svaseq", filter = TRUE)## Removing 11563 low-count genes (8360 remaining).## Setting 53 low elements to zero.## transform_counts: Found 53 values equal to 0, adding 1 to the matrix.plot_pca(persistence_neutrophil_nb)$plot
## Eosinophils
persistence_eosinophil_norm <- normalize_expt(persistence_eosinophil, transform = "log2", convert = "cpm",
norm = "quant", filter = TRUE)## Removing 9983 low-count genes (9940 remaining).## transform_counts: Found 6 values equal to 0, adding 1 to the matrix.plot_pca(persistence_eosinophil_norm)$plot
persistence_eosinophil_nb <- normalize_expt(persistence_eosinophil, transform = "log2", convert = "cpm",
batch = "svaseq", filter = TRUE)## Removing 9983 low-count genes (9940 remaining).## Setting 19 low elements to zero.## transform_counts: Found 19 values equal to 0, adding 1 to the matrix.plot_pca(persistence_eosinophil_nb)$plot
8.2.3 persistence DE
persistence_de <- all_pairwise(persistence_expt, filter = TRUE, model_batch = "svaseq")## Removing 0 low-count genes (11360 remaining).## Setting 1234 low elements to zero.## transform_counts: Found 1234 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.persistence_table <- combine_de_tables(
persistence_de,
excel = glue::glue("excel/persistence_all_de-v{ver}.xlsx"))## Deleting the file excel/persistence_all_de-v202203.xlsx before writing the tables.persistence_monocyte_de <- all_pairwise(persistence_monocyte, filter = TRUE, model_batch = "svaseq")## Removing 0 low-count genes (10371 remaining).## Setting 44 low elements to zero.## transform_counts: Found 44 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.persistence_monocyte_table <- combine_de_tables(
persistence_monocyte_de,
excel = glue::glue("excel/persistence_monocyte_de-v{ver}.xlsx"))## Deleting the file excel/persistence_monocyte_de-v202203.xlsx before writing the tables.persistence_neutrophil_de <- all_pairwise(persistence_neutrophil, filter = TRUE, model_batch = "svaseq")## Removing 0 low-count genes (8360 remaining).## Setting 53 low elements to zero.## transform_counts: Found 53 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.persistence_neutrophil_table <- combine_de_tables(
persistence_neutrophil_de,
excel = glue::glue("excel/persistence_neutrophil_de-v{ver}.xlsx"))## Deleting the file excel/persistence_neutrophil_de-v202203.xlsx before writing the tables.persistence_eosinophil_de <- all_pairwise(persistence_eosinophil, filter = TRUE, model_batch = "svaseq")## Removing 0 low-count genes (9940 remaining).## Setting 19 low elements to zero.## transform_counts: Found 19 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.persistence_eosinophil_table <- combine_de_tables(
persistence_eosinophil_de,
excel = glue::glue("excel/persistence_eosinophil_de-v{ver}.xlsx"))## Deleting the file excel/persistence_eosinophil_de-v202203.xlsx before writing the tables.8.3 Comparing visits without regard to cure/fail
8.3.1 All cell types
visit_all_de <- all_pairwise(visit_expt, filter = TRUE, model_batch = "svaseq")## Removing 0 low-count genes (12055 remaining).## Setting 13664 low elements to zero.## transform_counts: Found 13664 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
visit_all_table <- combine_de_tables(
visit_all_de,
keepers = visit_contrasts,
excel = glue::glue("excel/visit_all_de-v{ver}.xlsx"))## Deleting the file excel/visit_all_de-v202203.xlsx before writing the tables.visit_all_sig <- extract_significant_genes(
visit_all_table,
excel = glue::glue("excel/visit_all_sig-v{ver}.xlsx"))## Deleting the file excel/visit_all_sig-v202203.xlsx before writing the tables.visit_all_saved <- save(list = "visit_all_table",
file = glue::glue("rda/visit_all_table-v{ver}.rda"))8.3.2 Monocyte samples
visit_monocyte_de <- all_pairwise(visit_monocyte, filter = TRUE, model_batch = "svaseq")## Removing 0 low-count genes (11056 remaining).## Setting 1077 low elements to zero.## transform_counts: Found 1077 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
visit_monocyte_table <- combine_de_tables(
visit_monocyte_de,
keepers = visit_contrasts,
excel = glue::glue("excel/visit_monocyte_de-v{ver}.xlsx"))## Deleting the file excel/visit_monocyte_de-v202203.xlsx before writing the tables.visit_monocyte_sig <- extract_significant_genes(
visit_monocyte_table,
excel = glue::glue("excel/visit_monocyte_sig-v{ver}.xlsx"))## Deleting the file excel/visit_monocyte_sig-v202203.xlsx before writing the tables.8.3.3 Neutrophil samples
visit_neutrophil_de <- all_pairwise(visit_neutrophil, filter = TRUE, model_batch = "svaseq")## Removing 0 low-count genes (9210 remaining).## Setting 1087 low elements to zero.## transform_counts: Found 1087 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
visit_neutrophil_table <- combine_de_tables(
visit_neutrophil_de,
keepers = visit_contrasts,
excel = glue::glue("excel/visit_neutrophil_de-v{ver}.xlsx"))## Deleting the file excel/visit_neutrophil_de-v202203.xlsx before writing the tables.visit_neutrophil_sig <- extract_significant_genes(
visit_neutrophil_table,
excel = glue::glue("excel/visit_neutrophil_sig-v{ver}.xlsx"))## Deleting the file excel/visit_neutrophil_sig-v202203.xlsx before writing the tables.visit_neutrophil_saved <- save(list = "visit_neutrophil_table",
file = glue::glue("rda/visit_neutrophil_table-v{ver}.rda"))8.3.4 Eosinophil samples
visit_eosinophil_de <- all_pairwise(visit_eosinophil, filter = TRUE, model_batch = "svaseq")## Removing 0 low-count genes (10685 remaining).## Setting 470 low elements to zero.## transform_counts: Found 470 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
visit_eosinophil_table <- combine_de_tables(
visit_eosinophil_de,
keepers = visit_contrasts,
excel = glue::glue("excel/visit_eosinophil_de-v{ver}.xlsx"))## Deleting the file excel/visit_eosinophil_de-v202203.xlsx before writing the tables.visit_eosinophil_sig <- extract_significant_genes(
visit_eosinophil_table,
excel = glue::glue("excel/visit_eosinophil_sig-v{ver}.xlsx"))## Deleting the file excel/visit_eosinophil_sig-v202203.xlsx before writing the tables.visit_eosinophil_saved <- save(list = "visit_eosinophil_table",
file = glue::glue("rda/visit_eosinophil_table-v{ver}.rda"))9 Compare the two clinics directly
monocytes_by_place <- set_expt_conditions(monocyte_samples, fact="clinic")
eosinophils_by_place <- set_expt_conditions(eosinophil_samples, fact="clinic")
neutrophils_by_place <- set_expt_conditions(neutrophil_samples, fact="clinic")
biopsies_by_place <- set_expt_conditions(biopsy_samples, fact="clinic")
monocyte_place_de <- all_pairwise(monocytes_by_place, model_batch=TRUE)## Using limma's removeBatchEffect to visualize with(out) batch inclusion.## Finished running DE analyses, collecting outputs.## Comparing analyses.monocyte_table <- combine_de_tables(
monocyte_place_de,
excel="excel/monocytes_by_place-table.xlsx")## Deleting the file excel/monocytes_by_place-table.xlsx before writing the tables.monocyte_sig <- extract_significant_genes(
monocyte_table,
excel="excel/monocytes_by_place-sig.xlsx")## Deleting the file excel/monocytes_by_place-sig.xlsx before writing the tables.eosinophil_place_de <- all_pairwise(eosinophils_by_place, model_batch=TRUE)## Using limma's removeBatchEffect to visualize with(out) batch inclusion.## Finished running DE analyses, collecting outputs.## Comparing analyses.eosinophil_table <- combine_de_tables(
eosinophil_place_de,
excel="excel/eosinophils_by_place-table.xlsx")## Deleting the file excel/eosinophils_by_place-table.xlsx before writing the tables.eosinophil_sig <- extract_significant_genes(
eosinophil_table,
excel="excel/eosinophils_by_place-sig.xlsx")## Deleting the file excel/eosinophils_by_place-sig.xlsx before writing the tables.neutrophil_place_de <- all_pairwise(neutrophils_by_place, model_batch=TRUE)## Using limma's removeBatchEffect to visualize with(out) batch inclusion.## Finished running DE analyses, collecting outputs.## Comparing analyses.neutrophil_table <- combine_de_tables(
neutrophil_place_de,
excel="excel/neutrophils_by_place-table.xlsx")## Deleting the file excel/neutrophils_by_place-table.xlsx before writing the tables.neutrophil_sig <- extract_significant_genes(
neutrophil_table,
excel="excel/neutrophils_by_place-sig.xlsx")## Deleting the file excel/neutrophils_by_place-sig.xlsx before writing the tables.biopsy_place_de <- all_pairwise(biopsies_by_place, model_batch=TRUE)## Using limma's removeBatchEffect to visualize with(out) batch inclusion.## Finished running DE analyses, collecting outputs.## Comparing analyses.biopsy_table <- combine_de_tables(
biopsy_place_de,
excel="biopsies_by_place-table.xlsx")## Deleting the file biopsies_by_place-table.xlsx before writing the tables.biopsy_sig <- extract_significant_genes(
biopsy_table,
excel="biopsies_by_place-sig.xlsx")## Deleting the file biopsies_by_place-sig.xlsx before writing the tables.10 Likelihood Ratio Testing (LRT)
10.1 Shared patterns across visits
clinical_filt <- normalize_expt(hs_clinical, filter = TRUE)## Removing 5663 low-count genes (14260 remaining).lrt_visit <- deseq_lrt(clinical_filt, transform = "vst", interaction = FALSE,
interactor_column = "visitnumber",
interest_column = "clinicaloutcome")## converting counts to integer mode## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 178 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testing## A large number of genes was given-- please, make sure this is not an error. Normally, only DE genes will be useful for this function.## Working with 5663 genes.## Working with 5663 genes after filtering: minc > 3## Joining, by = "merge"
## Joining, by = "merge"## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
summary(lrt_visit[["favorite_genes"]])## Length Class Mode
## 0 NULL NULLwritten <- write_xlsx(data=as.data.frame(lrt_visit[["deseq_table"]]),
excel=glue::glue("excel/lrt_clinical_visit-v{ver}.xlsx"))
lrt_monocyte_visit <- deseq_lrt(visit_monocyte, transform = "vst",
interaction = FALSE,
interactor_column = "visitnumber",
interest_column = "clinicaloutcome")## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## -- replacing outliers and refitting for 86 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)
## estimating dispersions
## fitting model and testing
## Working with 20 genes.
## Working with 20 genes after filtering: minc > 3
## Joining, by = "merge"Joining, by = "merge"## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
lrt_monocyte_visit$cluster_data$plot## NULLlrt_tumaco_monocyte_visit <- deseq_lrt(tumaco_monocyte, transform = "vst",
interaction = FALSE, minc = 1,
interactor_column = "visitnumber",
interest_column = "clinicaloutcome")## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## -- replacing outliers and refitting for 52 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)
## estimating dispersions
## fitting model and testing
## Working with 3 genes.
## Working with 3 genes after filtering: minc > 1
## Joining, by = "merge"Joining, by = "merge"## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
10.2 Shared patterns across cell types
lrt_celltype_clinical_test <- deseq_lrt(hs_clinical, transform = "vst",
interactor_column = "typeofcells",
interest_column = "clinicaloutcome")## converting counts to integer mode## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 20 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testing## Working with 215 genes.## Working with 209 genes after filtering: minc > 3## Joining, by = "merge"
## Joining, by = "merge"## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
deseq_lrt_df <- merge(hs_annot, as.data.frame(lrt_celltype_clinical_test[["deseq_table"]]), all.y=TRUE,
by="row.names")
rownames(deseq_lrt_df) <- deseq_lrt_df[["Row.names"]]
deseq_lrt_df[["Row.names"]] <- NULL
written <- write_xlsx(data=deseq_lrt_df,
excel=glue::glue("excel/lrt_clinical_celltype-v{ver}.xlsx"))11 Gene Set Analyses
The gene sets we are most likely to consider are the results of the various preceding differential expression analyses.
11.1 GSEA
11.1.1 Cure/Fail groups
In the context of cure vs. fail, we have mixed and matched the data in quite a few different ways.
11.1.1.1 Cure/Fail, all samples
For the moment, let us assume that the default definition of ‘significant’ is sufficient for these analyses, with the knowledge that is not likely to remain true.
The resulting data structures are organized as:
thing[[“deseq”]][[ups"]][[contrast]]
In addition, we have a few ontology-esque tools available, so let us mix and match and see what pops out.
cf_all_up <- cf_clinical_sig[["deseq"]][["ups"]][["fail_vs_cure"]]
dim(cf_all_up)## [1] 75 58cf_all_down <- cf_clinical_sig[["deseq"]][["downs"]][["fail_vs_cure"]]
dim(cf_all_down)## [1] 24 58cf_all_up_gp <- simple_gprofiler(cf_all_up)## Performing gProfiler GO search of 75 genes against hsapiens.## GO search found 49 hits.## Performing gProfiler KEGG search of 75 genes against hsapiens.## KEGG search found 16 hits.## Performing gProfiler REAC search of 75 genes against hsapiens.## REAC search found 6 hits.## Performing gProfiler MI search of 75 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 75 genes against hsapiens.## TF search found 9 hits.## Performing gProfiler CORUM search of 75 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 75 genes against hsapiens.## HP search found 0 hits.cf_all_up_gp[["pvalue_plots"]][["reactome_plot_over"]]
cf_all_up_gp[["pvalue_plots"]][["kegg_plot_over"]]
cf_all_up_gp[["pvalue_plots"]][["mfp_plot_over"]]
cf_all_up_gp[["pvalue_plots"]][["bpp_plot_over"]]
cf_all_down_gp <- simple_gprofiler(cf_all_down)## Performing gProfiler GO search of 24 genes against hsapiens.## GO search found 9 hits.## Performing gProfiler KEGG search of 24 genes against hsapiens.## KEGG search found 0 hits.## Performing gProfiler REAC search of 24 genes against hsapiens.## REAC search found 0 hits.## Performing gProfiler MI search of 24 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 24 genes against hsapiens.## TF search found 0 hits.## Performing gProfiler CORUM search of 24 genes against hsapiens.## CORUM search found 4 hits.## Performing gProfiler HP search of 24 genes against hsapiens.## HP search found 0 hits.cf_all_down_gp[["pvalue_plots"]][["reactome_plot_over"]]## NULLcf_all_down_gp[["pvalue_plots"]][["kegg_plot_over"]]## NULLcf_all_down_gp[["pvalue_plots"]][["mfp_plot_over"]]## NULLcf_all_down_gp[["pvalue_plots"]][["bpp_plot_over"]]## NULL11.1.1.2 Cure/Fail, Biopsies
There are no genes deemed significant in the biopsy comparisons of cure/fail. Oddly, when I looked manually, I thought I saw one candidate gene. In addition, when I stepped through the function manually I got the same gene…
11.1.1.3 Cure/Fail, Monocytes
Try again with monocytes.
cf_monocyte_up <- cf_monocyte_sva_sig[["deseq"]][["ups"]][["fail_vs_cure"]]
dim(cf_monocyte_up)## [1] 30 58cf_monocyte_down <- cf_monocyte_sva_sig[["deseq"]][["downs"]][["fail_vs_cure"]]
dim(cf_monocyte_down)## [1] 19 58cf_monocyte_up_gp <- simple_gprofiler(cf_monocyte_up)## Performing gProfiler GO search of 30 genes against hsapiens.## GO search found 33 hits.## Performing gProfiler KEGG search of 30 genes against hsapiens.## KEGG search found 8 hits.## Performing gProfiler REAC search of 30 genes against hsapiens.## REAC search found 12 hits.## Performing gProfiler MI search of 30 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 30 genes against hsapiens.## TF search found 2 hits.## Performing gProfiler CORUM search of 30 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 30 genes against hsapiens.## HP search found 35 hits.cf_monocyte_up_gp[["pvalue_plots"]][["reactome_plot_over"]]
cf_monocyte_up_gp[["pvalue_plots"]][["kegg_plot_over"]]
cf_monocyte_up_gp[["pvalue_plots"]][["bpp_plot_over"]]
cf_monocyte_down_gp <- simple_gprofiler(cf_monocyte_down)## Performing gProfiler GO search of 19 genes against hsapiens.## GO search found 0 hits.## Performing gProfiler KEGG search of 19 genes against hsapiens.## KEGG search found 1 hits.## Performing gProfiler REAC search of 19 genes against hsapiens.## REAC search found 0 hits.## Performing gProfiler MI search of 19 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 19 genes against hsapiens.## TF search found 0 hits.## Performing gProfiler CORUM search of 19 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 19 genes against hsapiens.## HP search found 0 hits.cf_monocyte_down_gp[["pvalue_plots"]][["reactome_plot_over"]]## NULLcf_monocyte_down_gp[["pvalue_plots"]][["kegg_plot_over"]]
11.1.1.4 Cure/Fail, Neutrophils
cf_neutrophil_up <- cf_neutrophil_sig[["deseq"]][["ups"]][["fail_vs_cure"]]
dim(cf_neutrophil_up)## [1] 24 58cf_neutrophil_down <- cf_neutrophil_sig[["deseq"]][["downs"]][["fail_vs_cure"]]
dim(cf_neutrophil_down)## [1] 17 58cf_neutrophil_up_gp <- simple_gprofiler(cf_neutrophil_up)## Performing gProfiler GO search of 24 genes against hsapiens.## GO search found 0 hits.## Performing gProfiler KEGG search of 24 genes against hsapiens.## KEGG search found 0 hits.## Performing gProfiler REAC search of 24 genes against hsapiens.## REAC search found 0 hits.## Performing gProfiler MI search of 24 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 24 genes against hsapiens.## TF search found 12 hits.## Performing gProfiler CORUM search of 24 genes against hsapiens.## CORUM search found 1 hits.## Performing gProfiler HP search of 24 genes against hsapiens.## HP search found 0 hits.cf_neutrophil_down_gp <- simple_gprofiler(cf_neutrophil_down)## Performing gProfiler GO search of 17 genes against hsapiens.## GO search found 0 hits.## Performing gProfiler KEGG search of 17 genes against hsapiens.## KEGG search found 1 hits.## Performing gProfiler REAC search of 17 genes against hsapiens.## REAC search found 0 hits.## Performing gProfiler MI search of 17 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 17 genes against hsapiens.## TF search found 0 hits.## Performing gProfiler CORUM search of 17 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 17 genes against hsapiens.## HP search found 0 hits.cf_neutrophil_down_gp[["pvalue_plots"]][["kegg_plot_over"]]
11.1.1.5 Cure/Fail, Eosinophils
cf_eosinophil_up <- cf_eosinophil_sig[["deseq"]][["ups"]][["fail_vs_cure"]]
dim(cf_eosinophil_up)## [1] 111 58cf_eosinophil_down <- cf_eosinophil_sig[["deseq"]][["downs"]][["fail_vs_cure"]]
dim(cf_eosinophil_down)## [1] 68 58cf_eosinophil_up_gp <- simple_gprofiler(cf_eosinophil_up)## Performing gProfiler GO search of 111 genes against hsapiens.## GO search found 123 hits.## Performing gProfiler KEGG search of 111 genes against hsapiens.## KEGG search found 8 hits.## Performing gProfiler REAC search of 111 genes against hsapiens.## REAC search found 9 hits.## Performing gProfiler MI search of 111 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 111 genes against hsapiens.## TF search found 27 hits.## Performing gProfiler CORUM search of 111 genes against hsapiens.## CORUM search found 5 hits.## Performing gProfiler HP search of 111 genes against hsapiens.## HP search found 0 hits.cf_eosinophil_up_gp[["pvalue_plots"]][["kegg_plot_over"]]
cf_eosinophil_up_gp[["pvalue_plots"]][["reactome_plot_over"]]
cf_eosinophil_up_gp[["pvalue_plots"]][["mfp_plot_over"]]
cf_eosinophil_up_gp[["pvalue_plots"]][["bpp_plot_over"]]
cf_eosinophil_down_gp <- simple_gprofiler(cf_eosinophil_down)## Performing gProfiler GO search of 68 genes against hsapiens.## GO search found 27 hits.## Performing gProfiler KEGG search of 68 genes against hsapiens.## KEGG search found 3 hits.## Performing gProfiler REAC search of 68 genes against hsapiens.## REAC search found 2 hits.## Performing gProfiler MI search of 68 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 68 genes against hsapiens.## TF search found 3 hits.## Performing gProfiler CORUM search of 68 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 68 genes against hsapiens.## HP search found 0 hits.cf_eosinophil_down_gp[["pvalue_plots"]][["reactome_plot_over"]]
cf_eosinophil_down_gp[["pvalue_plots"]][["mfp_plot_over"]]
cf_eosinophil_down_gp[["pvalue_plots"]][["bpp_plot_over"]]
11.1.1.6 Clinical samples
cf_nobiopsy_up <- cf_nobiopsy_sig[["deseq"]][["ups"]][["fail_vs_cure"]]
dim(cf_nobiopsy_up)## [1] 85 58cf_nobiopsy_down <- cf_nobiopsy_sig[["deseq"]][["downs"]][["fail_vs_cure"]]
dim(cf_nobiopsy_down)## [1] 53 58cf_nobiopsy_up_gp <- simple_gprofiler(cf_nobiopsy_up)## Performing gProfiler GO search of 85 genes against hsapiens.## GO search found 43 hits.## Performing gProfiler KEGG search of 85 genes against hsapiens.## KEGG search found 5 hits.## Performing gProfiler REAC search of 85 genes against hsapiens.## REAC search found 6 hits.## Performing gProfiler MI search of 85 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 85 genes against hsapiens.## TF search found 3 hits.## Performing gProfiler CORUM search of 85 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 85 genes against hsapiens.## HP search found 0 hits.cf_nobiopsy_up_gp[["pvalue_plots"]][["kegg_plot_over"]]
cf_nobiopsy_up_gp[["pvalue_plots"]][["reactome_plot_over"]]
cf_nobiopsy_up_gp[["pvalue_plots"]][["mfp_plot_over"]]## NULLcf_nobiopsy_up_gp[["pvalue_plots"]][["bpp_plot_over"]]
cf_nobiopsy_down_gp <- simple_gprofiler(cf_nobiopsy_down)## Performing gProfiler GO search of 53 genes against hsapiens.## GO search found 1 hits.## Performing gProfiler KEGG search of 53 genes against hsapiens.## KEGG search found 0 hits.## Performing gProfiler REAC search of 53 genes against hsapiens.## REAC search found 0 hits.## Performing gProfiler MI search of 53 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 53 genes against hsapiens.## TF search found 2 hits.## Performing gProfiler CORUM search of 53 genes against hsapiens.## CORUM search found 6 hits.## Performing gProfiler HP search of 53 genes against hsapiens.## HP search found 2 hits.cf_nobiopsy_down_gp[["pvalue_plots"]][["reactome_plot_over"]]## NULLcf_nobiopsy_down_gp[["pvalue_plots"]][["mfp_plot_over"]]## NULLcf_nobiopsy_down_gp[["pvalue_plots"]][["bpp_plot_over"]]## NULL11.1.2 Cell type groups
11.1.3 Visit groups
11.2 GSVA
11.2.1 Clinical samples
hs_celltype_gsva_c2 <- simple_gsva(hs_valid)## Converting the rownames() of the expressionset to ENTREZID.## 588 ENSEMBL ID's didn't have a matching ENTEREZ ID. Dropping them now.## Before conversion, the expressionset has 19923 entries.## After conversion, the expressionset has 19495 entries.hs_celltype_gsva_c2_sig <- get_sig_gsva_categories(
hs_celltype_gsva_c2,
excel="excel/individual_celltypes_gsva_c2.xlsx")## Starting limma pairwise comparison.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$libsize.## Limma step 1/6: choosing model.## Choosing the non-intercept containing model.## Assuming this data is similar to a micro array and not performign voom.## Limma step 3/6: running lmFit with method: ls.## Limma step 4/6: making and fitting contrasts with no intercept. (~ 0 + factors)## Limma step 5/6: Running eBayes with robust = FALSE and trend = FALSE.## Limma step 6/6: Writing limma outputs.## Limma step 6/6: 1/1: Creating table: failure_vs_cure. Adjust = BH## Limma step 6/6: 1/2: Creating table: cure. Adjust = BH## Limma step 6/6: 2/2: Creating table: failure. Adjust = BH## The factor cure has 104 rows.## The factor failure has 58 rows.## plot labels was not set and there are more than 100 samples, disabling it.## Testing each factor against the others.## Scoring cure against everything else.## Scoring failure against everything else.## Deleting the file excel/individual_celltypes_gsva_c2.xlsx before writing the tables.hs_celltype_gsva_c2_sig$subset_ploths_celltype_gsva_c2_sig$score_plotbroad_c7 <- load_gmt_signatures(signatures="reference/msigdb/c7.all.v7.2.entrez.gmt",
signature_category="c7")
hs_celltype_gsva_c7 <- simple_gsva(hs_valid,
signatures="reference/msigdb/c7.all.v7.2.entrez.gmt",
signature_category="c7",
msig_xml="reference/msigdb_v7.2.xml",
cores=10)## Converting the rownames() of the expressionset to ENTREZID.## 588 ENSEMBL ID's didn't have a matching ENTEREZ ID. Dropping them now.## Before conversion, the expressionset has 19923 entries.## After conversion, the expressionset has 19495 entries.## Adding annotations from reference/msigdb_v7.2.xml.hs_celltype_gsva_c7_sig <- get_sig_gsva_categories(
hs_celltype_gsva_c7,
excel="excel/individual_celltypes_gsva_c7.xlsx")## Starting limma pairwise comparison.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$libsize.## Limma step 1/6: choosing model.## Choosing the non-intercept containing model.## Assuming this data is similar to a micro array and not performign voom.## Limma step 3/6: running lmFit with method: ls.## Limma step 4/6: making and fitting contrasts with no intercept. (~ 0 + factors)## Limma step 5/6: Running eBayes with robust = FALSE and trend = FALSE.## Limma step 6/6: Writing limma outputs.## Limma step 6/6: 1/1: Creating table: failure_vs_cure. Adjust = BH## Limma step 6/6: 1/2: Creating table: cure. Adjust = BH## Limma step 6/6: 2/2: Creating table: failure. Adjust = BH## The factor cure has 104 rows.## The factor failure has 58 rows.## plot labels was not set and there are more than 100 samples, disabling it.## Testing each factor against the others.## Scoring cure against everything else.## Scoring failure against everything else.## Deleting the file excel/individual_celltypes_gsva_c7.xlsx before writing the tables.11.2.2 Print some plots of the GSVA outputs
## The raw heatmap of the C2 values
hs_celltype_gsva_c2_sig[["raw_plot"]]
## The 'significance' scores of the C2 values
hs_celltype_gsva_c2_sig[["score_plot"]]
## The subset of scores for categories deemed significantly different.
hs_celltype_gsva_c2_sig[["subset_plot"]]
## The raw heatmap of the C7 values
hs_celltype_gsva_c7_sig[["raw_plot"]]
## The 'significance' scores of the C7 values
hs_celltype_gsva_c7_sig[["score_plot"]]
## The subset of scores for categories deemed significantly different.
hs_celltype_gsva_c7_sig[["subset_plot"]]
12 Concordance
Let us compare various results to see how well they agreed
monocyte_cor_subset <- cf_monocyte_sva_tables[["data"]][["fail_vs_cure"]][, c("deseq_logfc", "deseq_adjp")]
neutrophil_cor_subset <- cf_neutrophil_tables[["data"]][["fail_vs_cure"]][, c("deseq_logfc", "deseq_adjp")]
eosinophil_cor_subset <- cf_eosinophil_tables[["data"]][["fail_vs_cure"]][, c("deseq_logfc", "deseq_adjp")]
mono_neut_cor <- merge(monocyte_cor_subset, neutrophil_cor_subset, by="row.names")
mono_eo_cor <- merge(monocyte_cor_subset, eosinophil_cor_subset, by="row.names")
neut_eo_cor <- merge(neutrophil_cor_subset, eosinophil_cor_subset, by="row.names")
cor.test(mono_neut_cor[["deseq_logfc.x"]], mono_neut_cor[["deseq_logfc.y"]])##
## Pearson's product-moment correlation
##
## data: mono_neut_cor[["deseq_logfc.x"]] and mono_neut_cor[["deseq_logfc.y"]]
## t = 41, df = 8722, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3845 0.4197
## sample estimates:
## cor
## 0.4022monocyte_neutrophil_aucc <- calculate_aucc(cf_monocyte_sva_tables[["data"]][["fail_vs_cure"]],
cf_neutrophil_tables[["data"]][["fail_vs_cure"]],
px="deseq_p", py="deseq_p", ly="deseq_logfc", lx="deseq_logfc")
monocyte_neutrophil_aucc$plot
cor.test(mono_eo_cor[["deseq_logfc.x"]], mono_eo_cor[["deseq_logfc.y"]])##
## Pearson's product-moment correlation
##
## data: mono_eo_cor[["deseq_logfc.x"]] and mono_eo_cor[["deseq_logfc.y"]]
## t = 44, df = 9953, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3858 0.4187
## sample estimates:
## cor
## 0.4024monocyte_eosinophil_aucc <- calculate_aucc(cf_monocyte_sva_tables[["data"]][["fail_vs_cure"]],
cf_eosinophil_tables[["data"]][["fail_vs_cure"]],
px="deseq_p", py="deseq_p", ly="deseq_logfc", lx="deseq_logfc")
monocyte_eosinophil_aucc$plot
cor.test(neut_eo_cor[["deseq_logfc.x"]], neut_eo_cor[["deseq_logfc.y"]])##
## Pearson's product-moment correlation
##
## data: neut_eo_cor[["deseq_logfc.x"]] and neut_eo_cor[["deseq_logfc.y"]]
## t = 31, df = 8689, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2998 0.3376
## sample estimates:
## cor
## 0.3188neutrophil_eosinophil_aucc <- calculate_aucc(cf_neutrophil_tables[["data"]][["fail_vs_cure"]],
cf_eosinophil_tables[["data"]][["fail_vs_cure"]],
px="deseq_p", py="deseq_p", ly="deseq_logfc", lx="deseq_logfc")
neutrophil_eosinophil_aucc$plot
13 Classify me!
I wrote out all the z2.2 and z2.3 specific variants to a couple files, I want to see if I can classify a human sample as infected with 2.2 or 2.3.
z22 <- read.csv("csv/variants_22.csv")
z23 <- read.csv("csv/variants_23.csv")
cure <- read.csv("csv/cure_variants.txt")
fail <- read.csv("csv/fail_variants.txt")
z22_vec <- gsub(pattern="\\-", replacement="_", x=z22[["x"]])
z23_vec <- gsub(pattern="\\-", replacement="_", x=z23[["x"]])
cure_vec <- gsub(pattern="\\-", replacement="_", x=cure)
fail_vec <- gsub(pattern="\\-", replacement="_", x=fail)
classify_zymo <- function(sample) {
arbitrary_tags <- sm(readr::read_tsv(sample))
arbitrary_ids <- arbitrary_tags[["position"]]
message("Length: ", length(arbitrary_ids), ", z22: ",
sum(arbitrary_ids %in% z22_vec) / (length(z22_vec)), " z23: ",
sum(arbitrary_ids %in% z23_vec) / (length(z23_vec)))
}
arbitrary_sample <- "preprocessing/TMRC30156/outputs/40freebayes_lpanamensis_v36/all_tags.txt.xz"
classify_zymo(arbitrary_sample)if (!isTRUE(get0("skip_load"))) {
pander::pander(sessionInfo())
message(paste0("This is hpgltools commit: ", get_git_commit()))
message(paste0("Saving to ", savefile))
tmp <- sm(saveme(filename=savefile))
}tmp <- loadme(filename=savefile)---
title: "L. panamensis 202203: Reorganizing TMRC3 Analyses"
author: "atb abelew@gmail.com"
date: "`r Sys.Date()`"
output:
 html_document:
  code_download: true
  code_folding: show
  fig_caption: true
  fig_height: 7
  fig_width: 7
  highlight: default
  keep_md: false
  mode: selfcontained
  number_sections: true
  self_contained: true
  theme: readable
  toc: true
  toc_float:
   collapsed: false
   smooth_scroll: false
---

<style>
  body .main-container {
    max-width: 1600px;
  }
</style>

```{r options, include=FALSE}
library(hpgltools)
library(dplyr)
library(forcats)
tt <- sm(devtools::load_all("~/hpgltools"))
knitr::opts_knit$set(progress=TRUE,
                     verbose=TRUE,
                     width=90,
                     echo=TRUE)
knitr::opts_chunk$set(error=TRUE,
                      fig.width=8,
                      fig.height=8,
                      dpi=96)
old_options <- options(digits=4,
                       stringsAsFactors=FALSE,
                       knitr.duplicate.label="allow")
ggplot2::theme_set(ggplot2::theme_bw(base_size=12))
ver <- "202203"
previous_file <- paste0("01_annotation_v", ver, ".Rmd")
rundate <- format(Sys.Date(), format="%Y%m%d")

##tmp <- try(sm(loadme(filename=gsub(pattern="\\.Rmd", replace="\\.rda\\.xz", x=previous_file))))
rmd_file <- "tmrc3_reorganized_202203.Rmd"
savefile <- gsub(pattern="\\.Rmd", replace="\\.rda\\.xz", x=rmd_file)
```

# Introduction

The set of analyses performed in tmrc3 sample estimation has become
unorganized and difficult to follow.  This is intended to start at the
level of broad organization before stepping into the analyses.

As of 20220224, this document is attempting to synchronize with the
google doc named 'TMRC3_Aug18_2021'.  I am hoping therefore to have
blocks named according to the figures/etc in that document.

## Goals

These samples are from patients who either successfully cleared a
Leishmania panamensis infection following treatment, or did not.  They
include biopsies from each patient along with purifications for
Monocytes, Neutrophils, and Eosinophils.  When possible, this process
was repeated over three visits; but some patients did not return
for the second or third visit.

The over-arching goal is to look for attributes(most likely genes)
which distinguish patients who do and do not cure the infection after
treatment.  If possible, these will be apparent on the first visit.

## Relevant Metadata

The metadata factors (in no particular order) of the experiment are:

1.  Visit.  1, 2, or 3.
2.  Patient.
3.  Clinical outcome: cure or fail.  This includes the population of
    patients who did not return and are labeled 'lost'.  This is, by
    definition, confounded with patient.
4.  Drug treatment.  Most of the patients were treated with an
    antimonial, but a few were treated with miltefosine.
5.  Cell type or biopsy.

Metadata was also collected for the patients and there are many
factors which have potential to affect the outcome.  These analyses
will generally remain agnostic for these factors, which include (among
other things):

1.  sex
2.  ethnicity
4.  age
5.  ulcer/lesion attributes (by visit)
6.  amount of time spent with infection

## Libraries, sequencing, mapping, quantification

The samples used in these analyses were all collected, purified, and
libraries generated by the scientists/doctors at CIDEIM.  The
sequencing libraries were generated via the TruSeq non-stranded
library kit and sequenced either at JHU or UMD; earlier samples were
single-ended, but most were paired.

All samples were trimmed with trimomatic using the same set of
parameters.  All mapping was performed with hisat2 version 2.2.1.
Quantifications were also performed with salmon version 1.2.0.  The
reference genome used was hg38 100 (released 202004).  When samples
were mapped against L.panamensis, the TriTrypDB version 36 reference
was used.  The set of annotations were therefore limited to ensembl's
2020 release.  The early samples were actually first mapped with
hg38 91 and later redone.

## Types of analyses included

This document will limit itself to a set of canonical RNAseq
analyses.  It must therefore create files containing the raw data to
facilitate sharing the data.  It will plot metrics of the data to
demonstrate the sequencing quality and clustering of the samples under
the various conditions examined and normalizations employed.  It will
perform differential expression analyses for the metadata factors of
interest alongside likelihood ratio tests for factors like celltype
and time.  Given the sets of over/under expressed genes observed in
the various DE methods, it will perform the likely gene set tests for
over represented gene ontology groups, reactome, etc.  Simultaneously,
the raw data will be passed to gene set variance analyses to see if
there are groups of genes overrepresented in other experiments.

## Metadata collection

There are two metadata sources:

1.  The online sample sheet, which I periodically update and download
    into the 'sample_sheets/' directory.
2.  The crf metadata, describing the individual patients.

```{r metadata_sources}
samplesheet <- "sample_sheets/tmrc3_samples_202203.xlsx"
crf_metadata <- "sample_sheets/20210825_EXP_ESPECIAL_TMRC3_VERSION_2.xlsx"
```

# Annotation Collection

The primary annotation sources are:

1.  Ensembl's biomart archive from 2020 for human annotations.
2.  The TriTrypDB release 36 for parasite annotations.

Both provide GO data.  They also provide helpful links to other data
sources.  For the moment, we are focusing on the human annotations.

## Gene annotations

These analyses have focused on gene-level abundances/differences.
Thus, when htseq-count was invoked against the hisat2-based mappings,
parameters were chosen to count genes rather than transcripts.
Similarly, when salmon counts were used via tximport, a mapping of
genes to transcripts was used to collapse the matrix to gene-level
abundances.  This decision may be revisited.

```{r collect_annotations}
hs_annot <- load_biomart_annotations(year="2020", month="jan")

hs_annot <- hs_annot[["annotation"]]
hs_annot[["transcript"]] <- paste0(rownames(hs_annot), ".", hs_annot[["version"]])
rownames(hs_annot) <- make.names(hs_annot[["ensembl_gene_id"]], unique=TRUE)
tx_gene_map <- hs_annot[, c("transcript", "ensembl_gene_id")]
summary(hs_annot)
```

## Gene ontology data

The set of GO categories has not been limited to the 2020 data at the
time of this writing.

```{r hs_go}
hs_go <- load_biomart_go()[["go"]]
hs_length <- hs_annot[, c("ensembl_gene_id", "cds_length")]
colnames(hs_length) <- c("ID", "length")
```

# The full dataset

Before we do any of the following subsets/analyses of the data, we
need to collect it all in one place.  Let's do that here and name it
'hs_valid' meaning it is the set of valid data for Homo sapiens.

```{r all_data}
sanitize_columns <- c("visitnumber", "clinicaloutcome", "donor",
                      "typeofcells", "clinicalpresentation", "drug",
                      "condition", "batch")
hs_expt <- create_expt(samplesheet,
                       file_column="hg38100hisatfile",
                       savefile=glue::glue("rda/hs_expt_all-v{ver}.rda"),
                       gene_info=hs_annot) %>%
  exclude_genes_expt(column="gene_biotype", method="keep",
                     pattern="protein_coding", meta_column="ncrna_lost") %>%
  sanitize_expt_metadata(columns=sanitize_columns) %>%
  set_expt_factors(columns=sanitize_columns, class="factor") %>%
  set_expt_conditions(fact="clinicaloutcome") %>%
  set_expt_batches(fact="visitnumber")

## The following should make visit 1 the largest if one uses that column as a size factor when plotting.
meta <- pData(hs_expt) %>%
  mutate(visitnumber = fct_relevel(visitnumber, c("notapplicable", "3", "2", "1")))
pData(hs_expt) <- meta
```

The above block does the following:

1.  Creates an expressionset using the 'hg38100hisatfile' column from
    the most recently downloaded sample sheet (the column's name has
    any puctuation/spaces/capitals/etc removed) and the set of human
    annotations downloaded above.
2.  This expressionset is passed to a filter which pulls out only the
    protein_coding genes and uses the information from that process to
    add a new metadata column called 'ncrna_lost'. Thus it keeps a
    tally of the number of reads lost in the filter and adds it to the
    sample sheet.
3.  It is passed again to a function which sanitizes the metadata
    (there were a couple of entries which said 'cure' instead of
    'Cure' or vice versa) and similarly removes problematic
    characters.
4.  Passed to a function which sets some columns explicitly to factors
    instead of characters.
5.  Sets the experimental 'condition' to the factor of cure vs. fail.
6.  Sets the experimental 'batch' to visit number.
7.  Resets the levels of the visit number so that the samples which
    were 'notapplicable' are logically before visit 1 which is before
    2 before 3.

## Add the CRF patient metadata

Let us also merge in the clinician's metadata.  I worry a little that
this might not be allowed for dbGap data, but if it is a problem I
suspect we can just remove the bad columns from it.  Also note that I
rarely use the join function, but it is somewhat required here because
I do not want to risk shuffling the metadata when I add the new
metadata, which comes from a spreadsheet sorted by patient, not
sample.  In doing this I therefore just created a new column 'join'
which contains the shared information, e.g. the patient ID
from the existing metadata and the same ID from the CRF file which has
been coerced into lowercase.

```{r merge_crf}
hs_pd <- pData(hs_expt)
start <- rownames(hs_pd)
hs_crf <- openxlsx::read.xlsx(crf_metadata)
hs_crf[["join"]] <- tolower(hs_crf[["codigo_paciente"]])
hs_pd[["join"]] <- hs_pd[["tubelabelorigin"]]
test <- plyr::join(hs_pd, hs_crf, by="join")
test[["join"]] <- NULL
rownames(test) <- rownames(hs_pd)
na_idx <- is.na(test)
test[na_idx] <- "undefined"
pData(hs_expt) <- test
```

The above block does the following:

1.  Extracts the metadata and makes a character vector of the rownames
    (e.g. the sample IDs).
2.  Creates a table of the clinical metadata from the file I
    downloaded.
3.  Creates a 'join' column from both the existing metadata and the clinical
    metadata which is the patient code, using whatever the column
    names were in each sheet.
4.  Joins the two tables using this information.  The join function is
    explicitly used in this context to make sure that the row-order of
    the entries does not change.
5.  Gets rid of any entries in the new table which are NA.
6.  Replaces the metadata of the expressionset with this new, larger table.

# Sanity check, clinical outcome vs. CRF data

In our shared online sample sheet there is a clinical outcome column
which should match the final CRF column 'ef_lc_estado_final_estudio'
with the caveat that the CRF data is numeric:

0: curacion definitiva
1: fall terapeutica
2: perdida druante el seguimiento
3: excludio durante el estudio

```{r cf_sanity}
two_columns <- pData(hs_expt)[, c("clinicaloutcome", "ef_lc_estado_final_estudio")]
undef_idx <- two_columns[[2]] == "undefined"
two_columns <- two_columns[!undef_idx, ]
two_columns[["rewritten"]] <- "undef"
cure_idx <- two_columns[[2]] == 0
two_columns[cure_idx, "rewritten"] <- "cure"
fail_idx <- two_columns[[2]] == 1
two_columns[fail_idx, "rewritten"] <- "failure"
lost_idx <- two_columns[[2]] == 2
two_columns[lost_idx, "rewritten"] <- "lost"
same_idx <- two_columns[["clinicaloutcome"]] == two_columns[["rewritten"]]
broken <- two_columns[!same_idx, ]\
broken
```

## Collect sample numbers before filtering

```{r table_valid_pre}
dim(pData(hs_expt))
table(pData(hs_expt)$drug)
table(pData(hs_expt)$clinic)
table(pData(hs_expt)$typeofcells)
table(pData(hs_expt)$visit)
summary(as.numeric(pData(hs_expt)$eb_lc_tiempo_evolucion))
summary(as.numeric(pData(hs_expt)$eb_lc_tto_mcto_glucan_dosis))
summary(as.numeric(pData(hs_expt)$v3_lc_ejey_lesion_mm_1))
summary(as.numeric(pData(hs_expt)$v3_lc_lesion_area_1))
summary(as.numeric(pData(hs_expt)$v3_lc_ejex_ulcera_mm_1))
table(pData(hs_expt)$eb_lc_sexo)
table(pData(hs_expt)$eb_lc_etnia)
summary(as.numeric(pData(hs_expt)$edad))
table(pData(hs_expt)$eb_lc_peso)
table(pData(hs_expt)$eb_lc_estatura)
table(pData(hs_expt)$clinic)

table(pData(hs_expt)$ef_lc_estado_final_estudio)
table(pData(hs_expt)$clinicaloutcome)
```

## Define desired colors for the various subsets

There are lots of ways which we will categorize the data, here are
some potential color choices for them.

```{r color_choices}
cf_colors <- list(
    "cure" = "#998EC3",
    "failure" = "#F1A340")
type_visit_colors <- list(
    "monocytes_v1" = "#DD1C77",
    "monocytes_v2" = "#C994C7",
    "monocytes_v3" = "#E7E1EF",
    "eosinophils_v1" = "#31A354",
    "eosinophils_v2" = "#ADDD8E",
    "eosinophils_v3" = "#F7FCD9",
    "neutrophils_v1" = "#3182BD",
    "neutrophils_v2" = "#9ECAE1",
    "neutrophils_v3" = "#DEEBF7",
    "biopsy_v1" = "#D95F0E")
type_colors <- list(
    "monocytes" = "#DD1C77",
    "eosinophils" = "#31A354",
    "neutrophils" = "#3182BD",
    "biopsy" = "#D95F0E")
visit_colors <- list()
cf_type_colors <- list(
    cure_biopsy = "#D95F0E",
    failure_biopsy = "#FEC44F",
    cure_monocytes = "#DD1C77",
    failure_monocytes = "#C994C7",
    cure_eosinophils = "#31A354",
    failure_eosinophils = "#ADDD8E",
    cure_neutrophils = "#3182BD",
    failure_neutrophils = "#9ECAE1")
```

Note from Maria Adelaida: Some chemokines are suggestive of Eosinophil recruitment.

## Define the starting data

The following block contains the primary dataset which is the parent
of everything which follows.

### Set our initial coverage goal

There exists a baseline coverage below which we do not wish to fall.
One likely way to approach it heuristically is to assume we should
observe some number of genes.  With that in mind, I arbitrarily chose
11,000 non-zero genes as the minimum.

With this in mind, here is a non-zero plot before a cutoff of 11,000
genes.

```{r hs_pre_nz}
all_nz <- plot_nonzero(hs_expt)
all_nz$plot
```

To my eyes, there are 3 or 4 samples which are likely candidates for removal.

```{r hs_valid}
hs_valid <- subset_expt(hs_expt, nonzero=11000) %>%
  subset_expt(subset="clinicaloutcome!='lost'") %>%
  subset_expt(subset="clinicaloutcome!='notapplicable'") %>%
  subset_expt(subset="clinicaloutcome!='null'") %>%
  set_expt_colors(cf_colors)
```

### After removal

Here is the distribution of samples after dropping the less than
11,000 gene-samples.

```{r nz_post}
nz_post <- plot_nonzero(hs_valid)
nz_post$plot
```

## Count up sample types

I recently convinced myself that there is a difference between the
data when it does and does not include the miltefosine treated
patients.

However, when I actually counted up the samples by a few criteria I
immediately relized this is spurious.

```{r table_valid}
table(pData(hs_valid)$drug)
table(pData(hs_valid)$clinic)
table(pData(hs_valid)$clinicaloutcome)
table(pData(hs_valid)$typeofcells)
table(pData(hs_valid)$visit)
summary(as.numeric(pData(hs_valid)$eb_lc_tiempo_evolucion))
summary(as.numeric(pData(hs_valid)$eb_lc_tto_mcto_glucan_dosis))
summary(as.numeric(pData(hs_valid)$v3_lc_ejey_lesion_mm_1))
summary(as.numeric(pData(hs_valid)$v3_lc_lesion_area_1))
summary(as.numeric(pData(hs_valid)$v3_lc_ejex_ulcera_mm_1))
table(pData(hs_valid)$eb_lc_sexo)
table(pData(hs_valid)$eb_lc_etnia)
summary(as.numeric(pData(hs_valid)$edad))
table(pData(hs_valid)$eb_lc_peso)
table(pData(hs_valid)$eb_lc_estatura)
table(pData(hs_valid)$clinic)
```

The above block does what it says on the tin, subsets the data to
exclude any sample with less than 11,000 observed genes.

## Set up initial data subsets of interest

One of the first global metrics I would like to provide is the set of
library sizes.  Unfortunately, we have too many samples to fit on a
screen now.  Therefore, I am going to do an early split of the data by
cell type in the hopes that doing so will make it possible to
visualize the library sizes/nonzero genes.

The initial factor for this is 'typeofcells'.

```{r initial_subsets}
table(pData(hs_valid)[["typeofcells"]])
biopsy_samples <- subset_expt(hs_valid, subset="typeofcells=='biopsy'")
eosinophil_samples <- subset_expt(hs_valid, subset="typeofcells=='eosinophils'")
monocyte_samples <- subset_expt(hs_valid, subset="typeofcells=='monocytes'")
neutrophil_samples <- subset_expt(hs_valid, subset="typeofcells=='neutrophils'")

## Currently, these are not used, but instead I pulled the samples from the hs_clinical
## which means the biopsies are not included.
v1_samples <- subset_expt(hs_valid, subset="visitnumber=='1'")
v1_monocytes <- subset_expt(v1_samples, subset="typeofcells=='monocytes'")
v1_neutrophils <- subset_expt(v1_samples, subset="typeofcells=='neutrophils'")
v1_eosinophils <- subset_expt(v1_samples, subset="typeofcells=='eosinophils'")

v2_samples <- subset_expt(hs_valid, subset="visitnumber=='2'")
v2_monocytes <- subset_expt(v2_samples, subset="typeofcells=='monocytes'")
v2_neutrophils <- subset_expt(v2_samples, subset="typeofcells=='neutrophils'")
v2_eosinophils <- subset_expt(v2_samples, subset="typeofcells=='eosinophils'")

v3_samples <- subset_expt(hs_valid, subset="visitnumber=='3'")
v3_monocytes <- subset_expt(v3_samples, subset="typeofcells=='monocytes'")
v3_neutrophils <- subset_expt(v3_samples, subset="typeofcells=='neutrophils'")
v3_eosinophils <- subset_expt(v3_samples, subset="typeofcells=='eosinophils'")
```

The above block does a lot of subset operations to create separate
data structures on a per-celltype and per-visit basis.  Ergo, our
large data structure is now joined by ~21 new, smaller data
structures.

# Parasite reads

Let us see if we can make an expressionset of the parasite reads in
the TMRC3 samples and distinguish between the faux and real reads.
E.g: Are there samples which definitively contain parasites?

```{r parasite_expt, eval=FALSE}
lp_expt <- create_expt("sample_sheets/tmrc3_samples_20211207.xlsx",
                       file_column="lpanamensisv36hisatfile", gene_info = NULL) %>%
  subset_expt(coverage=1000) %>%
  set_expt_conditions(fact="typeofcells")
visit_fact <- pData(lp_expt)[["visitnumber"]]
batch_na <- is.na(visit_fact)
visit_fact[batch_na] <- "undefined"
lp_expt <- set_expt_batches(lp_expt, fact = visit_fact)

lp_norm <- normalize_expt(lp_expt, filter="simple", norm="quant",
                          convert="cpm", transform="log2")
plotted <- plot_pca(lp_norm, plot_labels=FALSE)
plotted$plot
plotted_3d <- plot_3d_pca(plotted)
```

The above block is similar in concept to the previous expressionset
creation.  It uses a different column and currently ignores the gene
annotations.  Given that many of the samples have essentially 0 reads,
I set a cutoff of only 20 observed genes.  Finally, I did a quick and
dirty PCA plot of this peculiar data structure in the hopes of being
able to 'see' the difference between what are assumed to be 'real'
samples with a significant number of 'real' parasite reads vs. those
samples which just have a couple of potentially spurious reads.

# Contrasts and colors of interest

This might be a bit early to consider the contrasts, but I think we
should consider this question immediately.  The main thing we will be
comparing is of course cure vs. fail; but we may also look at the
visits and compare cell types.

```{r define_contrasts}
cf_contrasts <- list(
    "fail_vs_cure" = c("failure", "cure"))
visit_contrasts <- list(
    "v2v1" = c("c2", "c1"),
    "v3v1" = c("c3", "c1"),
    "v3v2" = c("c3", "c2"))
type_contrasts <- list(
    "mono_biopsy" = c("monocytes", "biopsy"),
    "eosinophil_biopsy" = c("eosinophils", "biopsy"),
    "neutrophil_biopsy" = c("neutrophils", "biopsy"))
visit_cf_contrasts <- list(
    "v1fail_vs_cure" = c("v1failure", "v1cure"),
    "v2fail_vs_cure" = c("v2failure", "v2cure"),
    "v3fail_vs_cure" = c("v3failure", "v3cure"))
clinic_contrasts <- list(
    "clinics" = c("Cali", "Tumaco"))
```

Sample IDs starting with 1: Cali
       IDs starting with 2: Tumaco

# Distributions/Visualizations of interest

The sets of samples used to visualize the data will also comprise the
sets used when later performing the various differential expression
analyses.

## Global metrics

Start out with some initial metrics of all samples.  The most obvious
are plots of the numbers of non-zero genes observed, heatmaps showing
the relative relationships among the samples, the relative library
sizes, and some PCA.  It might be smart to split the library sizes up
across subsets of the data, because they have expanded too far to see
well on a computer screen.

The most likely factors to query when considering the entire dataset
are cure/fail, visit, and cell type.  This is the level at which we
will choose samples to exclude from future analyses.

```{r global_distributions}
plot_legend(biopsy_samples)$plot
plot_libsize(biopsy_samples)$plot
plot_nonzero(biopsy_samples)$plot
biopsy_prepost <- plot_libsize_prepost(biopsy_samples)
biopsy_prepost$count_plot
biopsy_prepost$lowgene_plot
## Minimum number of biopsy genes: ~ 14,000

plot_libsize(eosinophil_samples)$plot
plot_nonzero(eosinophil_samples)$plot
eosinophil_prepost <- plot_libsize_prepost(eosinophil_samples)
eosinophil_prepost$count_plot
eosinophil_prepost$lowgene_plot
## Minimum number of eosinophil genes: ~ 13,500

plot_libsize(monocyte_samples)$plot
plot_nonzero(monocyte_samples)$plot
monocyte_prepost <- plot_libsize_prepost(monocyte_samples)
monocyte_prepost$count_plot
monocyte_prepost$lowgene_plot
## Minimum number of monocyte genes: ~ 7,500 before setting the minimum.

plot_libsize(neutrophil_samples)$plot
plot_nonzero(neutrophil_samples)$plot
neutrophil_prepost <- plot_libsize_prepost(neutrophil_samples)
neutrophil_prepost$count_plot
neutrophil_prepost$lowgene_plot
## Minimum number of neutrophil genes: ~ 10,000 before setting minimum coverage.
```

The above block just repeats the same two plots on a per-celltype
basis: the number of reads observed / sample and a plot of observed
genes with respect to coverage.  I made some comments with my
observations about the number of genes.

## Global PCA

Now that those 'global' metrics are out of the way, lets look at some
global metrics of the data following normalization; the most likely
plots are of course PCA but also a couple of heatmaps.

### Figure 1

In the google doc TMRC3_Aug18_2021, there is an example of an image
for the first figure:

"Transcriptomic profiles of primary innate cells of CL patients show
unique transcriptional signatures - Remove PBMCs and M0, maybe
biopsies as well (but Remove WT samples)"

While we were talking in a meeting however, it sounded like there was
some desire to keep all cell types.  Therefore the following block
will have one image with everything and one following the above.

```{r global_pca}
type_valid <- set_expt_conditions(hs_valid, fact="typeofcells") %>%
  set_expt_batches(fact="clinicaloutcome") %>%
  set_expt_colors(type_colors)

all_norm <- sm(normalize_expt(type_valid, transform="log2", norm="quant",
                              convert="cpm", filter=TRUE))

all_pca <- plot_pca(all_norm, plot_labels=FALSE,
                    plot_title="PCA - Cell type", size_column="visitnumber")
dev <- pp(file=glue("images/tmrc3_pca_nolabels-v{ver}.png"))
all_pca$plot
closed <- dev.off()
all_pca$plot

all_pca_nosize <- plot_pca(all_norm, plot_labels=FALSE)
all_pca_nosize$plot

write.csv(all_pca$table, file="coords/hs_donor_pca_coords.csv")
all_cf_norm <- set_expt_batches(all_norm,
                                fact="visitnumber")
all_cf_corheat <- plot_corheat(all_cf_norm, plot_title="Heirarchical clustering:
         cell types")

dev <- pp(file=glue("images/tmrc3_corheat_cf-v{ver}.png"))
all_cf_corheat$plot
closed <- dev.off()
all_cf_corheat$plot

all_cf_disheat <- plot_disheat(all_cf_norm, plot_title="Heirarchical clustering:
         cell types")
dev <- pp(file=glue("images/tmrc3_disheat_cf-v{ver}.png"))
all_cf_disheat$plot
closed <- dev.off()
all_cf_disheat$plot
```

## Figure 1B: Transcriptomic profiles of primary innate

The biggest caveat for this is to ensure that there are no Wellcome
Trust samples.

A potential figure legend for the following images might include:

The observed counts per gene for all of the clinical samples were
filtered, log transformed, cpm converted, and quantile normalized.
The colors were defined by cell types and shapes by patient visit.
When the first two principle components were plotted, clustering was
observed by cell type.  The biopsy samples were significantly
different from the innate immune cell types.

```{r fig1b_transcriptomic_profiles}
fig1v2_norm <- normalize_expt(type_valid, transform="log2",
                              convert="cpm", norm="quant", filter=TRUE)
fig1v2_pca <- plot_pca(fig1v2_norm, cis=FALSE)
dev <- pp(file=glue("images/tmrc3_fig1v2.png"))
fig1v2_pca$plot
closed <- dev.off()
fig1v2_pca$plot

fig1v3_samples <- subset_expt(type_valid, subset="condition!='biopsy'")
fig1v3_norm <- normalize_expt(fig1v3_samples, transform="log2",
                              convert="cpm", norm="quant", filter=TRUE)
fig1v3_pca <- plot_pca(fig1v3_norm, cis=FALSE)
dev <- pp(file="images/tmrc3_fig1v3.png")
fig1v3_pca$plot
closed <- dev.off()
fig1v3_pca$plot
```

Continue looking, but switch the conditions/colors so that the
clinical outcome becomes the focus and get rid of a few samples which
are not actually a part of the TMRC3 focus (e.g. the PBMC and
macrophage samples, which are all from the Wellcome Trust).

### Clinically relevant samples

Included in this group will be the samples from patients who were lost.

#### Remove the lost samples

In our 20220218 meeting, it was decided that we would focus explicitly
on the cure and fail samples, ignoring lost/NA/null samples.

Thus I am adding explicit filters right at the top to exclude them.

#### Samples by clinic

```{r clinical_hs}
hs_clinical <- hs_valid %>%
  set_expt_conditions(fact="clinicaloutcome") %>%
  set_expt_batches(fact="typeofcells") %>%
  set_expt_colors(cf_colors)

hs_clinical_nobiop <- subset_expt(hs_clinical, subset="typeofcells!='biopsy'")
```

#### View biopsies

```{r clinic_biopsies_pca}
clinic_biopsy <- hs_valid %>%
  set_expt_conditions(fact="clinic") %>%
  set_expt_batches(fact="clinicaloutcome") %>%
  subset_expt(subset="typeofcells=='biopsy'")
clinic_cf <- paste0(pData(clinic_biopsy)$condition, "_",
                    pData(clinic_biopsy)$batch)
table(clinic_cf)
clinic_biopsy <- set_expt_conditions(clinic_biopsy, fact=clinic_cf) %>%
  set_expt_batches(fact="visitnumber")

clinic_biopsy_norm <- normalize_expt(clinic_biopsy, transform="log2",
                                     convert="cpm", norm="quant", filter=TRUE)
clinic_biopsy_pca <- plot_pca(clinic_biopsy_norm, plot_labels=FALSE)
dev <- pp(file="images/biopsy_place.png")
clinic_biopsy_pca$plot
closed <- dev.off()
clinic_biopsy_pca$plot

clinic_biopsy_nb <- normalize_expt(clinic_biopsy, transform="log2",
                                   convert="cpm", batch="svaseq", filter=TRUE)
clinic_biopsy_nb_pca <- plot_pca(clinic_biopsy_nb, plot_labels=FALSE)
dev <- pp(file="images/biopsy_place_nb.png")
clinic_biopsy_nb_pca$plot
closed <- dev.off()
clinic_biopsy_nb_pca$plot
```

#### Clinic Eosinophils

```{r clinic_eosinophil_pca}
clinic_eosinophil <- hs_clinical %>%
  set_expt_conditions(fact="clinic") %>%
  set_expt_batches(fact="clinicaloutcome") %>%
  subset_expt(subset="typeofcells=='eosinophils'")
clinic_cf <- paste0(pData(clinic_eosinophil)$condition, "_",
                    pData(clinic_eosinophil)$batch)
table(clinic_cf)
clinic_eosinophil <- set_expt_conditions(clinic_eosinophil, fact=clinic_cf) %>%
    set_expt_batches(fact="visitnumber")

clinic_eosinophil_norm <- normalize_expt(clinic_eosinophil, transform="log2",
                                         convert="cpm", norm="quant", filter=TRUE)
clinic_eosinophil_pca <- plot_pca(clinic_eosinophil_norm, plot_labels=FALSE)
dev <- pp(file="images/eosinophil_place.png")
clinic_eosinophil_pca$plot
closed <- dev.off()
clinic_eosinophil_pca$plot

clinic_eosinophil_nb <- normalize_expt(clinic_eosinophil, transform="log2",
                                       convert="cpm", batch="svaseq", filter=TRUE)
clinic_eosinophil_nb_pca <- plot_pca(clinic_eosinophil_nb, plot_labels=FALSE)
dev <- pp(file="images/eosinophil_place_nb.png")
clinic_eosinophil_nb_pca$plot
closed <- dev.off()
clinic_eosinophil_nb_pca$plot
```

#### Clinic Monocyte

```{r clinic_monocyte_pca}
clinic_monocyte <- hs_clinical %>%
  set_expt_conditions(fact="clinic") %>%
  set_expt_batches(fact="clinicaloutcome") %>%
  subset_expt(subset="typeofcells=='monocytes'")
clinic_cf <- paste0(pData(clinic_monocyte)$condition, "_",
                    pData(clinic_monocyte)$batch)
table(clinic_cf)
clinic_monocyte <- set_expt_conditions(clinic_monocyte, fact=clinic_cf) %>%
    set_expt_batches(fact="visitnumber")

clinic_monocyte_norm <- normalize_expt(clinic_monocyte, transform="log2",
                                       convert="cpm", norm="quant", filter=TRUE)
clinic_monocyte_pca <- plot_pca(clinic_monocyte_norm, plot_labels=FALSE)
dev <- pp(file="images/monocytes_place.png")
clinic_monocyte_pca$plot
closed <- dev.off()
clinic_monocyte_pca$plot

clinic_monocyte_nb <- normalize_expt(clinic_monocyte, transform="log2",
                                     convert="cpm", batch="svaseq", filter=TRUE)
clinic_monocyte_nb_pca <- plot_pca(clinic_monocyte_nb, plot_labels=FALSE)
dev <- pp(file="images/monocytes_place_nb.png")
clinic_monocyte_nb_pca$plot
closed <- dev.off()
clinic_monocyte_nb_pca$plot
```

## Clinic Neutrophils

```{r clinic_neutrophil_pca}
clinic_neutrophil <- hs_clinical %>%
  set_expt_conditions(fact="clinic") %>%
  set_expt_batches(fact="clinicaloutcome") %>%
  subset_expt(subset="typeofcells=='neutrophils'")
clinic_cf <- paste0(pData(clinic_neutrophil)$condition, "_",
                    pData(clinic_neutrophil)$batch)
table(clinic_cf)
clinic_neutrophil <- set_expt_conditions(clinic_neutrophil, fact=clinic_cf) %>%
    set_expt_batches(fact="visitnumber")

clinic_neutrophil_norm <- normalize_expt(clinic_neutrophil, transform="log2",
                                         convert="cpm", norm="quant", filter=TRUE)
clinic_neutrophil_pca <- plot_pca(clinic_neutrophil_norm, plot_labels=FALSE)
dev <- pp(file="images/neutrophil_place.png")
clinic_neutrophil_pca$plot
closed <- dev.off()
clinic_neutrophil_pca$plot

clinic_neutrophil_nb <- normalize_expt(clinic_neutrophil, transform="log2",
                                       convert="cpm", batch="svaseq", filter=TRUE)
clinic_neutrophil_nb_pca <- plot_pca(clinic_neutrophil_nb, plot_labels=FALSE)
dev <- pp(file="images/neutrophil_place_nb.png")
clinic_neutrophil_nb_pca$plot
closed <- dev.off()
clinic_neutrophil_nb_pca$plot
```

### Only Tumaco samples

A majority of samples came from tumaco, let us see what happens if we limit the data to them.

Let us therefore consider a couple of different view of this portion of the data.

```{r tumaco_only}
tumaco_clinical <- hs_clinical %>%
  subset_expt(subset="clinic=='Tumaco'")

tumaco_monocyte <- tumaco_clinical %>%
  subset_expt(subset="typeofcells=='monocytes'") %>%
  set_expt_conditions(fact="clinicaloutcome") %>%
  set_expt_batches(fact="visitnumber") %>%
  set_expt_samplenames("tubelabelorigin")
tumaco_monocyte_norm <- normalize_expt(tumaco_monocyte, transform="log2", convert="cpm",
                                       norm="quant", filter=TRUE)

tumaco_monocyte_pca <- plot_pca(tumaco_monocyte_norm, plot_labels=FALSE)
dev <- pp(file="images/monocytes_oneplace_norm.png")
tumaco_monocyte_pca$plot
closed <- dev.off()
tumaco_monocyte_pca$plot

tumaco_monocyte_nb <- normalize_expt(tumaco_monocyte, transform="log2", convert="cpm",
                                     batch="svaseq", filter=TRUE)
tumaco_monocyte_nb_pca <- plot_pca(tumaco_monocyte_nb, plot_labels=FALSE)
dev <- pp(file="images/monocytes_oneplace_norm_sva.png")
tumaco_monocyte_nb_pca$plot
closed <- dev.off()
tumaco_monocyte_nb_pca$plot
```

## Compare clinics

Let us move a direct comparison of the two clinics right to the top.

```{r clinic_comparison1}
hs_clinic <- hs_valid %>%
  set_expt_conditions(fact="clinic") %>%
  set_expt_batches(fact="typeofcells")

hs_clinic_norm <- normalize_expt(hs_clinic, transform="log2", convert="cpm",
                                 norm="quant", filter=TRUE)
hs_clinic_pca <- plot_pca(hs_clinic_norm)
hs_clinic_pca$plot
hs_clinic_nb <- normalize_expt(hs_clinic, transform="log2", convert="cpm",
                               batch="svaseq", filter=TRUE)
hs_clinic_nb_pca <- plot_pca(hs_clinic_nb)
hs_clinic_nb_pca$plot
```

### Compare clinics, all samples

```{r clinic_comparisons_all}
hs_clinic_de <- all_pairwise(hs_clinic, model_batch="svaseq", filter=TRUE)
hs_clinic_table <- combine_de_tables(
    hs_clinic_de, keepers=clinic_contrasts,
    excel=glue::glue("excel/hs_clinic_table-v{ver}.xlsx"))
hs_clinic_sig <- extract_significant_genes(
    hs_clinic_table,
    excel=glue::glue("excel/hs_clinic_sig-v{ver}.xlsx"))
```

### Clinic biopsy comparison

```{r clinic_biopsy_de}
clinic_biopsy_de <- all_pairwise(clinic_biopsy, model_batch="svaseq", filter=TRUE)
clinic_biopsy_table <- combine_de_tables(
    clinic_biopsy_de,
    excel=glue::glue("excel/clinic_biopsy_table-v{ver}.xlsx"))
clinic_biopsy_sig <- extract_significant_genes(
    clinic_biopsy_table,
    excel=glue::glue("excel/clinic_biopsy_sig-v{ver}.xlsx"))
```

### Clinic eosinophil comparison

```{r clinic_eosinophil_de}
clinic_eosinophil_de <- all_pairwise(clinic_eosinophil, model_batch=FALSE, filter=TRUE)
clinic_eosinophil_table <- combine_de_tables(
    clinic_eosinophil_de,
    excel=glue::glue("excel/clinic_eosinophil_table-v{ver}.xlsx"))
clinic_eosinophil_sig <- extract_significant_genes(
    clinic_eosinophil_table,
    excel=glue::glue("excel/clinic_eosinophil_sig-v{ver}.xlsx"))
```

### Clinic monocyte comparison

```{r clinic_monocyte_de}
clinic_monocyte_de <- all_pairwise(clinic_monocyte, model_batch=FALSE, filter=TRUE)
clinic_monocyte_table <- combine_de_tables(
    clinic_monocyte_de,
    excel=glue::glue("excel/clinic_monocyte_table-v{ver}.xlsx"))
clinic_monocyte_sig <- extract_significant_genes(
    clinic_monocyte_table,
    excel=glue::glue("excel/clinic_monocyte_sig-v{ver}.xlsx"))
```

### Clinic neutrophil comparison

```{r clinic_neutrophil_de}
clinic_neutrophil_de <- all_pairwise(clinic_neutrophil, model_batch=FALSE, filter=TRUE)
clinic_neutrophil_table <- combine_de_tables(
    clinic_neutrophil_de,
    excel=glue::glue("excel/clinic_neutrophil_table-v{ver}.xlsx"))
clinic_neutrophil_sig <- extract_significant_genes(
    clinic_neutrophil_table,
    excel=glue::glue("excel/clinic_neutrophil_sig-v{ver}.xlsx"))
```

### Compare clinic genes

```{r compare_clinic_genes}
clinic_sigenes_up <- rownames(hs_clinic_sig[["deseq"]][["ups"]][[1]])
clinic_sigenes_down <- rownames(hs_clinic_sig[["deseq"]][["downs"]][[1]])
clinic_sigenes <- c(clinic_sigenes_up, clinic_sigenes_down)

contrast <- "Tumacocure_vs_Calicure"
clinic_biopsy_sigenes <- c(rownames(clinic_biopsy_sig[["deseq"]][["ups"]][[contrast]]),
                           rownames(clinic_biopsy_sig[["deseq"]][["downs"]][[contrast]]))
clinic_eosinophil_sigenes_up <- rownames(clinic_eosinophil_sig[["deseq"]][["ups"]][[contrast]])
clinic_eosinophil_sigenes_down <- rownames(clinic_eosinophil_sig[["deseq"]][["downs"]][[contrast]])
clinic_monocyte_sigenes_up <- rownames(clinic_monocyte_sig[["deseq"]][["ups"]][[contrast]])
clinic_monocyte_sigenes_down <- rownames(clinic_monocyte_sig[["deseq"]][["downs"]][[contrast]])
clinic_neutrophil_sigenes_up <- rownames(clinic_neutrophil_sig[["deseq"]][["ups"]][[contrast]])
clinic_neutrophil_sigenes_down <- rownames(clinic_neutrophil_sig[["deseq"]][["downs"]][[contrast]])

clinic_eosinophil_sigenes <- c(clinic_eosinophil_sigenes_up,
                               clinic_eosinophil_sigenes_down)
clinic_monocyte_sigenes <- c(clinic_monocyte_sigenes_up,
                             clinic_monocyte_sigenes_down)
clinic_neutrophil_sigenes <- c(clinic_neutrophil_sigenes_up,
                               clinic_neutrophil_sigenes_down)
```

### Place gprofiler

```{r clinic_gprofiler}
clinic_gp <- simple_gprofiler(clinic_sigenes)
clinic_gp$pvalue_plots$kegg_plot_over
clinic_gp$pvalue_plots$reactome_plot_over
clinic_gp <- simple_gprofiler(clinic_sigenes)
clinic_gp$pvalue_plots$kegg_plot_over
clinic_gp$pvalue_plots$reactome_plot_over
clinic_gp <- simple_gprofiler(clinic_sigenes)
clinic_gp$pvalue_plots$kegg_plot_over
clinic_gp$pvalue_plots$reactome_plot_over


clinic_eosinophil_gp <- simple_gprofiler(clinic_eosinophil_sigenes)
clinic_eosinophil_gp$pvalue_plots$kegg_plot_over
clinic_eosinophil_gp$pvalue_plots$reactome_plot_over

clinic_eosinophil_up_gp <- simple_gprofiler(clinic_eosinophil_sigenes_up)
clinic_eosinophil_up_gp$pvalue_plots$kegg_plot_over
clinic_eosinophil_up_gp$pvalue_plots$reactome_plot_over

clinic_eosinophil_down_gp <- simple_gprofiler(clinic_eosinophil_sigenes_down)
clinic_eosinophil_down_gp$pvalue_plots$kegg_plot_over
clinic_eosinophil_down_gp$pvalue_plots$reactome_plot_over


clinic_monocyte_gp <- simple_gprofiler(clinic_monocyte_sigenes)
clinic_monocyte_gp$pvalue_plots$kegg_plot_over
clinic_monocyte_gp$pvalue_plots$reactome_plot_over

clinic_monocyte_up_gp <- simple_gprofiler(clinic_monocyte_sigenes_up)
clinic_monocyte_up_gp$pvalue_plots$kegg_plot_over
clinic_monocyte_up_gp$pvalue_plots$reactome_plot_over

clinic_monocyte_down_gp <- simple_gprofiler(clinic_monocyte_sigenes_down)
clinic_monocyte_down_gp$pvalue_plots$kegg_plot_over
clinic_monocyte_down_gp$pvalue_plots$reactome_plot_over


clinic_neutrophil_gp <- simple_gprofiler(clinic_neutrophil_sigenes)
clinic_neutrophil_gp$pvalue_plots$kegg_plot_over
clinic_neutrophil_gp$pvalue_plots$reactome_plot_over

clinic_neutrophil_up_gp <- simple_gprofiler(clinic_neutrophil_sigenes_up)
clinic_neutrophil_up_gp$pvalue_plots$kegg_plot_over
clinic_neutrophil_up_gp$pvalue_plots$reactome_plot_over

clinic_neutrophil_down_gp <- simple_gprofiler(clinic_neutrophil_sigenes_down)
clinic_neutrophil_down_gp$pvalue_plots$kegg_plot_over
clinic_neutrophil_down_gp$pvalue_plots$reactome_plot_over
```

### Plot the clinical samples

Let us recolor the same plot by cure/fail followed by a concatenation
of the cell type and cure/fail.  In the following block, the clinical
samples are plotted once with the most common normalization
(log,cpm,quant,filtered) method followed by a plot of the data using
svaseq adjusted values and without quantile normalization.

Thus, the following block switches the colors of the groups to the clinical
state (cure/fail) and shapes by cell type.

```{r hs_clinical_plot}
hs_clinical_norm <- sm(normalize_expt(hs_clinical, filter="simple", transform="log2",
                                      norm="quant", convert="cpm"))
clinical_pca <- plot_pca(hs_clinical_norm, plot_labels=FALSE,
                         cis=NULL,
                         plot_title="PCA - clinical samples")
dev <- pp(file=glue("images/all_clinical_nobatch_pca-v{ver}.png"), height=8, width=16)
clinical_pca$plot
closed <- dev.off()
clinical_pca$plot

hs_clinical_nb <- normalize_expt(hs_clinical, filter="simple", transform="log2",
                                 batch="svaseq", convert="cpm")
hs_clinical_nb_pca <- plot_pca(hs_clinical_nb)
dev <- pp(file=glue("images/all_clinical_svaseqbatch_pca-v{ver}.png"), height=6, width=8)
hs_clinical_nb_pca$plot
closed <- dev.off()
hs_clinical_nb_pca$plot

clinical_pca_info <- pca_information(
    hs_clinical_norm, plot_pcas=TRUE, num_components = 30,
    expt_factors=c("visitnumber", "typeofcells", "clinicaloutcome",
                   "clinic"))
dev <- pp(file="images/clinical_samples_neglogp_pcs.png")
clinical_pca_info$anova_neglogp_heatmap
closed <- dev.off()
clinical_pca_info$anova_neglogp_heatmap
clinical_pca_info$pca_plots$PC20_PC29

clinical_scores <- pca_highscores(hs_clinical_norm)
clinical_scores[["highest"]][,"Comp.20"]
clinical_scores[["highest"]][,"Comp.27"]
```

```{r iterative_sv_removal}
first <- normalize_expt(hs_clinical, transform="log2", convert="cpm",
                        filter = TRUE, batch="svaseq", surrogates=1)
first_info <- pca_information(
    first, plot_pcas=TRUE, num_components = 30,
    expt_factors=c("visitnumber", "typeofcells", "clinicaloutcome",
                   "clinic"))
first_info$anova_neglogp_heatmap
first_info$pca_plots[["PC1_PC2"]]

second <- normalize_expt(hs_clinical, transform="log2", convert="cpm",
                         filter = TRUE, batch="svaseq", surrogates=2) %>%
  set_expt_batches(fact="clinic")
second_info <- pca_information(
    second, plot_pcas=TRUE, num_components = 30,
    expt_factors=c("visitnumber", "typeofcells", "clinicaloutcome",
                   "clinic"))
second_info$anova_neglogp_heatmap

third <- normalize_expt(hs_clinical, transform="log2", convert="cpm",
                        filter = TRUE, batch="svaseq", surrogates=3) %>%
  set_expt_batches(fact="clinic")
third_info <- pca_information(
    third, plot_pcas=TRUE, num_components = 30,
    expt_factors=c("visitnumber", "typeofcells", "clinicaloutcome",
                   "clinic"))
third_info$anova_neglogp_heatmap

fourth <- normalize_expt(hs_clinical, transform="log2", convert="cpm",
                         filter = TRUE, batch="svaseq", surrogates=4) %>%
  set_expt_batches(fact="clinic")
fourth_info <- pca_information(
    fourth, plot_pcas=TRUE, num_components = 30,
    expt_factors=c("visitnumber", "typeofcells", "clinicaloutcome",
                   "clinic"))
fourth_info$anova_neglogp_heatmap
fourth_info[["pca_plots"]][["PC1_PC2"]]

fifth <- normalize_expt(hs_clinical, transform="log2", convert="cpm",
                        filter = TRUE, batch="svaseq", surrogates=5) %>%
  set_expt_batches(fact="clinic")
fifth_info <- pca_information(
    fifth, plot_pcas=TRUE, num_components = 30,
    expt_factors=c("visitnumber", "typeofcells", "clinicaloutcome",
                   "clinic"))
fifth_info$anova_neglogp_heatmap
fifth_info[["pca_plots"]][["PC1_PC12"]]

sixth <- normalize_expt(hs_clinical, transform="log2", convert="cpm",
                        filter = TRUE, batch="svaseq", surrogates=6) %>%
  set_expt_batches(fact="clinic")
sixth_info <- pca_information(
    sixth, plot_pcas=TRUE, num_components = 30,
    expt_factors=c("visitnumber", "typeofcells", "clinicaloutcome",
                   "clinic"))
sixth_info$anova_neglogp_heatmap

seventh <- normalize_expt(hs_clinical, transform="log2", convert="cpm",
                          filter = TRUE, batch="svaseq", surrogates=7) %>%
  set_expt_batches(fact="clinic")
seventh_info <- pca_information(
    seventh, plot_pcas=TRUE, num_components = 30,
    expt_factors=c("visitnumber", "typeofcells", "clinicaloutcome",
                   "clinic"))
seventh_info$anova_neglogp_heatmap

eighth <- normalize_expt(hs_clinical, transform="log2", convert="cpm",
                        filter = TRUE, batch="svaseq", surrogates=8)
eighth_info <- pca_information(
    eighth, plot_pcas=TRUE, num_components = 30,
    expt_factors=c("visitnumber", "typeofcells", "clinicaloutcome",
                   "clinic"))
eighth_info$anova_neglogp_heatmap
```

### Concatenate cure/fail and cell type

In the following block the experimental condition was reset to the
concatenation of clinical outcome and type of cells.  There are an
insufficient number of biopsy samples for them to be useful in this
visualization, so they are ignored.

```{r cf_and_type}
desired_levels <- c("cure_biopsy", "failure_biopsy", "cure_eosinophils", "failure_eosinophils",
                    "cure_monocytes", "failure_monocytes", "cure_neutrophils", "failure_neutrophils")
new_fact <- factor(
    paste0(pData(hs_clinical)[["condition"]], "_",
           pData(hs_clinical)[["batch"]]),
    levels=desired_levels)

hs_clinical_concat <- set_expt_conditions(hs_clinical, fact = new_fact) %>%
  set_expt_batches(fact = "visitnumber") %>%
  set_expt_colors(cf_type_colors) %>%
  subset_expt(subset="typeofcells!='biopsy'")

## Try to ensure that the levels stay in the order I want
meta <- pData(hs_clinical_concat) %>%
  mutate(condition = fct_relevel(condition, desired_levels))
pData(hs_expt) <- meta

clinical_concat_norm <- normalize_expt(hs_clinical_concat, transform = "log2", convert = "cpm",
                                       norm = "quant", filter = TRUE)
clinical_concat_norm_pca <- plot_pca(clinical_concat_norm)
dev <- pp(file=glue("images/clinical_concatenated_normalized_pca-v{ver}.png"), height=6, width=10)
clinical_concat_norm_pca$plot
closed <- dev.off()
clinical_concat_norm_pca$plot

clinical_concat_nb <- normalize_expt(hs_clinical_concat, transform = "log2", convert = "cpm",
                                     batch = "svaseq", filter = TRUE)
clinical_concat_nb_pca <- plot_pca(clinical_concat_nb)
dev <- pp(file=glue("images/clinical_concatenated_svaseqbatch_pca-v{ver}.png"), height=6, width=12)
clinical_concat_nb_pca$plot
closed <- dev.off()
clinical_concat_nb_pca$plot

tumaco_concat <- subset_expt(hs_clinical_concat, subset="clinic=='Tumaco'")
tumaco_concat_norm <- normalize_expt(tumaco_concat, transform = "log2", convert = "cpm",
                                       norm = "quant", filter = TRUE)
tumaco_concat_norm_pca <- plot_pca(tumaco_concat_norm)
dev <- pp(file=glue("images/tumaco_concatenated_normalized_pca-v{ver}.png"), height=6, width=10)
tumaco_concat_norm_pca$plot
closed <- dev.off()
tumaco_concat_norm_pca$plot

tumaco_concat_nb <- normalize_expt(tumaco_concat, transform = "log2", convert = "cpm",
                                     batch = "svaseq", filter = TRUE)
tumaco_concat_nb_pca <- plot_pca(tumaco_concat_nb)
dev <- pp(file=glue("images/clinical_concatenated_svaseqbatch_pca-v{ver}.png"), height=6, width=12)
tumaco_concat_nb_pca$plot
closed <- dev.off()
tumaco_concat_nb_pca$plot
```

```{r testing_legends}
library(ggplot2)
wanted_legend_order <- c("cure_eosinophils","cure_monocytes","cure_neutrophils",
                         "failure_eosinophils","failure_monocytes","failure_neutrophils")
test <- plot_pca(clinical_concat_norm)
test$plot
test$plot +
  scale_color_discrete(breaks=wanted_legend_order) +
  guides(color=guide_legend(ncol=2))

test2 <- plot_legend(clinical_concat_norm)
```

### Samples separated by visit

Separate the samples by visit in order to more easily see what
patterns emerge across cell type and clinical outcome.

The most likely data set for this is hs_clinical.

#### All visits together

Now let us shift the view slightly to focus on changes observed over time.

```{r compare_all_visits}
visit_expt <- set_expt_conditions(hs_clinical, fact = "visitnumber") %>%
  set_expt_batches(fact = "clinicaloutcome") %>%
  subset_expt(subset="typeofcells!='biopsy'")

visit_norm <- normalize_expt(visit_expt, transform="log2", convert="cpm",
                             norm="quant", filter=TRUE)
plot_pca(visit_norm)$plot
visit_nb <- normalize_expt(visit_expt, transform = "log2", convert="cpm",
                           filter = TRUE, batch = "svaseq")
visit_nb_pca <- plot_pca(visit_nb)
dev <- pp(file=glue("images/visit_svaseqbatch_pca-v{ver}.png"), height=7, width=9)
visit_nb_pca$plot
closed <- dev.off()
visit_nb_pca$plot
```

## Top 1000 genes

repeat with only the most expressed genes from one cell type.

```{r top1000}
rpkm_values <- normalize_expt(type_valid, transform="log2", convert="rpkm",
                              filter=TRUE, column="cds_length")
rpkm_median <- median_by_factor(rpkm_values)
rpkm_tumaco_monocyte_idx <- order(rpkm_median[["medians"]][["monocytes"]], decreasing=TRUE)
rpkm_tumaco_monocyte <- rpkm_median[["medians"]][rpkm_tumaco_monocyte_idx, ]
tumaco_monocyte_gene <- head(rownames(rpkm_tumaco_monocyte), n=1000)
```

#### All visits, separated by cell type

Now separate the visits by cell type and whether or not the drug used
was the antimonial.

```{r cell_type_visits}
visit_monocyte <- subset_expt(visit_expt, subset = "typeofcells=='monocytes'")
visit_neutrophil <- subset_expt(visit_expt, subset = "typeofcells=='neutrophils'")
visit_eosinophil <- subset_expt(visit_expt, subset = "typeofcells=='eosinophils'")
```

#### Visit 1 alone

```{r visit1_data}
v1_clinical <- subset_expt(visit_expt, subset = "visitnumber=='1'") %>%
  set_expt_conditions(fact = "clinicaloutcome") %>%
  set_expt_batches(fact = "typeofcells")
v1_norm <- normalize_expt(v1_clinical, transform="log2", convert="cpm", norm="quant", filter=TRUE)

plot_pca(v1_norm)$plot
v1_nb <- normalize_expt(v1_clinical, transform = "log2", convert = "cpm",
                        filter = TRUE, batch = "svaseq")
plot_pca(v1_nb, plot_labels = FALSE)$plot
```

So, given the data on hand, there is slight ability to discern
cure and fail among the visit 1 samples.

#### Visit 2 alone

```{r visit2_data}
v2_clinical <- subset_expt(visit_expt, subset="visitnumber=='2'") %>%
  set_expt_conditions(fact = "clinicaloutcome") %>%
  set_expt_batches(fact = "typeofcells")

v2_nb <- normalize_expt(v2_clinical, transform = "log2", convert = "cpm", norm = "quant",
                        filter = TRUE, batch = "svaseq")
plot_pca(v2_nb, plot_labels = FALSE)$plot
```

There _might_ be some variance among the visit 2 samples which are cure/fail-ish.

#### Visit 3 alone

```{r visit3_data}
v3_clinical <- subset_expt(visit_expt, subset="visitnumber=='3'") %>%
  set_expt_conditions(fact = "clinicaloutcome") %>%
  set_expt_batches(fact = "typeofcells")

v3_nb <- normalize_expt(v3_clinical, transform = "log2", convert = "cpm", norm = "quant",
                        filter = TRUE, batch = "svaseq")
plot_pca(v3_nb, plot_labels = FALSE)$plot
```

In a similar fashion, it seems that the visit 3 samples are not
particularly informative.

### Samples separated by cell type

Separate the samples by cell type in order to more easily observe
patterns with respect to visit and clinical outcome.

#### Biopsies

```{r biopsy_separate}
biopsy_norm <- normalize_expt(biopsy_samples, norm = "quant", convert = "cpm",
                              transform = "log2", filter = TRUE)

plot_pca(biopsy_norm, plot_labels = FALSE)$plot
biopsy_nb <- normalize_expt(biopsy_samples, convert = "cpm",
                            transform = "log2", filter = TRUE, batch = "svaseq")
plot_pca(biopsy_nb, plot_labels = FALSE)$plot
```

#### Monocytes

```{r monocyte_separate}
monocyte_norm <- normalize_expt(monocyte_samples, norm = "quant", convert = "cpm",
                                transform = "log2", filter = TRUE)
monocyte_pca <- plot_pca(monocyte_norm, plot_labels = FALSE)
dev <- pp(file="images/monocytes_cf_norm_pca.png")
monocyte_pca$plot
closed <- dev.off()
monocyte_pca$plot

monocyte_disheat <- plot_disheat(monocyte_norm)
dev <- pp(file="images/monocytes_cf_norm_disheat.png")
monocyte_disheat$plot
closed <- dev.off()
monocyte_disheat$plot

monocyte_nb <- normalize_expt(monocyte_samples, convert = "cpm",
                            transform = "log2", filter = TRUE, batch = "svaseq")
monocyte_nb_pca <- plot_pca(monocyte_nb, plot_labels = FALSE)
dev <- pp(file="images/monocytes_cf_norm_sva_pca.png")
monocyte_nb_pca$plot
closed <- dev.off()
monocyte_nb_pca$plot
```

##### Monocytes, visit 1

```{r monocytes_v1}
monocyte_v1 <- subset_expt(monocyte_samples, subset = "visitnumber=='1'")
monocyte_v1_norm <- normalize_expt(monocyte_v1, norm = "quant", convert = "cpm",
                                   transform = "log2", filter = TRUE)
monocyte_v1_pca <- plot_pca(monocyte_v1_norm, plot_labels = FALSE)
dev <- pp(file="images/monocytes_v1_cf_norm_pca.png")
monocyte_v1_pca$plot
closed <- dev.off()
monocyte_v1_pca$plot

monocyte_v1_nb <- normalize_expt(monocyte_v1, convert = "cpm",
                            transform = "log2", filter = TRUE, batch = "svaseq")
monocyte_v1_nb_pca <- plot_pca(monocyte_v1_nb, plot_labels = FALSE)
dev <- pp(file="images/monocytes_v1_cf_norm_sva_pca.png")
monocyte_v1_nb_pca$plot
closed <- dev.off()
monocyte_v1_nb_pca$plot
```


The monocytes, on the other hand, appear to have some real information
lurking in them.

#### Neutrophils

```{r neutrophil_separate}
neutrophil_norm <- normalize_expt(neutrophil_samples, norm = "quant", convert = "cpm",
                                transform = "log2", filter = TRUE)

plot_pca(neutrophil_norm, plot_labels = FALSE)$plot

neutrophil_nb <- normalize_expt(neutrophil_samples, convert = "cpm",
                            transform = "log2", filter = TRUE, batch = "svaseq")
plot_pca(neutrophil_nb, plot_labels = FALSE)$plot
```

This appears also to be the case for the neutrophil samples.

#### Eosinophils

```{r eosinophil_separate}
eosinophil_norm <- normalize_expt(eosinophil_samples, norm = "quant", convert = "cpm",
                                transform = "log2", filter = TRUE)

plot_pca(eosinophil_norm, plot_labels = FALSE)$plot

eosinophil_nb <- normalize_expt(eosinophil_samples, convert = "cpm",
                            transform = "log2", filter = TRUE, batch = "svaseq")
plot_pca(eosinophil_nb, plot_labels = FALSE)$plot
```

We have fewer eosinophil samples currently, but they appear to have
some real differences.

#### Monocytes, Neutrophils, and Eosinophils

# Differential expression analyses

The primary goal is to learn about cure vs. fail.

## Cure/Fail, all samples

Now let us start performing the various differential expression
analyses, starting with the set of all/most clinical samples.

```{r cf_all_de}
cf_clinical_de <- all_pairwise(hs_clinical, model_batch = "svaseq", filter = TRUE)
cf_clinical_tables <- combine_de_tables(
    cf_clinical_de,
    keepers = cf_contrasts,
    excel = glue::glue("excel/cf_clinical_tables-v{ver}.xlsx"))
cf_clinical_sig <- extract_significant_genes(
    cf_clinical_tables,
    excel = glue::glue("excel/cf_clinical_sig-v{ver}.xlsx"))
```

### By cell type

#### Cure/Fail, Biopsies

```{r cf_biopsy_de}
cf_biopsy_de <- all_pairwise(biopsy_samples, model_batch = "svaseq", filter = TRUE)
cf_biopsy_tables <- combine_de_tables(
    cf_biopsy_de,
    keepers = cf_contrasts,
    excel = glue::glue("excel/cf_biopsy_tables-v{ver}.xlsx"))
cf_biopsy_sig <- extract_significant_genes(
    cf_biopsy_tables,
    excel = glue::glue("excel/cf_biopsy_sig-v{ver}.xlsx"))
```

#### Cure/Fail, Monocytes

```{r cf_monocyte_de}
cf_monocyte_sva_de <- all_pairwise(monocyte_samples, model_batch = "svaseq", filter = TRUE)
cf_monocyte_sva_tables <- combine_de_tables(
    cf_monocyte_sva_de,
    keepers = cf_contrasts,
    excel = glue::glue("excel/cf_monocyte_sva_tables-v{ver}.xlsx"))
cf_monocyte_sva_sig <- extract_significant_genes(
    cf_monocyte_sva_tables,
    excel = glue::glue("excel/cf_monocyte_sva_sig-v{ver}.xlsx"))

cf_monocyte_batchvisit_de <- all_pairwise(monocyte_samples, model_batch = TRUE, filter = TRUE)
cf_monocyte_batchvisit_tables <- combine_de_tables(
    cf_monocyte_batchvisit_de,
    keepers = cf_contrasts,
    excel = glue::glue("excel/cf_monocyte_batchvisit_tables-v{ver}.xlsx"))
cf_monocyte_batchvisit_sig <- extract_significant_genes(
    cf_monocyte_batchvisit_tables,
    excel = glue::glue("excel/cf_monocyte_batchvisit_sig-v{ver}.xlsx"))
```

#### Cure/Fail, only the Tumaco samples

```{r cf_tumaco_only}
cf_tumaco_monocyte_sva_de <- all_pairwise(tumaco_monocyte, model_batch = "svaseq", filter = TRUE)
cf_tumaco_monocyte_sva_tables <- combine_de_tables(
    cf_tumaco_monocyte_sva_de,
    keepers = cf_contrasts,
    excel = glue::glue("excel/cf_monocyte_tables_sva_tumaco-v{ver}.xlsx"))
cf_tumaco_monocyte_sva_sig <- extract_significant_genes(
    cf_tumaco_monocyte_sva_tables,
    excel = glue::glue("excel/cf_monocyte_sva_tumaco_sig-v{ver}.xlsx"))

cf_tumaco_monocyte_batchvisit_de <- all_pairwise(tumaco_monocyte, model_batch = TRUE, filter = TRUE)
cf_tumaco_monocyte_batchvisit_tables <- combine_de_tables(
    cf_tumaco_monocyte_batchvisit_de,
    keepers = cf_contrasts,
    excel = glue::glue("excel/cf_monocyte_tables_batchvisit_tumaco-v{ver}.xlsx"))
cf_tumaco_monocyte_batchvisit_sig <- extract_significant_genes(
    cf_tumaco_monocyte_batchvisit_tables,
    excel = glue::glue("excel/cf_monocyte_batchvisit_tumaco_sig-v{ver}.xlsx"))

tumaco_sva_aucc <- calculate_aucc(cf_tumaco_monocyte_sva_tables[["data"]][["fail_vs_cure"]],
                            tbl2=cf_monocyte_sva_tables[["data"]][["fail_vs_cure"]],
                            py="deseq_adjp", ly="deseq_logfc")
tumaco_sva_aucc

shared_ids <- rownames(cf_tumaco_monocyte_sva_tables[["data"]][["fail_vs_cure"]]) %in%
  rownames(cf_monocyte_sva_tables[["data"]][["fail_vs_cure"]])
first <- cf_monocyte_sva_tables[["data"]][["fail_vs_cure"]][shared_ids, ]
second <- cf_tumaco_monocyte_sva_tables[["data"]][["fail_vs_cure"]][rownames(first), ]
cor.test(first[["deseq_logfc"]], second[["deseq_logfc"]])


tumaco_batch_aucc <- calculate_aucc(cf_tumaco_monocyte_batchvisit_tables[["data"]][["fail_vs_cure"]],
                            tbl2=cf_monocyte_batchvisit_tables[["data"]][["fail_vs_cure"]],
                            py="deseq_adjp", ly="deseq_logfc")
tumaco_batch_aucc

shared_ids <- rownames(cf_tumaco_monocyte_batchvisit_tables[["data"]][["fail_vs_cure"]]) %in%
  rownames(cf_monocyte_batchvisit_tables[["data"]][["fail_vs_cure"]])
first <- cf_monocyte_batchvisit_tables[["data"]][["fail_vs_cure"]][shared_ids, ]
second <- cf_tumaco_monocyte_batchvisit_tables[["data"]][["fail_vs_cure"]][rownames(first), ]
cor.test(first[["deseq_logfc"]], second[["deseq_logfc"]])

first_sig_names <- rownames(cf_monocyte_batchvisit_sig[["deseq"]][["ups"]][["fail_vs_cure"]])
second_sig_names <- rownames(cf_tumaco_monocyte_batchvisit_sig[["deseq"]][["ups"]][["fail_vs_cure"]])
batch_venn_lst <- list(all_batch = first_sig_names, tumaco_batch = second_sig_names)
batch_venn <- Vennerable::Venn(batch_venn_lst)
Vennerable::plot(batch_venn)
```

#### Cure/Fail, Neutrophils

```{r cf_neutrophil_de}
cf_neutrophil_de <- all_pairwise(neutrophil_samples, model_batch = "svaseq", filter = TRUE)
cf_neutrophil_tables <- combine_de_tables(
    cf_neutrophil_de,
    keepers = cf_contrasts,
    excel = glue::glue("excel/cf_neutrophil_tables-v{ver}.xlsx"))
cf_neutrophil_sig <- extract_significant_genes(
    cf_neutrophil_tables,
    excel = glue::glue("excel/cf_neutrophil_sig-v{ver}.xlsx"))
```

#### Cure/Fail, Eosinophils

```{r cf_eosinophil_de}
cf_eosinophil_de <- all_pairwise(eosinophil_samples, model_batch = "svaseq", filter = TRUE)
cf_eosinophil_tables <- combine_de_tables(
    cf_eosinophil_de,
    keepers = cf_contrasts,
    excel = glue::glue("excel/cf_eosinophil_tables-v{ver}.xlsx"))
cf_eosinophil_sig <- extract_significant_genes(
    cf_eosinophil_tables,
    excel = glue::glue("excel/cf_eosinophil_sig-v{ver}.xlsx"))
```

#### Cure/Fail clinical (Not biopsies)

```{r cf_nobiopsy_de}
cf_nobiopsy_de <- all_pairwise(hs_clinical_nobiop, model_batch = "svaseq", filter = TRUE)
cf_nobiopsy_tables <- combine_de_tables(
    cf_nobiopsy_de,
    keepers = cf_contrasts,
    excel = glue::glue("excel/cf_nobiopsy_tables-v{ver}.xlsx"))
cf_nobiopsy_sig <- extract_significant_genes(
    cf_nobiopsy_tables,
    excel = glue::glue("excel/cf_nobiopsy_sig-v{ver}.xlsx"))
```

### By visit

For these contrasts, we want to see fail_v1 vs. cure_v1, fail_v2
vs. cure_v2 etc.  As a result, we will need to juggle the data
slightly and add another set of contrasts.

### Setting up

```{r setup_visit_cf_data_contrasts}
visit_cf_expt_factor <- paste0("v", pData(hs_clinical)[["visitnumber"]],
                               pData(hs_clinical)[["condition"]])
visit_cf_expt <- set_expt_conditions(hs_clinical, fact = visit_cf_expt_factor)

visit_cf_eosinophil <- subset_expt(visit_cf_expt, subset="typeofcells=='eosinophils'")
visit_cf_tumaco_eosinophil <- subset_expt(visit_cf_eosinophil, subset="clinic=='Tumaco'")
visit_cf_monocyte <- subset_expt(visit_cf_expt, subset="typeofcells=='monocytes'")
visit_cf_tumaco_monocyte <- subset_expt(visit_cf_monocyte, subset="clinic=='Tumaco'")
visit_cf_neutrophil <- subset_expt(visit_cf_expt, subset="typeofcells=='neutrophils'")
visit_cf_tumaco_neutrophil <- subset_expt(visit_cf_neutrophil, subset="clinic=='Tumaco'")
```

#### Cure/Fail by visits, all cell types

```{r visit_cf_all_de}
visit_cf_all_de <- all_pairwise(visit_cf_expt, model_batch = "svaseq", filter = TRUE)
visit_cf_all_tables <- combine_de_tables(
    visit_cf_all_de,
    keepers = visit_cf_contrasts,
    excel = glue::glue("excel/visit_cf_all_tables-v{ver}.xlsx"))
visit_cf_all_sig <- extract_significant_genes(
    visit_cf_all_tables,
    excel = glue::glue("excel/visit_cf_all_sig-v{ver}.xlsx"))
```

#### Cure/Fail by visit, Monocytes

```{r visit_cf_monocyte_de}
visit_cf_monocyte_de <- all_pairwise(visit_cf_monocyte, model_batch = "svaseq", filter = TRUE)
visit_cf_monocyte_tables <- combine_de_tables(
    visit_cf_monocyte_de,
    keepers = visit_cf_contrasts,
    excel = glue::glue("excel/visit_cf_monocyte_tables-v{ver}.xlsx"))
visit_cf_monocyte_sig <- extract_significant_genes(
    visit_cf_monocyte_tables,
    excel = glue::glue("excel/visit_cf_monocyte_sig-v{ver}.xlsx"))

v1fc_deseq_ma <- visit_cf_monocyte_tables[["plots"]][["v1fail_vs_cure"]][["deseq_ma_plots"]][["plot"]]
dev <- pp(file="images/monocyte_cf_de_v1_maplot.png")
v1fc_deseq_ma
closed <- dev.off()
v1fc_deseq_ma

v2fc_deseq_ma <- visit_cf_monocyte_tables[["plots"]][["v2fail_vs_cure"]][["deseq_ma_plots"]][["plot"]]
dev <- pp(file="images/monocyte_cf_de_v2_maplot.png")
v2fc_deseq_ma
closed <- dev.off()
v2fc_deseq_ma

v3fc_deseq_ma <- visit_cf_monocyte_tables[["plots"]][["v3fail_vs_cure"]][["deseq_ma_plots"]][["plot"]]
dev <- pp(file="images/monocyte_cf_de_v3_maplot.png")
v3fc_deseq_ma
closed <- dev.off()
v3fc_deseq_ma

## Repeat for the tumaco subset
visit_cf_tumaco_monocyte_de <- all_pairwise(visit_cf_tumaco_monocyte,
                                          model_batch = "svaseq", filter = TRUE)
visit_cf_tumaco_monocyte_tables <- combine_de_tables(
    visit_cf_tumaco_monocyte_de,
    keepers = visit_cf_contrasts,
    excel = glue::glue("excel/visit_cf_tumaco_monocyte_tables-v{ver}.xlsx"))
visit_cf_tumaco_monocyte_sig <- extract_significant_genes(
    visit_cf_tumaco_monocyte_tables,
    excel = glue::glue("excel/visit_cf_tumaco_monocyte_sig-v{ver}.xlsx"))
```

One query from Alejandro is to look at the genes shared up/down across
visits.  I am not entirely certain we have enough samples for this to
work, but let us find out.

I am thinking this is a good place to use the AUCC curves I learned
about thanks to Julie Cridland.

Note that the following is all monocyte samples, this should therefore
potentially be moved up and a version of this with only the Tumaco
samples put here?

```{r monocyte_shared_de_genes}
v1fc <- visit_cf_monocyte_tables[["data"]][["v1fail_vs_cure"]]
v2fc <- visit_cf_monocyte_tables[["data"]][["v2fail_vs_cure"]]
v3fc <- visit_cf_monocyte_tables[["data"]][["v3fail_vs_cure"]]

v1_sig <- c(
    rownames(visit_cf_monocyte_sig[["deseq"]][["ups"]][["v1fail_vs_cure"]]),
    rownames(visit_cf_monocyte_sig[["deseq"]][["downs"]][["v1fail_vs_cure"]]))
v2_sig <- c(
    rownames(visit_cf_monocyte_sig[["deseq"]][["ups"]][["v2fail_vs_cure"]]),
    rownames(visit_cf_monocyte_sig[["deseq"]][["downs"]][["v2fail_vs_cure"]]))
v3_sig <- c(
    rownames(visit_cf_monocyte_sig[["deseq"]][["ups"]][["v2fail_vs_cure"]]),
    rownames(visit_cf_monocyte_sig[["deseq"]][["downs"]][["v2fail_vs_cure"]]))

monocyte_visit_aucc_v2v1 <- calculate_aucc(v1fc, tbl2=v2fc, py="deseq_adjp", ly="deseq_logfc")
dev <- pp(file="images/monocyte_visit_v2v1_aucc.png")
monocyte_visit_aucc_v2v1[["plot"]]
closed <- dev.off()
monocyte_visit_aucc_v2v1[["plot"]]

monocyte_visit_aucc_v3v1 <- calculate_aucc(v1fc, tbl2=v3fc, py="deseq_adjp", ly="deseq_logfc")
dev <- pp(file="images/monocyte_visit_v3v1_aucc.png")
monocyte_visit_aucc_v3v1[["plot"]]
closed <- dev.off()
monocyte_visit_aucc_v3v1[["plot"]]
```

#### Repeat for only the Tumaco samples

```{r aucc_upset_tumaco}
v1fc_tumaco <- visit_cf_tumaco_monocyte_tables[["data"]][["v1fail_vs_cure"]]
v2fc_tumaco <- visit_cf_tumaco_monocyte_tables[["data"]][["v2fail_vs_cure"]]
v3fc_tumaco <- visit_cf_tumaco_monocyte_tables[["data"]][["v3fail_vs_cure"]]

v1_tumaco_sig <- c(
    rownames(visit_cf_tumaco_monocyte_sig[["deseq"]][["ups"]][["v1fail_vs_cure"]]),
    rownames(visit_cf_tumaco_monocyte_sig[["deseq"]][["downs"]][["v1fail_vs_cure"]]))
v2_tumaco_sig <- c(
    rownames(visit_cf_tumaco_monocyte_sig[["deseq"]][["ups"]][["v2fail_vs_cure"]]),
    rownames(visit_cf_tumaco_monocyte_sig[["deseq"]][["downs"]][["v2fail_vs_cure"]]))
v3_tumaco_sig <- c(
    rownames(visit_cf_tumaco_monocyte_sig[["deseq"]][["ups"]][["v2fail_vs_cure"]]),
    rownames(visit_cf_tumaco_monocyte_sig[["deseq"]][["downs"]][["v2fail_vs_cure"]]))

monocyte_tumaco_visit_aucc_v2v1 <- calculate_aucc(v1fc_tumaco, tbl2=v2fc_tumaco,
                                                  py="deseq_adjp", ly="deseq_logfc")
dev <- pp(file="images/monocyte_visit_tumaco_v2v1_aucc.png")
monocyte_tumaco_visit_aucc_v2v1[["plot"]]
closed <- dev.off()
monocyte_tumaco_visit_aucc_v2v1[["plot"]]

monocyte_tumaco_visit_aucc_v3v1 <- calculate_aucc(v1fc_tumaco, tbl2=v3fc_tumaco,
                                                  py="deseq_adjp", ly="deseq_logfc")
dev <- pp(file="images/monocyte_visit_tumaco_v3v1_aucc.png")
monocyte_tumaco_visit_aucc_v3v1[["plot"]]
closed <- dev.off()
monocyte_tumaco_visit_aucc_v3v1[["plot"]]

monocyte_tumaco_visit_aucc_v3v2 <- calculate_aucc(v3fc_tumaco, tbl2=v2fc_tumaco,
                                                  py="deseq_adjp", ly="deseq_logfc")
dev <- pp(file="images/monocyte_visit_tumaco_v3v2_aucc.png")
monocyte_tumaco_visit_aucc_v3v2[["plot"]]
closed <- dev.off()
monocyte_tumaco_visit_aucc_v3v2[["plot"]]
```

#### Cure/Fail by visit, Neutrophils
a
```{r visit_cf_neutrophil_de}
visit_cf_neutrophil_de <- all_pairwise(visit_cf_neutrophil, model_batch = "svaseq", filter = TRUE)
visit_cf_neutrophil_tables <- combine_de_tables(
    visit_cf_neutrophil_de,
    keepers = visit_cf_contrasts,
    excel = glue::glue("excel/visit_cf_neutrophil_tables-v{ver}.xlsx"))
visit_cf_neutrophil_sig <- extract_significant_genes(
    visit_cf_neutrophil_tables,
    excel = glue::glue("excel/visit_cf_neutrophil_sig-v{ver}.xlsx"))
```

#### Cure/Fail by visit, Eosinophils

```{r visit_cf_eosinophil_de}
visit_cf_eosinophil_de <- all_pairwise(visit_cf_eosinophil, model_batch = "svaseq", filter = TRUE)
visit_cf_eosinophil_tables <- combine_de_tables(
    visit_cf_eosinophil_de,
    keepers = visit_cf_contrasts,
    excel = glue::glue("excel/visit_cf_eosinophil_tables-v{ver}.xlsx"))
visit_cf_eosinophil_sig <- extract_significant_genes(
    visit_cf_eosinophil_tables,
    excel = glue::glue("excel/visit_cf_eosinophil_sig-v{ver}.xlsx"))
```

## Persistence in visit 3

Having put some SL read mapping information in the sample sheet, Maria
Adelaida added a new column using it with the putative persistence
state on a per-sample basis.  One question which arised from that:
what differences are observable between the persistent yes vs. no
samples on a per-cell-type basis among the visit 3 samples.

### Setting up

First things first, create the datasets.

```{r persistence_setup}
persistence_expt <- subset_expt(hs_clinical, subset = "persistence=='Y'|persistence=='N'") %>%
  subset_expt(subset = 'visitnumber==3') %>%
  set_expt_conditions(fact = 'persistence')

## persistence_biopsy <- subset_expt(persistence_expt, subset = "typeofcells=='biopsy'")
persistence_monocyte <- subset_expt(persistence_expt, subset = "typeofcells=='monocytes'")
persistence_neutrophil <- subset_expt(persistence_expt, subset = "typeofcells=='neutrophils'")
persistence_eosinophil <- subset_expt(persistence_expt, subset = "typeofcells=='eosinophils'")
```

### Take a look

See if there are any patterns which look usable.

```{r persistence_plot}
## All
persistence_norm <- normalize_expt(persistence_expt, transform = "log2", convert = "cpm",
                                   norm = "quant", filter = TRUE)
plot_pca(persistence_norm)$plot
persistence_nb <- normalize_expt(persistence_expt, transform = "log2", convert = "cpm",
                                 batch = "svaseq", filter = TRUE)
plot_pca(persistence_nb)$plot

## Biopsies
##persistence_biopsy_norm <- normalize_expt(persistence_biopsy, transform = "log2", convert = "cpm",
##                                   norm = "quant", filter = TRUE)
##plot_pca(persistence_biopsy_norm)$plot
## Insufficient data

## Monocytes
persistence_monocyte_norm <- normalize_expt(persistence_monocyte, transform = "log2", convert = "cpm",
                                   norm = "quant", filter = TRUE)
plot_pca(persistence_monocyte_norm)$plot
persistence_monocyte_nb <- normalize_expt(persistence_monocyte, transform = "log2", convert = "cpm",
                                 batch = "svaseq", filter = TRUE)
plot_pca(persistence_monocyte_nb)$plot

## Neutrophils
persistence_neutrophil_norm <- normalize_expt(persistence_neutrophil, transform = "log2", convert = "cpm",
                                   norm = "quant", filter = TRUE)
plot_pca(persistence_neutrophil_norm)$plot
persistence_neutrophil_nb <- normalize_expt(persistence_neutrophil, transform = "log2", convert = "cpm",
                                 batch = "svaseq", filter = TRUE)
plot_pca(persistence_neutrophil_nb)$plot

## Eosinophils
persistence_eosinophil_norm <- normalize_expt(persistence_eosinophil, transform = "log2", convert = "cpm",
                                   norm = "quant", filter = TRUE)
plot_pca(persistence_eosinophil_norm)$plot
persistence_eosinophil_nb <- normalize_expt(persistence_eosinophil, transform = "log2", convert = "cpm",
                                 batch = "svaseq", filter = TRUE)
plot_pca(persistence_eosinophil_nb)$plot
```

### persistence DE

```{r persistence_de}
persistence_de <- all_pairwise(persistence_expt, filter = TRUE, model_batch = "svaseq")
persistence_table <- combine_de_tables(
    persistence_de,
    excel = glue::glue("excel/persistence_all_de-v{ver}.xlsx"))
persistence_monocyte_de <- all_pairwise(persistence_monocyte, filter = TRUE, model_batch = "svaseq")
persistence_monocyte_table <- combine_de_tables(
    persistence_monocyte_de,
    excel = glue::glue("excel/persistence_monocyte_de-v{ver}.xlsx"))
persistence_neutrophil_de <- all_pairwise(persistence_neutrophil, filter = TRUE, model_batch = "svaseq")
persistence_neutrophil_table <- combine_de_tables(
    persistence_neutrophil_de,
    excel = glue::glue("excel/persistence_neutrophil_de-v{ver}.xlsx"))
persistence_eosinophil_de <- all_pairwise(persistence_eosinophil, filter = TRUE, model_batch = "svaseq")
persistence_eosinophil_table <- combine_de_tables(
    persistence_eosinophil_de,
    excel = glue::glue("excel/persistence_eosinophil_de-v{ver}.xlsx"))
```

## Comparing visits without regard to cure/fail

### All cell types

```{r de_cf_visit_all}
visit_all_de <- all_pairwise(visit_expt, filter = TRUE, model_batch = "svaseq")
visit_all_table <- combine_de_tables(
    visit_all_de,
    keepers = visit_contrasts,
    excel = glue::glue("excel/visit_all_de-v{ver}.xlsx"))
visit_all_sig <- extract_significant_genes(
    visit_all_table,
    excel = glue::glue("excel/visit_all_sig-v{ver}.xlsx"))
visit_all_saved <- save(list = "visit_all_table",
                        file = glue::glue("rda/visit_all_table-v{ver}.rda"))
```

### Monocyte samples

```{r de_cf_visit_monocyte}
visit_monocyte_de <- all_pairwise(visit_monocyte, filter = TRUE, model_batch = "svaseq")
visit_monocyte_table <- combine_de_tables(
    visit_monocyte_de,
    keepers = visit_contrasts,
    excel = glue::glue("excel/visit_monocyte_de-v{ver}.xlsx"))
visit_monocyte_sig <- extract_significant_genes(
    visit_monocyte_table,
    excel = glue::glue("excel/visit_monocyte_sig-v{ver}.xlsx"))
```

### Neutrophil samples

```{r de_cf_visit_neutrophil}
visit_neutrophil_de <- all_pairwise(visit_neutrophil, filter = TRUE, model_batch = "svaseq")
visit_neutrophil_table <- combine_de_tables(
    visit_neutrophil_de,
    keepers = visit_contrasts,
    excel = glue::glue("excel/visit_neutrophil_de-v{ver}.xlsx"))
visit_neutrophil_sig <- extract_significant_genes(
    visit_neutrophil_table,
    excel = glue::glue("excel/visit_neutrophil_sig-v{ver}.xlsx"))
visit_neutrophil_saved <- save(list = "visit_neutrophil_table",
                               file = glue::glue("rda/visit_neutrophil_table-v{ver}.rda"))
```

### Eosinophil samples

```{r de_cf_visit_eosinophil}
visit_eosinophil_de <- all_pairwise(visit_eosinophil, filter = TRUE, model_batch = "svaseq")
visit_eosinophil_table <- combine_de_tables(
    visit_eosinophil_de,
    keepers = visit_contrasts,
    excel = glue::glue("excel/visit_eosinophil_de-v{ver}.xlsx"))
visit_eosinophil_sig <- extract_significant_genes(
    visit_eosinophil_table,
    excel = glue::glue("excel/visit_eosinophil_sig-v{ver}.xlsx"))
visit_eosinophil_saved <- save(list = "visit_eosinophil_table",
                               file = glue::glue("rda/visit_eosinophil_table-v{ver}.rda"))
```

# Compare the two clinics directly

```{r clinic_comparison2}
monocytes_by_place <- set_expt_conditions(monocyte_samples, fact="clinic")
eosinophils_by_place <- set_expt_conditions(eosinophil_samples, fact="clinic")
neutrophils_by_place <- set_expt_conditions(neutrophil_samples, fact="clinic")
biopsies_by_place <- set_expt_conditions(biopsy_samples, fact="clinic")

monocyte_place_de <- all_pairwise(monocytes_by_place, model_batch=TRUE)
monocyte_table <- combine_de_tables(
    monocyte_place_de,
    excel="excel/monocytes_by_place-table.xlsx")
monocyte_sig <- extract_significant_genes(
    monocyte_table,
    excel="excel/monocytes_by_place-sig.xlsx")
eosinophil_place_de <- all_pairwise(eosinophils_by_place, model_batch=TRUE)
eosinophil_table <- combine_de_tables(
    eosinophil_place_de,
    excel="excel/eosinophils_by_place-table.xlsx")
eosinophil_sig <- extract_significant_genes(
    eosinophil_table,
    excel="excel/eosinophils_by_place-sig.xlsx")
neutrophil_place_de <- all_pairwise(neutrophils_by_place, model_batch=TRUE)
neutrophil_table <- combine_de_tables(
    neutrophil_place_de,
    excel="excel/neutrophils_by_place-table.xlsx")
neutrophil_sig <- extract_significant_genes(
    neutrophil_table,
    excel="excel/neutrophils_by_place-sig.xlsx")
biopsy_place_de <- all_pairwise(biopsies_by_place, model_batch=TRUE)
biopsy_table <- combine_de_tables(
    biopsy_place_de,
    excel="biopsies_by_place-table.xlsx")
biopsy_sig <- extract_significant_genes(
    biopsy_table,
    excel="biopsies_by_place-sig.xlsx")
```

# Likelihood Ratio Testing (LRT)

## Shared patterns across visits

```{r lrt_visit}
clinical_filt <- normalize_expt(hs_clinical, filter = TRUE)
lrt_visit <- deseq_lrt(clinical_filt, transform = "vst", interaction = FALSE,
                       interactor_column = "visitnumber",
                       interest_column = "clinicaloutcome")
summary(lrt_visit[["favorite_genes"]])
written <- write_xlsx(data=as.data.frame(lrt_visit[["deseq_table"]]),
                      excel=glue::glue("excel/lrt_clinical_visit-v{ver}.xlsx"))

lrt_monocyte_visit <- deseq_lrt(visit_monocyte, transform = "vst",
                                interaction = FALSE,
                                interactor_column = "visitnumber",
                                interest_column = "clinicaloutcome")
lrt_monocyte_visit$cluster_data$plot

lrt_tumaco_monocyte_visit <- deseq_lrt(tumaco_monocyte, transform = "vst",
                                       interaction = FALSE, minc = 1,
                                       interactor_column = "visitnumber",
                                       interest_column = "clinicaloutcome")
```

## Shared patterns across cell types

```{r lrt_celltype}
lrt_celltype_clinical_test <- deseq_lrt(hs_clinical, transform = "vst",
                                        interactor_column = "typeofcells",
                                        interest_column = "clinicaloutcome")

deseq_lrt_df <- merge(hs_annot, as.data.frame(lrt_celltype_clinical_test[["deseq_table"]]), all.y=TRUE,
                      by="row.names")
rownames(deseq_lrt_df) <- deseq_lrt_df[["Row.names"]]
deseq_lrt_df[["Row.names"]] <- NULL
written <- write_xlsx(data=deseq_lrt_df,
                      excel=glue::glue("excel/lrt_clinical_celltype-v{ver}.xlsx"))
```

# Gene Set Analyses

The gene sets we are most likely to consider are the results of the
various preceding differential expression analyses.

## GSEA

### Cure/Fail groups

In the context of cure vs. fail, we have mixed and matched the data in
quite a few different ways.

#### Cure/Fail, all samples

For the moment, let us assume that the default definition of
'significant' is sufficient for these analyses, with the knowledge
that is not likely to remain true.

The resulting data structures are organized as:

thing[["deseq"]][[ups"]][[contrast]]

In addition, we have a few ontology-esque tools available, so let us
mix and match and see what pops out.

```{r gsea_cf_all}
cf_all_up <- cf_clinical_sig[["deseq"]][["ups"]][["fail_vs_cure"]]
dim(cf_all_up)
cf_all_down <- cf_clinical_sig[["deseq"]][["downs"]][["fail_vs_cure"]]
dim(cf_all_down)

cf_all_up_gp <- simple_gprofiler(cf_all_up)
cf_all_up_gp[["pvalue_plots"]][["reactome_plot_over"]]
cf_all_up_gp[["pvalue_plots"]][["kegg_plot_over"]]
cf_all_up_gp[["pvalue_plots"]][["mfp_plot_over"]]
cf_all_up_gp[["pvalue_plots"]][["bpp_plot_over"]]

cf_all_down_gp <- simple_gprofiler(cf_all_down)
cf_all_down_gp[["pvalue_plots"]][["reactome_plot_over"]]
cf_all_down_gp[["pvalue_plots"]][["kegg_plot_over"]]
cf_all_down_gp[["pvalue_plots"]][["mfp_plot_over"]]
cf_all_down_gp[["pvalue_plots"]][["bpp_plot_over"]]
```

#### Cure/Fail, Biopsies

There are no genes deemed significant in the biopsy comparisons of
cure/fail.  Oddly, when I looked manually, I thought I saw one
candidate gene.  In addition, when I stepped through the function
manually I got the same gene...

#### Cure/Fail, Monocytes

Try again with monocytes.

```{r gsea_cf_monocyte}
cf_monocyte_up <- cf_monocyte_sva_sig[["deseq"]][["ups"]][["fail_vs_cure"]]
dim(cf_monocyte_up)
cf_monocyte_down <- cf_monocyte_sva_sig[["deseq"]][["downs"]][["fail_vs_cure"]]
dim(cf_monocyte_down)

cf_monocyte_up_gp <- simple_gprofiler(cf_monocyte_up)
cf_monocyte_up_gp[["pvalue_plots"]][["reactome_plot_over"]]
cf_monocyte_up_gp[["pvalue_plots"]][["kegg_plot_over"]]
cf_monocyte_up_gp[["pvalue_plots"]][["bpp_plot_over"]]

cf_monocyte_down_gp <- simple_gprofiler(cf_monocyte_down)
cf_monocyte_down_gp[["pvalue_plots"]][["reactome_plot_over"]]
cf_monocyte_down_gp[["pvalue_plots"]][["kegg_plot_over"]]
```

#### Cure/Fail, Neutrophils

```{r gsea_cf_neutrophil}
cf_neutrophil_up <- cf_neutrophil_sig[["deseq"]][["ups"]][["fail_vs_cure"]]
dim(cf_neutrophil_up)
cf_neutrophil_down <- cf_neutrophil_sig[["deseq"]][["downs"]][["fail_vs_cure"]]
dim(cf_neutrophil_down)

cf_neutrophil_up_gp <- simple_gprofiler(cf_neutrophil_up)

cf_neutrophil_down_gp <- simple_gprofiler(cf_neutrophil_down)
cf_neutrophil_down_gp[["pvalue_plots"]][["kegg_plot_over"]]
```

#### Cure/Fail, Eosinophils

```{r gsea_cf_eosinophil}
cf_eosinophil_up <- cf_eosinophil_sig[["deseq"]][["ups"]][["fail_vs_cure"]]
dim(cf_eosinophil_up)
cf_eosinophil_down <- cf_eosinophil_sig[["deseq"]][["downs"]][["fail_vs_cure"]]
dim(cf_eosinophil_down)

cf_eosinophil_up_gp <- simple_gprofiler(cf_eosinophil_up)
cf_eosinophil_up_gp[["pvalue_plots"]][["kegg_plot_over"]]
cf_eosinophil_up_gp[["pvalue_plots"]][["reactome_plot_over"]]
cf_eosinophil_up_gp[["pvalue_plots"]][["mfp_plot_over"]]
cf_eosinophil_up_gp[["pvalue_plots"]][["bpp_plot_over"]]

cf_eosinophil_down_gp <- simple_gprofiler(cf_eosinophil_down)
cf_eosinophil_down_gp[["pvalue_plots"]][["reactome_plot_over"]]
cf_eosinophil_down_gp[["pvalue_plots"]][["mfp_plot_over"]]
cf_eosinophil_down_gp[["pvalue_plots"]][["bpp_plot_over"]]
```

#### Clinical samples

```{r gsea_cf_clinical}
cf_nobiopsy_up <- cf_nobiopsy_sig[["deseq"]][["ups"]][["fail_vs_cure"]]
dim(cf_nobiopsy_up)
cf_nobiopsy_down <- cf_nobiopsy_sig[["deseq"]][["downs"]][["fail_vs_cure"]]
dim(cf_nobiopsy_down)

cf_nobiopsy_up_gp <- simple_gprofiler(cf_nobiopsy_up)
cf_nobiopsy_up_gp[["pvalue_plots"]][["kegg_plot_over"]]
cf_nobiopsy_up_gp[["pvalue_plots"]][["reactome_plot_over"]]
cf_nobiopsy_up_gp[["pvalue_plots"]][["mfp_plot_over"]]
cf_nobiopsy_up_gp[["pvalue_plots"]][["bpp_plot_over"]]

cf_nobiopsy_down_gp <- simple_gprofiler(cf_nobiopsy_down)
cf_nobiopsy_down_gp[["pvalue_plots"]][["reactome_plot_over"]]
cf_nobiopsy_down_gp[["pvalue_plots"]][["mfp_plot_over"]]
cf_nobiopsy_down_gp[["pvalue_plots"]][["bpp_plot_over"]]
```

### Cell type groups

### Visit groups

## GSVA

### Clinical samples

```{r gsva, fig.show="hide"}
hs_celltype_gsva_c2 <- simple_gsva(hs_valid)
hs_celltype_gsva_c2_sig <- get_sig_gsva_categories(
    hs_celltype_gsva_c2,
    excel="excel/individual_celltypes_gsva_c2.xlsx")
hs_celltype_gsva_c2_sig$subset_plot
hs_celltype_gsva_c2_sig$score_plot

broad_c7 <- load_gmt_signatures(signatures="reference/msigdb/c7.all.v7.2.entrez.gmt",
                                signature_category="c7")
hs_celltype_gsva_c7 <- simple_gsva(hs_valid,
                                   signatures="reference/msigdb/c7.all.v7.2.entrez.gmt",
                                   signature_category="c7",
                                   msig_xml="reference/msigdb_v7.2.xml",
                                   cores=10)
hs_celltype_gsva_c7_sig <- get_sig_gsva_categories(
    hs_celltype_gsva_c7,
    excel="excel/individual_celltypes_gsva_c7.xlsx")
```

### Print some plots of the GSVA outputs

```{r gsva_plots}
## The raw heatmap of the C2 values
hs_celltype_gsva_c2_sig[["raw_plot"]]
## The 'significance' scores of the C2 values
hs_celltype_gsva_c2_sig[["score_plot"]]
## The subset of scores for categories deemed significantly different.
hs_celltype_gsva_c2_sig[["subset_plot"]]

## The raw heatmap of the C7 values
hs_celltype_gsva_c7_sig[["raw_plot"]]
## The 'significance' scores of the C7 values
hs_celltype_gsva_c7_sig[["score_plot"]]
## The subset of scores for categories deemed significantly different.
hs_celltype_gsva_c7_sig[["subset_plot"]]
```

# Concordance

Let us compare various results to see how well they agreed

```{r concordance}
monocyte_cor_subset <- cf_monocyte_sva_tables[["data"]][["fail_vs_cure"]][, c("deseq_logfc", "deseq_adjp")]
neutrophil_cor_subset <- cf_neutrophil_tables[["data"]][["fail_vs_cure"]][, c("deseq_logfc", "deseq_adjp")]
eosinophil_cor_subset <- cf_eosinophil_tables[["data"]][["fail_vs_cure"]][, c("deseq_logfc", "deseq_adjp")]
mono_neut_cor <- merge(monocyte_cor_subset, neutrophil_cor_subset, by="row.names")
mono_eo_cor <- merge(monocyte_cor_subset, eosinophil_cor_subset, by="row.names")
neut_eo_cor <- merge(neutrophil_cor_subset, eosinophil_cor_subset, by="row.names")

cor.test(mono_neut_cor[["deseq_logfc.x"]], mono_neut_cor[["deseq_logfc.y"]])
monocyte_neutrophil_aucc <- calculate_aucc(cf_monocyte_sva_tables[["data"]][["fail_vs_cure"]],
                                           cf_neutrophil_tables[["data"]][["fail_vs_cure"]],
                                           px="deseq_p", py="deseq_p", ly="deseq_logfc", lx="deseq_logfc")
monocyte_neutrophil_aucc$plot

cor.test(mono_eo_cor[["deseq_logfc.x"]], mono_eo_cor[["deseq_logfc.y"]])
monocyte_eosinophil_aucc <- calculate_aucc(cf_monocyte_sva_tables[["data"]][["fail_vs_cure"]],
                                           cf_eosinophil_tables[["data"]][["fail_vs_cure"]],
                                           px="deseq_p", py="deseq_p", ly="deseq_logfc", lx="deseq_logfc")
monocyte_eosinophil_aucc$plot

cor.test(neut_eo_cor[["deseq_logfc.x"]], neut_eo_cor[["deseq_logfc.y"]])
neutrophil_eosinophil_aucc <- calculate_aucc(cf_neutrophil_tables[["data"]][["fail_vs_cure"]],
                                           cf_eosinophil_tables[["data"]][["fail_vs_cure"]],
                                           px="deseq_p", py="deseq_p", ly="deseq_logfc", lx="deseq_logfc")
neutrophil_eosinophil_aucc$plot
```

# Classify me!

I wrote out all the z2.2 and z2.3 specific variants to a couple files,
I want to see if I can classify a human sample as infected with 2.2 or
2.3.

```{r classifyme, eval=FALSE}
z22 <- read.csv("csv/variants_22.csv")
z23 <- read.csv("csv/variants_23.csv")
cure <- read.csv("csv/cure_variants.txt")
fail <- read.csv("csv/fail_variants.txt")
z22_vec <- gsub(pattern="\\-", replacement="_", x=z22[["x"]])
z23_vec <- gsub(pattern="\\-", replacement="_", x=z23[["x"]])
cure_vec <- gsub(pattern="\\-", replacement="_", x=cure)
fail_vec <- gsub(pattern="\\-", replacement="_", x=fail)

classify_zymo <- function(sample) {
  arbitrary_tags <- sm(readr::read_tsv(sample))
  arbitrary_ids <- arbitrary_tags[["position"]]
  message("Length: ", length(arbitrary_ids), ", z22: ",
          sum(arbitrary_ids %in% z22_vec) / (length(z22_vec)), " z23: ",
          sum(arbitrary_ids %in% z23_vec) / (length(z23_vec)))
}

arbitrary_sample <- "preprocessing/TMRC30156/outputs/40freebayes_lpanamensis_v36/all_tags.txt.xz"
classify_zymo(arbitrary_sample)
```

```{r saveme, eval=FALSE}
if (!isTRUE(get0("skip_load"))) {
  pander::pander(sessionInfo())
  message(paste0("This is hpgltools commit: ", get_git_commit()))
  message(paste0("Saving to ", savefile))
  tmp <- sm(saveme(filename=savefile))
}
```

```{r loadme_after, eval=FALSE}
tmp <- loadme(filename=savefile)
```
