1 Annotation
We take the annotation data from ensembl’s biomart instance. The genome which was used to map the data was hg38 revision 100. My default when using biomart is to load the data from 1 year before the current date.
hs_annot <- sm(load_biomart_annotations(year = "2020"))
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:227921 Length:227921 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.:16.0 3rd Qu.: 5.00
## Max. :29.0 Max. :17.00
##
## hgnc_symbol description gene_biotype cds_length
## Length:227921 Length:227921 Length:227921 Min. : 3
## Class :character Class :character Class :character 1st Qu.: 357
## Mode :character Mode :character Mode :character Median : 694
## Mean : 1139
## 3rd Qu.: 1446
## Max. :107976
## NA's :127343
## chromosome_name strand start_position end_position
## Length:227921 Length:227921 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:227921
## Class :character
## Mode :character
##
##
##
## hs_go <- sm(load_biomart_go()[["go"]])
hs_length <- hs_annot[, c("ensembl_gene_id", "cds_length")]
colnames(hs_length) <- c("ID", "length")2 Introduction
This document is intended to provide an overview of TMRC3 samples which have been sequenced. It includes some plots and analyses showing the relationships among the samples as well as some differential analyses when possible.
3 Sample Estimation
3.1 Generate expressionsets
The sample sheet is copied from our shared online sheet and updated with each release of sequencing data.
samplesheet <- "sample_sheets/tmrc3_samples_20210601.xlsx"3.1.1 Hisat2 expressionsets
The first thing to note is the large range in coverage. There are multiple samples with coverage which is too low to use. These will be removed shortly.
In the following block I immediately exclude any non-coding reads as well.
## Create the expressionset and immediately pass it to a filter
## removing the non protein coding genes.
sanitize_columns <- c("visitnumber", "clinicaloutcome", "donor",
"typeofcells", "clinicalpresentation",
"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")## Reading the sample metadata.## Dropped 71 rows from the sample metadata because they were blank.## The sample definitions comprises: 173 rows(samples) and 75 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 21452 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 rows and 138 columns.## Before removal, there were 21481 genes, now there are 19941.## There are 13 samples which kept less than 90 percent counts.## TMRC30015 TMRC30017 TMRC30019 TMRC30044 TMRC30045 TMRC30097 TMRC30075 TMRC30087
## 79.24 85.72 89.75 80.34 73.33 89.90 86.97 83.63
## TMRC30101 TMRC30104 TMRC30114 TMRC30131 TMRC30073
## 88.41 80.29 87.62 86.82 89.26levels(pData(hs_expt[["expressionset"]])[["visitnumber"]]) <- list(
'0' = "notapplicable", '1' = 1, '2' = 2, '3' = 3)Split this data into CDS and lncRNA. Oh crap in order to do that I need to recount the data. Running now (20210518)
## lnc_expt <- create_expt(samplesheet,
## file_column = "hg38100lncfile",
## gene_info = hs_annot)3.1.1.1 Initial metrics
Once the data was loaded, there are a couple of metrics which may be plotted immediately.
nonzero <- plot_nonzero(hs_expt)
nonzero$plot## Warning: ggrepel: 111 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ncrna_lost_df <- as.data.frame(pData(hs_expt)[["ncrna_lost"]])
rownames(ncrna_lost_df) <- rownames(pData(hs_expt))
colnames(ncrna_lost_df) <- "ncrna_lost"
tmpdf <- merge(nonzero$table, ncrna_lost_df, by = "row.names")
rownames(tmpdf) <- tmpdf[["Row.names"]]
tmpdf[["Row.names"]] <- NULL
ggplot(tmpdf, aes(x=ncrna_lost, y=nonzero_genes)) +
ggplot2::geom_point() +
ggplot2::ggtitle("Nonzero genes with respect to percent counts
lost when ncRNA was removed.")
Najib doesn’t want this plot, but I am using it to check new samples, so will hide it from general use.
libsize <- plot_libsize(hs_expt)
libsize$plot
3.2 Minimum coverage sample filtering
I arbitrarily chose 11,000 non-zero genes as a minimum. We may want this to be higher.
hs_valid <- subset_expt(hs_expt, nonzero = 11000)## The samples (and read coverage) removed when filtering 11000 non-zero genes are:## TMRC30010 TMRC30050 TMRC30052
## 52471 808149 3087347## subset_expt(): There were 138, now there are 135 samples.valid_write <- sm(write_expt(hs_valid, excel = glue("excel/hs_valid-v{ver}.xlsx")))4 Project Aims
The project seeks to determine the relationship of the innate immune response and inflammatory signaling to the clinical outcome of antileishmanial drug treatment. We will test the hypothesis that the profile of innate immune cell activation and their dynamics through the course of treatment differ between CL patients with prospectively determined therapeutic cure or failure.
This will be achieved through the characterization of the in vivo dynamics of blood-derived monocyte, neutrophil and eosinophil transcriptome before, during and at the end of treatment in CL patients. Cell-type specific transcriptomes, composite signatures and time-response expression profiles will be contrasted among patients with therapeutic cure or failure.
4.1 Preparation
To address these, I added to the end of the sample sheet columns named ‘condition’, ‘batch’, ‘donor’, and ‘time’. These are filled in with shorthand values according to the above.
4.2 Global view
Before addressing the questions explicitly by subsetting the data, I want to get a look at the samples as they are.
new_names <- pData(hs_valid)[["samplename"]]
hs_valid <- hs_valid %>%
set_expt_batches(fact = "cellssource") %>%
set_expt_conditions(fact = "typeofcells") %>%
set_expt_samplenames(newnames = new_names)
all_norm <- sm(normalize_expt(hs_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")
pp(file = glue("images/tmrc3_pca_nolabels-v{ver}.png"), image = all_pca$plot)
write.csv(all_pca$table, file = "coords/hs_donor_pca_coords.csv")
plot_corheat(all_norm, plot_title = "Heirarchical clustering:
cell types")$plot
4.3 Examine samples relevant to clinical outcome
Now let us consider only the samples for which we have a clinical outcome. These fall primarily into either ‘cured’ or ‘failed’, but some people have not yet returned to the clinic after the first or second visit. These are deemed ‘lost’.
hs_clinical <- hs_valid %>%
set_expt_conditions(fact = "clinicaloutcome") %>%
set_expt_batches(fact = "typeofcells") %>%
subset_expt(subset = "typeofcells!='pbmcs'&typeofcells!='macrophages'")## subset_expt(): There were 135, now there are 117 samples.chosen_colors <- c("#D95F02", "#7570B3", "#1B9E77", "#FF0000", "#FF0000")
names(chosen_colors) <- c("cure", "failure", "lost", "null", "notapplicable")
hs_clinical <- set_expt_colors(hs_clinical, colors = chosen_colors)
newnames <- make.names(pData(hs_clinical)[["samplename"]], unique = TRUE)
hs_clinical <- set_expt_samplenames(hs_clinical, newnames = newnames)
hs_clinical_norm <- sm(normalize_expt(hs_clinical, filter = TRUE, transform = "log2",
convert = "cpm", norm = "quant"))
clinical_pca <- plot_pca(hs_clinical_norm, plot_labels = FALSE,
size_column = "visitnumber", cis = NULL,
plot_title = "PCA - clinical samples")
pp(file = glue("images/all_clinical_nobatch_pca-v{ver}.png"), image = clinical_pca$plot,
height = 8, width = 20)
4.3.1 Repeat without the biopsy samples
hs_clinical_nobiop <- hs_clinical %>%
subset_expt(subset = "typeofcells!='biopsy'")## subset_expt(): There were 117, now there are 75 samples.hs_clinical_nobiop_norm <- sm(normalize_expt(hs_clinical_nobiop, filter = TRUE, transform = "log2",
convert = "cpm", norm = "quant"))
clinical_nobiop_pca <- plot_pca(hs_clinical_nobiop_norm, plot_labels = FALSE, cis = NULL,
plot_title = "PCA - clinical samples without biopsies")
pp(file = glue("images/all_clinical_nobiop_nobatch_pca-v{ver}.png"),
image = clinical_nobiop_pca$plot)
4.3.2 Attempt to correct for the surrogate variables
At this time we have two primary data structures of interest: hs_clinical and hs_clinical_nobiop
hs_clinical_nb <- normalize_expt(hs_clinical, filter = TRUE, batch = "svaseq",
transform = "log2", convert = "cpm")## Removing 5312 low-count genes (14629 remaining).## batch_counts: Before batch/surrogate estimation, 108221 entries are x==0: 6%.## batch_counts: Before batch/surrogate estimation, 314017 entries are 0<x<1: 18%.## Setting 22060 low elements to zero.## transform_counts: Found 22060 values equal to 0, adding 1 to the matrix.clinical_batch_pca <- plot_pca(hs_clinical_nb, plot_labels = FALSE, cis = NULL,
size_column = "visitnumber", plot_title = "PCA - clinical samples")
clinical_batch_pca$plot
hs_clinical_nobiop_nb <- sm(normalize_expt(hs_clinical_nobiop, filter = TRUE, batch = "svaseq",
transform = "log2", convert = "cpm"))
clinical_nobiop_batch_pca <- plot_pca(hs_clinical_nobiop_nb,
plot_title = "PCA - clinical samples without biopsies",
plot_labels = FALSE)
pp(file = "images/clinical_batch.png", image = clinical_nobiop_batch_pca$plot)
test <- plot_pca(hs_clinical_nobiop_nb, size_column = "visitnumber",
plot_title = "PCA - clinical samples without biopsies",
plot_labels = FALSE)
test$plot
clinical_nobiop_batch_tsne <- plot_tsne(hs_clinical_nobiop_nb,
plot_title = "tSNE - clinical samples without biopsies",
plot_labels = FALSE)
clinical_nobiop_batch_tsne$plot
4.3.2.1 Look at remaining variance with variancePartition
test <- simple_varpart(hs_clinical_nobiop)##
## Total:100 stest$partition_plot
4.4 Perform DE of the clinical samples cure vs. fail
individual_celltypes <- subset_expt(hs_clinical_nobiop, subset="condition!='lost'")## subset_expt(): There were 75, now there are 60 samples.hs_clinic_de <- sm(all_pairwise(individual_celltypes, model_batch = "svaseq", filter = TRUE))
hs_clinic_table <- sm(combine_de_tables(
hs_clinic_de,
excel = glue::glue("excel/individual_celltypes_table-v{ver}.xlsx")))
hs_clinic_sig <- sm(extract_significant_genes(
hs_clinic_table,
excel = glue::glue("excel/individual_celltypes_sig-v{ver}.xlsx")))
hs_clinic_sig[["summary_df"]]## limma_V1 limma_V2 edger_V1 edger_V2 deseq_V1 deseq_V2 ebseq_V1 ebseq_V2
## 1 169 207 260 289 227 323 63 194
## basic_V1 basic_V2
## 1 34 9hs_clinic_de[["comparison"]][["heat"]]## NULL4.4.1 Perform LRT with the clinical samples
I am not sure if we have enough samples across the three visit to completely work as well as we would like, but there is only 1 way to find out! Now that I think about it, one thing which might be awesome is to use cell type as an interacting factor…
4.4.1.1 With biopsy samples
I figure this might be a place where the biopsy samples might prove useful.
clinical_nolost <- subset_expt(hs_clinical, subset="condition!='lost'")## subset_expt(): There were 117, now there are 100 samples.lrt_visit_clinical_test <- deseq_lrt(clinical_nolost, transform = "vst",
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 231 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testing## Working with 2 genes.## Warning: `distinct_()` was deprecated in dplyr 0.7.0.
## Please use `distinct()` instead.
## See vignette('programming') for more help## Working with 0 genes after filtering: minc > 3## Error: object 'cutoff' not foundlrt_visit_clinical_test[["favorite_genes"]]## Error in eval(expr, envir, enclos): object 'lrt_visit_clinical_test' not foundlrt_celltype_clinical_test <- deseq_lrt(clinical_nolost, 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 57 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testing## Working with 882 genes.## Working with 880 genes after filtering: minc > 3## Joining, by = "merge"
## Joining, by = "merge"
lrt_celltype_clinical_test[["favorite_genes"]]## genes cluster
## ENSG00000001497 ENSG00000001497 1
## ENSG00000003137 ENSG00000003137 2
## ENSG00000004455 ENSG00000004455 3
## ENSG00000004846 ENSG00000004846 4
## ENSG00000005020 ENSG00000005020 5
## ENSG00000005175 ENSG00000005175 6
## ENSG00000005486 ENSG00000005486 7
## ENSG00000005801 ENSG00000005801 1
## ENSG00000007047 ENSG00000007047 5
## ENSG00000007392 ENSG00000007392 8
## ENSG00000008056 ENSG00000008056 9
## ENSG00000008086 ENSG00000008086 10
## ENSG00000010270 ENSG00000010270 11
## ENSG00000010278 ENSG00000010278 8
## ENSG00000010704 ENSG00000010704 11
## ENSG00000010803 ENSG00000010803 12
## ENSG00000011028 ENSG00000011028 2
## ENSG00000011426 ENSG00000011426 4
## ENSG00000013306 ENSG00000013306 13
## ENSG00000013583 ENSG00000013583 14
## ENSG00000017427 ENSG00000017427 15
## ENSG00000019144 ENSG00000019144 2
## ENSG00000020256 ENSG00000020256 8
## ENSG00000020577 ENSG00000020577 2
## ENSG00000020633 ENSG00000020633 16
## ENSG00000021355 ENSG00000021355 9
## ENSG00000028203 ENSG00000028203 1
## ENSG00000030066 ENSG00000030066 17
## ENSG00000032444 ENSG00000032444 16
## ENSG00000035141 ENSG00000035141 3
## ENSG00000035720 ENSG00000035720 17
## ENSG00000038945 ENSG00000038945 2
## ENSG00000039560 ENSG00000039560 2
## ENSG00000042088 ENSG00000042088 1
## ENSG00000044459 ENSG00000044459 13
## ENSG00000048828 ENSG00000048828 5
## ENSG00000050820 ENSG00000050820 18
## ENSG00000055130 ENSG00000055130 13
## ENSG00000057704 ENSG00000057704 19
## ENSG00000060138 ENSG00000060138 14
## ENSG00000060982 ENSG00000060982 2
## ENSG00000061918 ENSG00000061918 3
## ENSG00000062038 ENSG00000062038 4
## ENSG00000062598 ENSG00000062598 5
## ENSG00000064763 ENSG00000064763 10
## ENSG00000064933 ENSG00000064933 3
## ENSG00000065150 ENSG00000065150 1
## ENSG00000065809 ENSG00000065809 7
## ENSG00000065989 ENSG00000065989 12
## ENSG00000066427 ENSG00000066427 5
## ENSG00000066455 ENSG00000066455 15
## ENSG00000066651 ENSG00000066651 2
## ENSG00000067533 ENSG00000067533 1
## ENSG00000068305 ENSG00000068305 16
## ENSG00000069020 ENSG00000069020 15
## ENSG00000069345 ENSG00000069345 7
## ENSG00000069998 ENSG00000069998 13
## ENSG00000070269 ENSG00000070269 10
## ENSG00000072657 ENSG00000072657 20
## ENSG00000073150 ENSG00000073150 5
## ENSG00000073350 ENSG00000073350 8
## ENSG00000073417 ENSG00000073417 12
## ENSG00000073737 ENSG00000073737 10
## ENSG00000073754 ENSG00000073754 2
## ENSG00000074211 ENSG00000074211 4
## ENSG00000074657 ENSG00000074657 2
## ENSG00000074935 ENSG00000074935 3
## ENSG00000075643 ENSG00000075643 1
## ENSG00000076242 ENSG00000076242 3
## ENSG00000076716 ENSG00000076716 2
## ENSG00000077616 ENSG00000077616 17
## ENSG00000077782 ENSG00000077782 2
## ENSG00000077935 ENSG00000077935 13
## ENSG00000078098 ENSG00000078098 15
## ENSG00000078589 ENSG00000078589 22
## ENSG00000078596 ENSG00000078596 3
## ENSG00000079215 ENSG00000079215 1
## ENSG00000079482 ENSG00000079482 1
## ENSG00000079616 ENSG00000079616 8
## ENSG00000081386 ENSG00000081386 3
## ENSG00000081913 ENSG00000081913 9
## ENSG00000082516 ENSG00000082516 3
## ENSG00000083457 ENSG00000083457 12
## ENSG00000083828 ENSG00000083828 19
## ENSG00000084207 ENSG00000084207 13
## ENSG00000085871 ENSG00000085871 7
## ENSG00000085999 ENSG00000085999 1
## ENSG00000086730 ENSG00000086730 9
## ENSG00000087116 ENSG00000087116 1
## ENSG00000087269 ENSG00000087269 1
## ENSG00000088543 ENSG00000088543 3
## ENSG00000088726 ENSG00000088726 3
## ENSG00000088826 ENSG00000088826 1
## ENSG00000088827 ENSG00000088827 14
## ENSG00000088992 ENSG00000088992 16
## ENSG00000089012 ENSG00000089012 18
## ENSG00000089127 ENSG00000089127 14
## ENSG00000089818 ENSG00000089818 5
## ENSG00000090013 ENSG00000090013 14
## ENSG00000090674 ENSG00000090674 7
## ENSG00000091409 ENSG00000091409 18
## ENSG00000091972 ENSG00000091972 15
## ENSG00000092445 ENSG00000092445 1
## ENSG00000094841 ENSG00000094841 1
## ENSG00000095002 ENSG00000095002 18
## ENSG00000095585 ENSG00000095585 2
## ENSG00000099260 ENSG00000099260 18
## ENSG00000099783 ENSG00000099783 16
## ENSG00000100036 ENSG00000100036 13
## ENSG00000100065 ENSG00000100065 3
## ENSG00000100124 ENSG00000100124 15
## ENSG00000100138 ENSG00000100138 1
## ENSG00000100281 ENSG00000100281 16
## ENSG00000100288 ENSG00000100288 1
## ENSG00000100292 ENSG00000100292 14
## ENSG00000100335 ENSG00000100335 23
## ENSG00000100354 ENSG00000100354 10
## ENSG00000100376 ENSG00000100376 13
## ENSG00000100413 ENSG00000100413 23
## ENSG00000100558 ENSG00000100558 1
## ENSG00000100596 ENSG00000100596 16
## ENSG00000100767 ENSG00000100767 6
## ENSG00000100842 ENSG00000100842 15
## ENSG00000101000 ENSG00000101000 4
## ENSG00000101096 ENSG00000101096 17
## ENSG00000101188 ENSG00000101188 11
## ENSG00000101189 ENSG00000101189 23
## ENSG00000101255 ENSG00000101255 9
## ENSG00000101384 ENSG00000101384 3
## ENSG00000101665 ENSG00000101665 23
## ENSG00000101844 ENSG00000101844 18
## ENSG00000102007 ENSG00000102007 5
## ENSG00000102048 ENSG00000102048 1
## ENSG00000102221 ENSG00000102221 1
## ENSG00000102290 ENSG00000102290 13
## ENSG00000102384 ENSG00000102384 18
## ENSG00000102409 ENSG00000102409 3
## ENSG00000102572 ENSG00000102572 5
## ENSG00000102738 ENSG00000102738 18
## ENSG00000102967 ENSG00000102967 11
## ENSG00000102978 ENSG00000102978 9
## ENSG00000103047 ENSG00000103047 13
## ENSG00000103056 ENSG00000103056 17
## ENSG00000103257 ENSG00000103257 4
## ENSG00000103274 ENSG00000103274 13
## ENSG00000103319 ENSG00000103319 8
## ENSG00000103740 ENSG00000103740 18
## ENSG00000104133 ENSG00000104133 10
## ENSG00000104213 ENSG00000104213 2
## ENSG00000104218 ENSG00000104218 1
## ENSG00000104321 ENSG00000104321 15
## ENSG00000104689 ENSG00000104689 13
## ENSG00000104738 ENSG00000104738 3
## ENSG00000104936 ENSG00000104936 2
## ENSG00000104969 ENSG00000104969 16
## ENSG00000104980 ENSG00000104980 13
## ENSG00000104998 ENSG00000104998 13
## ENSG00000105146 ENSG00000105146 4
## ENSG00000105220 ENSG00000105220 12
## ENSG00000105245 ENSG00000105245 20
## ENSG00000105278 ENSG00000105278 6
## ENSG00000105281 ENSG00000105281 13
## ENSG00000105366 ENSG00000105366 17
## ENSG00000105499 ENSG00000105499 2
## ENSG00000105576 ENSG00000105576 3
## ENSG00000105612 ENSG00000105612 14
## ENSG00000105649 ENSG00000105649 10
## ENSG00000105656 ENSG00000105656 5
## ENSG00000105668 ENSG00000105668 15
## ENSG00000105928 ENSG00000105928 2
## ENSG00000105967 ENSG00000105967 14
## ENSG00000105997 ENSG00000105997 6
## ENSG00000106100 ENSG00000106100 22
## ENSG00000106366 ENSG00000106366 1
## ENSG00000106617 ENSG00000106617 16
## ENSG00000106635 ENSG00000106635 16
## ENSG00000106638 ENSG00000106638 13
## ENSG00000106804 ENSG00000106804 14
## ENSG00000107130 ENSG00000107130 4
## ENSG00000107185 ENSG00000107185 5
## ENSG00000107281 ENSG00000107281 1
## ENSG00000107438 ENSG00000107438 8
## ENSG00000107485 ENSG00000107485 1
## ENSG00000107719 ENSG00000107719 2
## ENSG00000107731 ENSG00000107731 3
## ENSG00000107816 ENSG00000107816 1
## ENSG00000107819 ENSG00000107819 23
## ENSG00000108219 ENSG00000108219 16
## ENSG00000108389 ENSG00000108389 10
## ENSG00000108821 ENSG00000108821 15
## ENSG00000108963 ENSG00000108963 13
## ENSG00000109113 ENSG00000109113 13
## ENSG00000109182 ENSG00000109182 4
## ENSG00000109685 ENSG00000109685 8
## ENSG00000109917 ENSG00000109917 23
## ENSG00000109919 ENSG00000109919 1
## ENSG00000109971 ENSG00000109971 2
## ENSG00000110092 ENSG00000110092 3
## ENSG00000110171 ENSG00000110171 23
## ENSG00000110200 ENSG00000110200 12
## ENSG00000110455 ENSG00000110455 12
## ENSG00000110660 ENSG00000110660 1
## ENSG00000110665 ENSG00000110665 24
## ENSG00000110851 ENSG00000110851 17
## ENSG00000111144 ENSG00000111144 11
## ENSG00000111145 ENSG00000111145 9
## ENSG00000111331 ENSG00000111331 11
## ENSG00000111335 ENSG00000111335 14
## ENSG00000111424 ENSG00000111424 9
## ENSG00000111640 ENSG00000111640 14
## ENSG00000111799 ENSG00000111799 15
## ENSG00000111801 ENSG00000111801 2
## ENSG00000111850 ENSG00000111850 6
## ENSG00000112033 ENSG00000112033 5
## ENSG00000112130 ENSG00000112130 1
## ENSG00000112208 ENSG00000112208 18
## ENSG00000112299 ENSG00000112299 11
## ENSG00000112715 ENSG00000112715 16
## ENSG00000113273 ENSG00000113273 24
## ENSG00000113360 ENSG00000113360 3
## ENSG00000113649 ENSG00000113649 13
## ENSG00000113716 ENSG00000113716 12
## ENSG00000114439 ENSG00000114439 10
## ENSG00000114735 ENSG00000114735 8
## ENSG00000114737 ENSG00000114737 16
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## ENSG00000178338 ENSG00000178338 1
## ENSG00000178573 ENSG00000178573 1
## ENSG00000178700 ENSG00000178700 2
## ENSG00000178741 ENSG00000178741 13
## ENSG00000178896 ENSG00000178896 9
## ENSG00000179044 ENSG00000179044 20
## ENSG00000179262 ENSG00000179262 23
## ENSG00000179409 ENSG00000179409 1
## ENSG00000179476 ENSG00000179476 15
## ENSG00000179528 ENSG00000179528 1
## ENSG00000180376 ENSG00000180376 8
## ENSG00000180543 ENSG00000180543 1
## ENSG00000181045 ENSG00000181045 14
## ENSG00000181350 ENSG00000181350 1
## ENSG00000181392 ENSG00000181392 22
## ENSG00000181523 ENSG00000181523 14
## ENSG00000181873 ENSG00000181873 1
## ENSG00000182158 ENSG00000182158 8
## ENSG00000182173 ENSG00000182173 8
## ENSG00000182240 ENSG00000182240 17
## ENSG00000182263 ENSG00000182263 4
## ENSG00000182378 ENSG00000182378 8
## ENSG00000182504 ENSG00000182504 15
## ENSG00000182557 ENSG00000182557 12
## ENSG00000182704 ENSG00000182704 2
## ENSG00000182885 ENSG00000182885 19
## ENSG00000182901 ENSG00000182901 6
## ENSG00000182952 ENSG00000182952 20
## ENSG00000182986 ENSG00000182986 3
## ENSG00000183044 ENSG00000183044 10
## ENSG00000183087 ENSG00000183087 1
## ENSG00000183091 ENSG00000183091 6
## ENSG00000183117 ENSG00000183117 14
## ENSG00000183283 ENSG00000183283 5
## ENSG00000183431 ENSG00000183431 7
## ENSG00000183578 ENSG00000183578 2
## ENSG00000183617 ENSG00000183617 13
## ENSG00000183763 ENSG00000183763 3
## ENSG00000183785 ENSG00000183785 22
## ENSG00000183801 ENSG00000183801 18
## ENSG00000183853 ENSG00000183853 2
## ENSG00000183955 ENSG00000183955 7
## ENSG00000184226 ENSG00000184226 15
## ENSG00000184384 ENSG00000184384 11
## ENSG00000184402 ENSG00000184402 10
## ENSG00000184661 ENSG00000184661 18
## ENSG00000185033 ENSG00000185033 11
## ENSG00000185043 ENSG00000185043 7
## ENSG00000185561 ENSG00000185561 4
## ENSG00000185669 ENSG00000185669 5
## ENSG00000185686 ENSG00000185686 4
## ENSG00000185736 ENSG00000185736 19
## ENSG00000185825 ENSG00000185825 9
## ENSG00000185842 ENSG00000185842 6
## ENSG00000185920 ENSG00000185920 18
## ENSG00000186197 ENSG00000186197 2
## ENSG00000186645 ENSG00000186645 10
## ENSG00000186818 ENSG00000186818 14
## ENSG00000186951 ENSG00000186951 12
## ENSG00000187097 ENSG00000187097 8
## ENSG00000187105 ENSG00000187105 17
## ENSG00000187164 ENSG00000187164 13
## ENSG00000187231 ENSG00000187231 11
## ENSG00000187554 ENSG00000187554 11
## ENSG00000187566 ENSG00000187566 1
## ENSG00000188037 ENSG00000188037 9
## ENSG00000188343 ENSG00000188343 15
## ENSG00000188452 ENSG00000188452 22
## ENSG00000188636 ENSG00000188636 13
## ENSG00000188921 ENSG00000188921 10
## ENSG00000188938 ENSG00000188938 9
## ENSG00000189013 ENSG00000189013 2
## ENSG00000189067 ENSG00000189067 6
## ENSG00000189077 ENSG00000189077 5
## ENSG00000189159 ENSG00000189159 19
## ENSG00000189195 ENSG00000189195 20
## ENSG00000189337 ENSG00000189337 6
## ENSG00000196072 ENSG00000196072 5
## ENSG00000196123 ENSG00000196123 14
## ENSG00000196126 ENSG00000196126 13
## ENSG00000196199 ENSG00000196199 10
## ENSG00000196230 ENSG00000196230 1
## ENSG00000196247 ENSG00000196247 6
## ENSG00000196305 ENSG00000196305 1
## ENSG00000196369 ENSG00000196369 11
## ENSG00000196378 ENSG00000196378 12
## ENSG00000196405 ENSG00000196405 8
## ENSG00000196526 ENSG00000196526 3
## ENSG00000196652 ENSG00000196652 6
## ENSG00000196872 ENSG00000196872 6
## ENSG00000196923 ENSG00000196923 5
## ENSG00000197008 ENSG00000197008 3
## ENSG00000197093 ENSG00000197093 6
## ENSG00000197283 ENSG00000197283 8
## ENSG00000197302 ENSG00000197302 18
## ENSG00000197451 ENSG00000197451 13
## ENSG00000197461 ENSG00000197461 3
## ENSG00000197603 ENSG00000197603 3
## ENSG00000197782 ENSG00000197782 18
## ENSG00000197959 ENSG00000197959 18
## ENSG00000198178 ENSG00000198178 20
## ENSG00000198286 ENSG00000198286 1
## ENSG00000198336 ENSG00000198336 18
## ENSG00000198435 ENSG00000198435 3
## ENSG00000198502 ENSG00000198502 13
## ENSG00000198690 ENSG00000198690 10
## ENSG00000198700 ENSG00000198700 13
## ENSG00000198795 ENSG00000198795 15
## ENSG00000198805 ENSG00000198805 13
## ENSG00000198829 ENSG00000198829 18
## ENSG00000198959 ENSG00000198959 2
## ENSG00000203546 ENSG00000203546 1
## ENSG00000204103 ENSG00000204103 14
## ENSG00000204388 ENSG00000204388 2
## ENSG00000204389 ENSG00000204389 6
## ENSG00000204520 ENSG00000204520 6
## ENSG00000204568 ENSG00000204568 1
## ENSG00000204869 ENSG00000204869 15
## ENSG00000204909 ENSG00000204909 6
## ENSG00000204991 ENSG00000204991 18
## ENSG00000205038 ENSG00000205038 3
## ENSG00000205060 ENSG00000205060 1
## ENSG00000205090 ENSG00000205090 10
## ENSG00000205362 ENSG00000205362 15
## ENSG00000205639 ENSG00000205639 12
## ENSG00000205730 ENSG00000205730 14
## ENSG00000211448 ENSG00000211448 15
## ENSG00000213588 ENSG00000213588 3
## ENSG00000213719 ENSG00000213719 11
## ENSG00000213859 ENSG00000213859 3
## ENSG00000214226 ENSG00000214226 2
## ENSG00000214706 ENSG00000214706 1
## ENSG00000214872 ENSG00000214872 5
## ENSG00000215788 ENSG00000215788 1
## ENSG00000219481 ENSG00000219481 8
## ENSG00000235750 ENSG00000235750 20
## ENSG00000239920 ENSG00000239920 10
## ENSG00000240445 ENSG00000240445 4
## ENSG00000244165 ENSG00000244165 13
## ENSG00000244405 ENSG00000244405 2
## ENSG00000248405 ENSG00000248405 20
## ENSG00000248993 ENSG00000248993 13
## ENSG00000249242 ENSG00000249242 1
## ENSG00000254979 ENSG00000254979 23
## ENSG00000256043 ENSG00000256043 1
## ENSG00000256235 ENSG00000256235 12
## ENSG00000257335 ENSG00000257335 5
## ENSG00000259330 ENSG00000259330 23
## ENSG00000261652 ENSG00000261652 18
## ENSG00000261832 ENSG00000261832 24
## ENSG00000263001 ENSG00000263001 25
## ENSG00000263528 ENSG00000263528 13
## ENSG00000263715 ENSG00000263715 3
## ENSG00000264364 ENSG00000264364 19
## ENSG00000268182 ENSG00000268182 8
## ENSG00000268861 ENSG00000268861 24
## ENSG00000273802 ENSG00000273802 6
## ENSG00000274810 ENSG00000274810 18
## ENSG00000275895 ENSG00000275895 7
## ENSG00000276085 ENSG00000276085 6
## ENSG00000277117 ENSG00000277117 13
## ENSG00000282988 ENSG00000282988 10
## ENSG00000283149 ENSG00000283149 13
## ENSG00000283977 ENSG00000283977 16
## ENSG00000285253 ENSG00000285253 1
## ENSG00000285708 ENSG00000285708 104.4.2 Look at only the differential genes
A good suggestion from Theresa was to examine only the most variant genes from failure vs. cure and see how they change the clustering/etc results. This is my attempt to address this query.
hs_clinic_topn <- sm(extract_significant_genes(hs_clinic_table, n = 100))
table <- "failure_vs_cure"
wanted <- rbind(hs_clinic_topn[["deseq"]][["ups"]][[table]],
hs_clinic_topn[["deseq"]][["downs"]][[table]])
small_expt <- exclude_genes_expt(hs_clinical_nobiop, ids = rownames(wanted), method = "keep")## Before removal, there were 19941 genes, now there are 200.## There are 75 samples which kept less than 90 percent counts.## X1017n1 X1017m1 X1034n1 X1034n2 X1034m2 X1034m2. X2052e1 X2052m2
## 0.4192 0.3754 2.4868 2.9008 1.2743 1.2381 0.5331 0.6164
## X2052n2 X2052m3 X2052n3 X2065m1 X2065n1 X2066m1 X2066n1 X2065m2
## 1.4080 0.7016 1.1583 1.0719 2.8747 0.3940 0.7222 0.5819
## X2065n2 X2065e2 X2066m2 X2066n2 X2066e2 X2068m1 X2068n1 X2068e1
## 0.4933 0.8420 0.4952 0.6676 0.8413 0.5006 0.5287 1.0548
## X2072m1 X2072n1 X2072e1 X2071m1 X2071n1 X2073m1 X2073n1 X2073e1
## 0.4799 0.4190 0.6492 0.7265 1.6127 0.6570 1.7804 0.7564
## X2068m2 X2068n2 X2068e2 X2072m2 X2072n2 X2072e2 X2073m2 X2073n2
## 0.3427 0.4429 0.6802 0.3992 0.5430 0.5317 1.0397 2.8228
## X2073e2 X2066m3 X2066n3 X2065e3 X2068m3 X2068n3 X2068e3 X2072m3
## 0.7690 0.4304 0.5995 0.4657 0.4092 0.4483 0.6113 0.7292
## X2072n3 X2072e3 X2073m3 X2073n3 X2073e3 X2162m1 X2162n1 X2162e1
## 1.9625 0.7389 0.5093 0.6651 0.4507 0.4594 0.4140 0.7208
## X2167m1 X2168n1 X2168e1 X2168m2 X2168n2 X2168e2 X2167m2 X2167n3
## 0.6211 2.3153 0.9847 0.9395 2.3681 0.8613 0.3830 1.0254
## X2167e3 X2168m3 X2168n3 X2168e3 X2172n1 X2172e1 X1168m1 X1168n1
## 1.1411 1.3360 3.7049 1.0558 0.3235 0.3720 0.7320 1.4522
## X1168m2 X1168e2 X1168n3
## 0.7185 0.5010 1.0138small_norm <- sm(normalize_expt(small_expt, transform = "log2", convert = "cpm",
norm = "quant", filter = TRUE))
plot_pca(small_norm)$plot## Warning: ggrepel: 15 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
small_nb <- normalize_expt(small_expt, transform = "log2", convert = "cpm",
batch = "svaseq", norm = "quant", filter = TRUE)## Warning in normalize_expt(small_expt, transform = "log2", convert = "cpm", :
## Quantile normalization and sva do not always play well together.## Removing 0 low-count genes (200 remaining).## batch_counts: Before batch/surrogate estimation, 2 entries are x==0: 0%.## batch_counts: Before batch/surrogate estimation, 526 entries are 0<x<1: 4%.## Setting 58 low elements to zero.## transform_counts: Found 58 values equal to 0, adding 1 to the matrix.plot_pca(small_nb)$plot
## DESeq2 MA plot of failure / cure
hs_clinic_table[["plots"]][["failure_vs_cure"]][["deseq_ma_plots"]]$plot
## DESeq2 Volcano plot of failure / cure
hs_clinic_table[["plots"]][["failure_vs_cure"]][["deseq_vol_plots"]]$plot
4.4.3 g:Profiler results using the significant up and down genes
ups <- hs_clinic_sig[["deseq"]][["ups"]][[1]]
downs <- hs_clinic_sig[["deseq"]][["downs"]][[1]]
hs_clinic_gprofiler_ups <- simple_gprofiler(ups)## Performing gProfiler GO search of 227 genes against hsapiens.## GO search found 71 hits.## Performing gProfiler KEGG search of 227 genes against hsapiens.## KEGG search found 5 hits.## Performing gProfiler REAC search of 227 genes against hsapiens.## REAC search found 9 hits.## Performing gProfiler MI search of 227 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 227 genes against hsapiens.## TF search found 43 hits.## Performing gProfiler CORUM search of 227 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 227 genes against hsapiens.## HP search found 0 hits.hs_clinic_gprofiler_ups[["pvalue_plots"]][["bpp_plot_over"]]
hs_clinic_gprofiler_ups[["pvalue_plots"]][["mfp_plot_over"]]
hs_clinic_gprofiler_ups[["pvalue_plots"]][["reactome_plot_over"]]
##hs_try2 <- simple_gprofiler2(ups)
hs_clinic_gprofiler_downs <- simple_gprofiler(downs)## Performing gProfiler GO search of 323 genes against hsapiens.## GO search found 106 hits.## Performing gProfiler KEGG search of 323 genes against hsapiens.## KEGG search found 3 hits.## Performing gProfiler REAC search of 323 genes against hsapiens.## REAC search found 5 hits.## Performing gProfiler MI search of 323 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 323 genes against hsapiens.## TF search found 40 hits.## Performing gProfiler CORUM search of 323 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 323 genes against hsapiens.## HP search found 3 hits.hs_clinic_gprofiler_downs[["pvalue_plots"]][["bpp_plot_over"]]
hs_clinic_gprofiler_downs[["pvalue_plots"]][["mfp_plot_over"]]
hs_clinic_gprofiler_downs[["pvalue_plots"]][["reactome_plot_over"]]
4.5 Perform GSVA on the clinical samples
hs_celltype_gsva_c2 <- sm(simple_gsva(individual_celltypes))
hs_celltype_gsva_c2_sig <- sm(get_sig_gsva_categories(
hs_celltype_gsva_c2,
excel = "excel/individual_celltypes_gsva_c2.xlsx"))
broad_c7 <- GSEABase::getGmt("reference/msigdb/c7.all.v7.2.entrez.gmt",
collectionType = GSEABase::BroadCollection(category = "c7"),
geneIdType = GSEABase::EntrezIdentifier())
hs_celltype_gsva_c7 <- sm(simple_gsva(individual_celltypes, signatures = broad_c7,
msig_xml = "reference/msigdb_v7.2.xml", cores = 10))
hs_celltype_gsva_c7_sig <- sm(get_sig_gsva_categories(
hs_celltype_gsva_c7,
excel = "excel/individual_celltypes_gsva_c7.xlsx"))4.5.1 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"]]
5 Individual Cell types
The following blocks split the samples into a few groups by sample type and look at the distributions between them.
5.1 Implementation details
Get top/bottom n genes for each cell type, using clinical outcome as the factor of interest. For the moment, use sva for the DE analysis. Provide cpms for the top/bottom n genes.
Start with top/bottom 200. Perform default logFC and p-value as well.
5.2 Monocytes
5.2.1 Evaluate Monocyte samples
mono <- subset_expt(hs_valid, subset = "typeofcells=='monocytes'") %>%
set_expt_conditions(fact = "clinicaloutcome") %>%
set_expt_batches(fact = "donor") %>%
set_expt_colors(colors = chosen_colors)## subset_expt(): There were 135, now there are 27 samples.## FIXME set_expt_colors should speak up if there are mismatches here!!!
save_result <- save(mono, file = "rda/monocyte_expt.rda")
mono_norm <- normalize_expt(mono, convert = "cpm", filter = TRUE,
transform = "log2", norm = "quant")## Removing 8915 low-count genes (11026 remaining).## transform_counts: Found 9 values equal to 0, adding 1 to the matrix.plt <- plot_pca(mono_norm, plot_labels = FALSE)$plot
pp(file = glue("images/mono_pca_normalized-v{ver}.pdf"), image = plt)
mono_nb <- normalize_expt(mono, convert = "cpm", filter = TRUE,
transform = "log2", batch = "svaseq")## Removing 8915 low-count genes (11026 remaining).## batch_counts: Before batch/surrogate estimation, 1404 entries are x==0: 0%.## batch_counts: Before batch/surrogate estimation, 17611 entries are 0<x<1: 6%.## Setting 556 low elements to zero.## transform_counts: Found 556 values equal to 0, adding 1 to the matrix.plt <- plot_pca(mono_nb, plot_labels = FALSE)$plot
pp(file = glue("images/mono_pca_normalized_batch-v{ver}.pdf"), image = plt)
5.2.2 DE of Monocyte samples
5.2.2.1 Without sva
mono_de <- sm(all_pairwise(mono, model_batch = FALSE, filter = TRUE))mono_tables <- sm(combine_de_tables(
mono_de, keepers = keepers,
excel = glue::glue("excel/monocyte_clinical_all_tables-v{ver}.xlsx")))
written <- write_xlsx(data = mono_tables[["data"]][[1]],
excel = glue::glue("excel/monocyte_clinical_table-v{ver}.xlsx"))
mono_sig <- sm(extract_significant_genes(mono_tables, according_to = "deseq"))
written <- write_xlsx(data = mono_sig[["deseq"]][["ups"]][[1]],
excel = glue::glue("excel/monocyte_clinical_sigup-v{ver}.xlsx"))
written <- write_xlsx(data = mono_sig[["deseq"]][["downs"]][[1]],
excel = glue::glue("excel/monocyte_clinical_sigdown-v{ver}.xlsx"))
mono_pct_sig <- sm(extract_significant_genes(mono_tables, n = 200,
lfc = NULL, p = NULL, according_to = "deseq"))
written <- write_xlsx(data = mono_pct_sig[["deseq"]][["ups"]][[1]],
excel = glue::glue("excel/monocyte_clinical_sigup_pct-v{ver}.xlsx"))
written <- write_xlsx(data = mono_pct_sig[["deseq"]][["downs"]][[1]],
excel = glue::glue("excel/monocyte_clinical_sigdown_pct-v{ver}.xlsx"))
mono_sig$summary_df## data frame with 0 columns and 1 row## Print out a table of the cpm values for other explorations.
mono_cpm <- sm(normalize_expt(mono, convert = "cpm"))
written <- write_xlsx(data = exprs(mono_cpm),
excel = glue::glue("excel/monocyte_cpm_before_batch-v{ver}.xlsx"))
mono_bcpm <- sm(normalize_expt(mono, filter = TRUE, convert = "cpm", batch = "svaseq"))
written <- write_xlsx(data = exprs(mono_bcpm),
excel = glue::glue("excel/monocyte_cpm_after_batch-v{ver}.xlsx"))5.2.2.2 With sva
mono_de_sva <- sm(all_pairwise(mono, model_batch = "svaseq", filter = TRUE))mono_tables_sva <- sm(combine_de_tables(
mono_de_sva, keepers = keepers,
excel = glue::glue("excel/monocyte_clinical_all_tables_sva-v{ver}.xlsx")))
mono_sig_sva <- sm(extract_significant_genes(
mono_tables_sva,
excel = glue::glue("excel/monocyte_clinical_sig_tables_sva-v{ver}.xlsx"),
according_to = "deseq"))5.2.2.3 Monocyte DE plots
First print out the DE plots without and then with sva estimates.
## DESeq2 MA plot of failure / cure
mono_tables[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]]$plot
## DESeq2 Volcano plot of failure / cure
mono_tables[["plots"]][["fail_vs_cure"]][["deseq_vol_plots"]]$plot
## DESeq2 MA plot of failure / cure with svaseq
mono_tables_sva[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]]$plot
## DESeq2 Volcano plot of failure / cure with svaseq
mono_tables_sva[["plots"]][["fail_vs_cure"]][["deseq_vol_plots"]]$plot
5.2.2.4 Monocyte ontology search
ups <- mono_sig[["deseq"]][["ups"]][["fail_vs_cure"]]
downs <- mono_sig[["deseq"]][["downs"]][["fail_vs_cure"]]
mono_up_gp <- simple_gprofiler(sig_genes = ups)## Performing gProfiler GO search of 103 genes against hsapiens.## GO search found 22 hits.## Performing gProfiler KEGG search of 103 genes against hsapiens.## KEGG search found 4 hits.## Performing gProfiler REAC search of 103 genes against hsapiens.## REAC search found 4 hits.## Performing gProfiler MI search of 103 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 103 genes against hsapiens.## TF search found 13 hits.## Performing gProfiler CORUM search of 103 genes against hsapiens.## CORUM search found 1 hits.## Performing gProfiler HP search of 103 genes against hsapiens.## HP search found 19 hits.mono_up_gp[["pvalue_plots"]][["bpp_plot_over"]]
mono_up_gp[["pvalue_plots"]][["mfp_plot_over"]]
mono_up_gp[["pvalue_plots"]][["reactome_plot_over"]]
mono_down_gp <- simple_gprofiler(sig_genes = downs)## Performing gProfiler GO search of 265 genes against hsapiens.## GO search found 97 hits.## Performing gProfiler KEGG search of 265 genes against hsapiens.## KEGG search found 2 hits.## Performing gProfiler REAC search of 265 genes against hsapiens.## REAC search found 6 hits.## Performing gProfiler MI search of 265 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 265 genes against hsapiens.## TF search found 136 hits.## Performing gProfiler CORUM search of 265 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 265 genes against hsapiens.## HP search found 0 hits.mono_down_gp[["pvalue_plots"]][["bpp_plot_over"]]
mono_down_gp[["pvalue_plots"]][["mfp_plot_over"]]
mono_down_gp[["pvalue_plots"]][["reactome_plot_over"]]
5.2.2.5 Monocyte MSigDB query
broad_c7 <- GSEABase::getGmt("reference/msigdb/c7.all.v7.2.entrez.gmt",
collectionType = GSEABase::BroadCollection(category = "c7"),
geneIdType = GSEABase::EntrezIdentifier())
mono_up_goseq_msig <- goseq_msigdb(sig_genes = ups, signatures = broad_c7,
signature_category = "c7", length_db = hs_length)## Before conversion: 103, after conversion: 104.## Before conversion: 227921, after conversion: 35341.## Found 99 go_db genes and 104 length_db genes out of 104.mono_down_goseq_msig <- goseq_msigdb(sig_genes = downs, signatures = broad_c7,
signature_category = "c7", length_db = hs_length)## Before conversion: 265, after conversion: 264.## Before conversion: 227921, after conversion: 35341.## Found 254 go_db genes and 264 length_db genes out of 264.5.2.2.6 Plot of similar experiments
## Monocyte genes with increased expression in the failed samples
## share genes with the following experiments
mono_up_goseq_msig[["pvalue_plots"]][["mfp_plot_over"]]
## Monocyte genes with increased expression in the cured samples
## share genes with the following experiments
mono_down_goseq_msig[["pvalue_plots"]][["mfp_plot_over"]]
5.2.3 Evaluate Neutrophil samples
neut <- subset_expt(hs_valid, subset = "typeofcells=='neutrophils'") %>%
set_expt_conditions(fact = "clinicaloutcome") %>%
set_expt_batches(fact = "donor") %>%
set_expt_colors(colors = chosen_colors)## subset_expt(): There were 135, now there are 28 samples.save_result <- save(neut, file = "rda/neutrophil_expt.rda")
neut_norm <- sm(normalize_expt(neut, convert = "cpm", filter = TRUE, transform = "log2"))
plt <- plot_pca(neut_norm, plot_labels = FALSE)$plot
pp(file = glue("images/neut_pca_normalized-v{ver}.pdf"), image = plt)
neut_nb <- sm(normalize_expt(neut, convert = "cpm", filter = TRUE,
transform = "log2", batch = "svaseq"))
plt <- plot_pca(neut_nb, plot_labels = FALSE)$plot
pp(file = glue("images/neut_pca_normalized_svaseq-v{ver}.pdf"), image = plt)
5.2.4 DE of Netrophil samples
5.2.4.1 Without sva
neut_de <- sm(all_pairwise(neut, model_batch = FALSE, filter = TRUE))neut_tables <- sm(combine_de_tables(
neut_de, keepers = keepers,
excel = glue::glue("excel/neutrophil_clinical_all_tables-v{ver}.xlsx")))
written <- write_xlsx(data = neut_tables[["data"]][[1]],
excel = glue::glue("excel/neutrophil_clinical_table-v{ver}.xlsx"))
neut_sig <- sm(extract_significant_genes(neut_tables, according_to = "deseq"))
written <- write_xlsx(data = neut_sig[["deseq"]][["ups"]][[1]],
excel = glue::glue("excel/neutrophil_clinical_sigup-v{ver}.xlsx"))
written <- write_xlsx(data = neut_sig[["deseq"]][["downs"]][[1]],
excel = glue::glue("excel/neutrophil_clinical_sigdown-v{ver}.xlsx"))
neut_pct_sig <- sm(extract_significant_genes(neut_tables, n = 200, lfc = NULL,
p = NULL, according_to = "deseq"))
written <- write_xlsx(data = neut_sig[["deseq"]][["ups"]][[1]],
excel = glue::glue("excel/neutrophil_clinical_sigup_pct-v{ver}.xlsx"))
written <- write_xlsx(data = neut_sig[["deseq"]][["downs"]][[1]],
excel = glue::glue("excel/neutrophil_clinical_sigdown_pct-v{ver}.xlsx"))
neut_cpm <- sm(normalize_expt(neut, convert = "cpm"))
written <- write_xlsx(data = exprs(neut_cpm),
excel = glue::glue("excel/neutrophil_cpm_before_batch-v{ver}.xlsx"))
neut_bcpm <- sm(normalize_expt(neut, filter = TRUE, batch = "svaseq", convert = "cpm"))
written <- write_xlsx(data = exprs(neut_bcpm),
excel = glue::glue("excel/neutrophil_cpm_after_batch-v{ver}.xlsx"))5.2.4.2 With sva
neut_de_sva <- sm(all_pairwise(neut, model_batch = "svaseq", filter = TRUE))neut_tables_sva <- sm(combine_de_tables(
neut_de_sva, keepers = keepers,
excel = glue::glue("excel/neutrophil_clinical_all_tables_sva-v{ver}.xlsx")))
neut_sig_sva <- sm(extract_significant_genes(
neut_tables_sva,
excel = glue::glue("excel/neutrophil_clinical_sig_tables_sva-v{ver}.xlsx"),
according_to = "deseq"))5.2.4.3 Neutrophil DE plots
## DESeq2 MA plot of failure / cure
neut_tables[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]]$plot
## DESeq2 Volcano plot of failure / cure
neut_tables[["plots"]][["fail_vs_cure"]][["deseq_vol_plots"]]$plot
## DESeq2 MA plot of failure / cure with sva
neut_tables_sva[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]]$plot
## DESeq2 Volcano plot of failure / cure with sva
neut_tables_sva[["plots"]][["fail_vs_cure"]][["deseq_vol_plots"]]$plot
5.2.4.4 Neutrophil ontology search
ups <- neut_sig[["deseq"]][["ups"]][["fail_vs_cure"]]
downs <- neut_sig[["deseq"]][["downs"]][["fail_vs_cure"]]
neut_up_gp <- simple_gprofiler(sig_genes = ups)## Performing gProfiler GO search of 122 genes against hsapiens.## GO search found 0 hits.## Performing gProfiler KEGG search of 122 genes against hsapiens.## KEGG search found 1 hits.## Performing gProfiler REAC search of 122 genes against hsapiens.## REAC search found 1 hits.## Performing gProfiler MI search of 122 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 122 genes against hsapiens.## TF search found 6 hits.## Performing gProfiler CORUM search of 122 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 122 genes against hsapiens.## HP search found 1 hits.neut_up_gp[["pvalue_plots"]][["bpp_plot_over"]]## NULLneut_up_gp[["pvalue_plots"]][["mfp_plot_over"]]## NULLneut_up_gp[["pvalue_plots"]][["reactome_plot_over"]]
neut_down_gp <- simple_gprofiler(downs)## Performing gProfiler GO search of 137 genes against hsapiens.## GO search found 2 hits.## Performing gProfiler KEGG search of 137 genes against hsapiens.## KEGG search found 0 hits.## Performing gProfiler REAC search of 137 genes against hsapiens.## REAC search found 0 hits.## Performing gProfiler MI search of 137 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 137 genes against hsapiens.## TF search found 6 hits.## Performing gProfiler CORUM search of 137 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 137 genes against hsapiens.## HP search found 5 hits.neut_down_gp[["pvalue_plots"]][["bpp_plot_over"]]
neut_down_gp[["pvalue_plots"]][["mfp_plot_over"]]## NULLneut_down_gp[["pvalue_plots"]][["reactome_plot_over"]]## NULL5.2.4.5 Neutrophil GSVA query
neut_up_goseq_msig <- goseq_msigdb(sig_genes = ups, signatures = broad_c7,
signature_category = "c7", length_db = hs_length)## Before conversion: 122, after conversion: 121.## Before conversion: 227921, after conversion: 35341.## Found 114 go_db genes and 121 length_db genes out of 121.neut_down_goseq_msig <- goseq_msigdb(sig_genes = downs, signatures = broad_c7,
signature_category = "c7", length_db = hs_length)## Before conversion: 137, after conversion: 133.## Before conversion: 227921, after conversion: 35341.## Found 127 go_db genes and 133 length_db genes out of 133.5.2.4.6 Plot of similar experiments
## Neutrophil genes with increased expression in the failed samples
## share genes with the following experiments
neut_up_goseq_msig[["pvalue_plots"]][["mfp_plot_over"]]
## Neutrophil genes with increased expression in the cured samples
## share genes with the following experiments
neut_down_goseq_msig[["pvalue_plots"]][["mfp_plot_over"]]
5.3 Eosinophils
5.3.1 Evaluate Eosinophil samples
eo <- subset_expt(hs_valid, subset = "typeofcells=='eosinophils'") %>%
set_expt_conditions(fact = "clinicaloutcome") %>%
set_expt_batches(fact = "donor") %>%
set_expt_colors(colors = chosen_colors)## subset_expt(): There were 135, now there are 20 samples.save_result <- save(eo, file = "rda/eosinophil_expt.rda")
eo_norm <- sm(normalize_expt(eo, convert = "cpm", transform = "log2",
norm = "quant", filter = TRUE))
plt <- plot_pca(eo_norm, plot_labels = FALSE)$plot
pp(file = glue("images/eo_pca_normalized-v{ver}.pdf"), image = plt)
eo_nb <- sm(normalize_expt(eo, convert = "cpm", transform = "log2",
filter = TRUE, batch = "svaseq"))
plot_pca(eo_nb)$plot
5.3.2 DE of Eosinophil samples
5.3.2.1 Withouth sva
eo_de <- sm(all_pairwise(eo, model_batch = FALSE, filter = TRUE))eo_tables <- sm(combine_de_tables(
eo_de, keepers = keepers,
excel = glue::glue("excel/eosinophil_clinical_all_tables-v{ver}.xlsx")))
written <- write_xlsx(data = eo_tables[["data"]][[1]],
excel = glue::glue("excel/eosinophil_clinical_table-v{ver}.xlsx"))
eo_sig <- sm(extract_significant_genes(eo_tables, according_to = "deseq"))
written <- write_xlsx(data = eo_sig[["deseq"]][["ups"]][[1]],
excel = glue::glue("excel/eosinophil_clinical_sigup-v{ver}.xlsx"))
written <- write_xlsx(data = eo_sig[["deseq"]][["downs"]][[1]],
excel = glue::glue("excel/eosinophil_clinical_sigdown-v{ver}.xlsx"))
eo_pct_sig <- sm(extract_significant_genes(eo_tables, n = 200,
lfc = NULL, p = NULL, according_to = "deseq"))
written <- write_xlsx(data = eo_pct_sig[["deseq"]][["ups"]][[1]],
excel = glue::glue("excel/eosinophil_clinical_sigup_pct-v{ver}.xlsx"))
written <- write_xlsx(data = eo_pct_sig[["deseq"]][["downs"]][[1]],
excel = glue::glue("excel/eosinophil_clinical_sigdown_pct-v{ver}.xlsx"))
eo_cpm <- sm(normalize_expt(eo, convert = "cpm"))
written <- write_xlsx(data = exprs(eo_cpm),
excel = glue::glue("excel/eosinophil_cpm_before_batch-v{ver}.xlsx"))
eo_bcpm <- sm(normalize_expt(eo, filter = TRUE, batch = "svaseq", convert = "cpm"))
written <- write_xlsx(data = exprs(eo_bcpm),
excel = glue::glue("excel/eosinophil_cpm_after_batch-v{ver}.xlsx"))5.3.2.2 With sva
eo_de_sva <- sm(all_pairwise(eo, model_batch = "svaseq", filter = TRUE))eo_tables_sva <- sm(combine_de_tables(
eo_de_sva, keepers = keepers,
excel = glue::glue("excel/eosinophil_clinical_all_tables_sva-v{ver}.xlsx")))
eo_sig_sva <- sm(extract_significant_genes(
eo_tables_sva,
excel = glue::glue("excel/eosinophil_clinical_sig_tables_sva-v{ver}.xlsx"),
according_to = "deseq"))5.3.2.3 Eosinophil DE plots
## DESeq2 MA plot of failure / cure
eo_tables[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]]$plot
## DESeq2 Volcano plot of failure / cure
eo_tables[["plots"]][["fail_vs_cure"]][["deseq_vol_plots"]]$plot
## DESeq2 MA plot of failure / cure with sva
eo_tables_sva[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]]$plot
## DESeq2 Volcano plot of failure / cure with sva
eo_tables_sva[["plots"]][["fail_vs_cure"]][["deseq_vol_plots"]]$plot
5.3.2.4 Eosinophil ontology search
ups <- eo_sig[["deseq"]][["ups"]][["fail_vs_cure"]]
downs <- eo_sig[["deseq"]][["downs"]][["fail_vs_cure"]]
eo_up_gp <- simple_gprofiler(sig_genes = ups)## Performing gProfiler GO search of 103 genes against hsapiens.## GO search found 97 hits.## Performing gProfiler KEGG search of 103 genes against hsapiens.## KEGG search found 9 hits.## Performing gProfiler REAC search of 103 genes against hsapiens.## REAC search found 9 hits.## Performing gProfiler MI search of 103 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 103 genes against hsapiens.## TF search found 50 hits.## Performing gProfiler CORUM search of 103 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 103 genes against hsapiens.## HP search found 1 hits.eo_up_gp[["pvalue_plots"]][["bpp_plot_over"]]
eo_up_gp[["pvalue_plots"]][["mfp_plot_over"]]
eo_up_gp[["pvalue_plots"]][["reactome_plot_over"]]
eo_down_gp <- simple_gprofiler(downs)## Performing gProfiler GO search of 40 genes against hsapiens.## GO search found 0 hits.## Performing gProfiler KEGG search of 40 genes against hsapiens.## KEGG search found 0 hits.## Performing gProfiler REAC search of 40 genes against hsapiens.## REAC search found 0 hits.## Performing gProfiler MI search of 40 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 40 genes against hsapiens.## TF search found 0 hits.## Performing gProfiler CORUM search of 40 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 40 genes against hsapiens.## HP search found 0 hits.eo_down_gp[["pvalue_plots"]][["bpp_plot_over"]]## NULLeo_down_gp[["pvalue_plots"]][["mfp_plot_over"]]## NULLeo_down_gp[["pvalue_plots"]][["reactome_plot_over"]]## NULL5.3.2.5 Eosinophil MSigDB query
eo_up_goseq_msig <- goseq_msigdb(sig_genes = ups, signatures = broad_c7,
signature_category = "c7", length_db = hs_length)## Before conversion: 103, after conversion: 103.## Before conversion: 227921, after conversion: 35341.## Found 101 go_db genes and 103 length_db genes out of 103.eo_down_goseq_msig <- goseq_msigdb(sig_genes = downs, signatures = broad_c7,
signature_category = "c7", length_db = hs_length)## Before conversion: 40, after conversion: 39.## Before conversion: 227921, after conversion: 35341.## Found 34 go_db genes and 39 length_db genes out of 39.5.3.2.6 Plot of similar experiments
## Eosinophil genes with increased expression in the failed samples
## share genes with the following experiments
eo_up_goseq_msig[["pvalue_plots"]][["mfp_plot_over"]]
## Eosinophil genes with increased expression in the cured samples
## share genes with the following experiments
eo_down_goseq_msig[["pvalue_plots"]][["mfp_plot_over"]]
5.4 Biopsies
5.4.1 Evaluate Biopsy samples
biop <- subset_expt(hs_valid, subset = "typeofcells=='biopsy'") %>%
set_expt_conditions(fact = "clinicaloutcome") %>%
set_expt_batches(fact = "donor") %>%
set_expt_colors(colors = chosen_colors)## subset_expt(): There were 135, now there are 42 samples.save_result <- save(biop, file = "rda/biopsy_expt.rda")
biop_norm <- normalize_expt(biop, filter = TRUE, convert = "cpm",
transform = "log2", norm = "quant")## Removing 5799 low-count genes (14142 remaining).## transform_counts: Found 5 values equal to 0, adding 1 to the matrix.plt <- plot_pca(biop_norm, plot_labels = FALSE)$plot
pp(file = glue("images/biop_pca_normalized-v{ver}.pdf"), image = plt)
biop_nb <- sm(normalize_expt(biop, convert = "cpm", filter = TRUE,
transform = "log2", batch = "svaseq"))
plt <- plot_pca(biop_nb, plot_labels = FALSE)$plot
pp(file = glue("images/biop_pca_normalized_svaseq-v{ver}.pdf"), image = plt)
5.4.2 DE of Biopsy samples
5.4.2.1 Without sva
biop_de <- sm(all_pairwise(biop, model_batch = FALSE, filter = TRUE))biop_tables <- combine_de_tables(biop_de, keepers = keepers,
excel = glue::glue("excel/biopsy_clinical_all_tables-v{ver}.xlsx"))## Deleting the file excel/biopsy_clinical_all_tables-v202106.xlsx before writing the tables.written <- write_xlsx(data = biop_tables[["data"]][[1]],
excel = glue::glue("excel/biopsy_clinical_table-v{ver}.xlsx"))
biop_sig <- extract_significant_genes(biop_tables, according_to = "deseq")
##written <- write_xlsx(data = biop_sig[["deseq"]][["ups"]][[1]],
## excel = glue::glue("excel/biopsy_clinical_sigup-v{ver}.xlsx"))
written <- write_xlsx(data = biop_sig[["deseq"]][["downs"]][[1]],
excel = glue::glue("excel/biopsy_clinical_sigdown-v{ver}.xlsx"))
biop_pct_sig <- extract_significant_genes(biop_tables, n = 200, lfc = NULL, p = NULL, according_to = "deseq")## Getting the top and bottom 200 genes.written <- write_xlsx(data = biop_pct_sig[["deseq"]][["ups"]][[1]],
excel = glue::glue("excel/biopsy_clinical_sigup_pct-v{ver}.xlsx"))
written <- write_xlsx(data = biop_pct_sig[["deseq"]][["downs"]][[1]],
excel = glue::glue("excel/biopsy_clinical_sigdown_pct-v{ver}.xlsx"))
biop_cpm <- sm(normalize_expt(biop, convert = "cpm"))
written <- write_xlsx(data = exprs(biop_cpm),
excel = glue::glue("excel/biopsy_cpm_before_batch-v{ver}.xlsx"))
biop_bcpm <- sm(normalize_expt(biop, filter = TRUE, batch = "svaseq", convert = "cpm"))
written <- write_xlsx(data = exprs(biop_bcpm),
excel = glue::glue("excel/biopsy_cpm_after_batch-v{ver}.xlsx"))5.4.2.2 with sva
biop_de_sva <- sm(all_pairwise(biop, model_batch = "svaseq", filter = TRUE))biop_tables_sva <- sm(combine_de_tables(
biop_de_sva, keepers = keepers,
excel = glue::glue("excel/biopsy_clinical_all_tables_sva-v{ver}.xlsx")))
biop_sig_sva <- sm(extract_significant_genes(
biop_tables_sva,
excel = glue::glue("excel/biopsy_clinical_sig_tables_sva-v{ver}.xlsx"),
according_to = "deseq"))5.4.2.3 Biopsy DE plots
## DESeq2 MA plot of failure / cure
biop_tables[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]]$plot
## DESeq2 Volcano plot of failure / cure
biop_tables[["plots"]][["fail_vs_cure"]][["deseq_vol_plots"]]$plot
## DESeq2 MA plot of failure / cure
biop_tables_sva[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]]$plot
## DESeq2 Volcano plot of failure / cure
biop_tables_sva[["plots"]][["fail_vs_cure"]][["deseq_vol_plots"]]$plot
7 Monocytes by visit
- Can you please share with us a PCA (SVA and non-SVA) of the monocytes of the TMRC3 project, but labeling them based on the visit (V1, V2, V3)?
- Can you please share DE lists of V1 vs V2, V1 vs V3, V1 vs. V2+V3 and V2 vs V3?
visit_colors <- chosen_colors <- c("#D95F02", "#7570B3", "#1B9E77")
names(visit_colors) <- c(1, 2, 3)
mono_visit <- subset_expt(hs_valid, subset = "typeofcells=='monocytes'") %>%
set_expt_conditions(fact = "visitnumber") %>%
set_expt_batches(fact = "clinicaloutcome") %>%
set_expt_colors(colors = chosen_colors)## subset_expt(): There were 135, now there are 27 samples.mono_visit_norm <- normalize_expt(mono_visit, filter = TRUE, norm = "quant", convert = "cpm",
transform = "log2")## Removing 8915 low-count genes (11026 remaining).## transform_counts: Found 9 values equal to 0, adding 1 to the matrix.mono_visit_pca <- plot_pca(mono_visit_norm)
pp(file = "images/monocyte_by_visit.png", image = mono_visit_pca$plot)
mono_visit_nb <- normalize_expt(mono_visit, filter = TRUE, convert = "cpm",
batch = "svaseq", transform = "log2")## Removing 8915 low-count genes (11026 remaining).## batch_counts: Before batch/surrogate estimation, 1404 entries are x==0: 0%.## batch_counts: Before batch/surrogate estimation, 17611 entries are 0<x<1: 6%.## Setting 441 low elements to zero.## transform_counts: Found 441 values equal to 0, adding 1 to the matrix.mono_visit_nb_pca <- plot_pca(mono_visit_nb)
pp(file = "images/monocyte_by_visit_nb.png", image = mono_visit_nb_pca$plot)
table(pData(mono_visit_norm)$batch)##
## cure failure lost
## 9 12 6keepers <- list(
"second_vs_first" = c("c2", "c1"),
"third_vs_second" = c("c3", "c2"),
"third_vs_first" = c("c3", "c1"))
mono_visit_de <- all_pairwise(mono_visit, model_batch = "svaseq", filter = TRUE)## batch_counts: Before batch/surrogate estimation, 1404 entries are x==0: 0%.## Plotting a PCA before surrogate/batch inclusion.## Using svaseq to visualize before/after batch inclusion.## Performing a test normalization with: raw## Removing 0 low-count genes (11026 remaining).## batch_counts: Before batch/surrogate estimation, 1404 entries are x==0: 0%.## batch_counts: Before batch/surrogate estimation, 17611 entries are 0<x<1: 6%.## Setting 441 low elements to zero.## transform_counts: Found 441 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.mono_visit_tables <- combine_de_tables(
mono_visit_de,
keepers = keepers,
excel = glue::glue("excel/mono_visit_tables-v{ver}.xlsx"))new_factor <- as.character(pData(mono_visit)[["visitnumber"]])
not_one_idx <- new_factor != 1
new_factor[not_one_idx] <- "not_1"
mono_one_vs <- set_expt_conditions(mono_visit, new_factor)
mono_one_vs_de <- all_pairwise(mono_one_vs, model_batch = "svaseq", filter = TRUE)## batch_counts: Before batch/surrogate estimation, 1404 entries are x==0: 0%.## Plotting a PCA before surrogate/batch inclusion.## Using svaseq to visualize before/after batch inclusion.## Performing a test normalization with: raw## Removing 0 low-count genes (11026 remaining).## batch_counts: Before batch/surrogate estimation, 1404 entries are x==0: 0%.## batch_counts: Before batch/surrogate estimation, 17611 entries are 0<x<1: 6%.## Setting 436 low elements to zero.## transform_counts: Found 436 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.mono_one_vs_tables <- combine_de_tables(
mono_one_vs_de,
excel = glue::glue("excel/mono_one_vs_tables-v{ver}.xlsx"))8 Test TSP
In writing the following, I quickly realized that tspair was not joking when it said it is intended for small numbers of genes. For a full expressionset of human data it is struggling. I like the idea, it may prove worth while to spend some time optimizing the package so that it is more usable.
expt <- hs_clinical_nobiop
simple_tsp <- function(expt, column = "condition") {
facts <- levels(as.factor(pData(expt)[[column]]))
retlist <- list()
if (length(facts) < 2) {
stop("This requires factors with at least 2 levels.")
} else if (length(facts) == 2) {
retlist <- simple_tsp_pair(expt, column = column)
} else {
for (first in 1:(length(facts) - 1)) {
for (second in 2:(length(facts))) {
if (first < second) {
name <- glue::glue("{facts[first]}_vs_{facts[second]}")
message("Starting ", name, ".")
substring <- glue::glue("{column}=='{facts[first]}'|{column}=='{facts[second]}'")
subby <- subset_expt(expt, subset=as.character(substring))
retlist[[name]] <- simple_tsp_pair(subby, column = column)
}
}
}
}
}
simple_tsp_pair <- function(subby, column = "condition", repetitions = 50) {
tsp_input <- subby[["expressionset"]]
tsp_output <- tspcalc(tsp_input, column)
tsp_scores <- tspsig(tsp_input, column, B = repetitions)
}
tsp1 <- tspcalc(tsp_input, "condition")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))
}## If you wish to reproduce this exact build of hpgltools, invoke the following:## > git clone http://github.com/abelew/hpgltools.git## > git reset 72947fcc6afe09da22d71967059edd84e3063341## This is hpgltools commit: Tue Jun 1 15:57:56 2021 -0400: 72947fcc6afe09da22d71967059edd84e3063341## Saving to tmrc3_02sample_estimation_v202106.rda.xztmp <- loadme(filename = savefile)---
title: "TMRC3 Comprehensive Data Analysis: 202106"
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)
tt <- sm(devtools::load_all("~/hpgltools"))
knitr::opts_knit$set(progress = TRUE,
                     verbose = TRUE,
                     width = 120,
                     echo = TRUE)
knitr::opts_chunk$set(error = TRUE,
                      fig.width = 12,
                      fig.height = 12,
                      dpi = 96)
old_options <- options(digits = 4,
                       stringsAsFactors = FALSE,
                       knitr.duplicate.label = "allow")
ggplot2::theme_set(ggplot2::theme_bw(base_size = 12))
ver <- "202106"
rundate <- format(Sys.Date(), format = "%Y%m%d")

rmd_file <- glue::glue("tmrc3_02sample_estimation_v{ver}.Rmd")
savefile <- gsub(pattern = "\\.Rmd", replace = "\\.rda\\.xz", x = rmd_file)
```

# Annotation

We take the annotation data from ensembl's biomart instance.  The genome which
was used to map the data was hg38 revision 100.  My default when using biomart is
to load the data from 1 year before the current date.

```{r hs_annot}
hs_annot <- sm(load_biomart_annotations(year = "2020"))
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)
```

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

# Introduction

This document is intended to provide an overview of TMRC3 samples which have
been sequenced.  It includes some plots and analyses showing the relationships
among the samples as well as some differential analyses when possible.

# Sample Estimation

## Generate expressionsets

The sample sheet is copied from our shared online sheet and updated with each release
of sequencing data.

```{r samplesheet}
samplesheet <- "sample_sheets/tmrc3_samples_20210601.xlsx"
```

### Hisat2 expressionsets

The first thing to note is the large range in coverage.  There are multiple
samples with coverage which is too low to use.  These will be removed shortly.

In the following block I immediately exclude any non-coding reads as well.

```{r all_new_hisat2}
## Create the expressionset and immediately pass it to a filter
## removing the non protein coding genes.
sanitize_columns <- c("visitnumber", "clinicaloutcome", "donor",
                      "typeofcells", "clinicalpresentation",
                      "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")

levels(pData(hs_expt[["expressionset"]])[["visitnumber"]]) <- list(
    '0' = "notapplicable", '1' = 1, '2' = 2, '3' = 3)
```

Split this data into CDS and lncRNA.  Oh crap in order to do that I need to recount the data.
Running now (20210518)

```{r lnc_cds}
## lnc_expt <- create_expt(samplesheet,
##                         file_column = "hg38100lncfile",
##                         gene_info = hs_annot)
```

#### Initial metrics

Once the data was loaded, there are a couple of metrics which may be plotted immediately.

```{r initial_metrics}
nonzero <- plot_nonzero(hs_expt)
nonzero$plot

ncrna_lost_df <- as.data.frame(pData(hs_expt)[["ncrna_lost"]])
rownames(ncrna_lost_df) <- rownames(pData(hs_expt))
colnames(ncrna_lost_df) <- "ncrna_lost"

tmpdf <- merge(nonzero$table, ncrna_lost_df, by = "row.names")
rownames(tmpdf) <- tmpdf[["Row.names"]]
tmpdf[["Row.names"]] <- NULL

ggplot(tmpdf, aes(x=ncrna_lost, y=nonzero_genes)) +
  ggplot2::geom_point() +
  ggplot2::ggtitle("Nonzero genes with respect to percent counts 
lost when ncRNA was removed.")
```

Najib doesn't want this plot, but I am using it to check new samples,
so will hide it from general use.

```{r libsize}
libsize <- plot_libsize(hs_expt)
libsize$plot
```

## Minimum coverage sample filtering

I arbitrarily chose 11,000 non-zero genes as a minimum.  We may
want this to be higher.

```{r hisat2_write, fig.show = "hide"}
hs_valid <- subset_expt(hs_expt, nonzero = 11000)

valid_write <- sm(write_expt(hs_valid, excel = glue("excel/hs_valid-v{ver}.xlsx")))
```

# Project Aims

The project seeks to determine the relationship of the innate immune response
and inflammatory signaling to the clinical outcome of antileishmanial drug
treatment. We will test the hypothesis that the profile of innate immune cell
activation and their dynamics through the course of treatment differ between CL
patients with prospectively determined therapeutic cure or failure.

This will be achieved through the characterization of the in vivo dynamics of
blood-derived monocyte, neutrophil and eosinophil transcriptome before, during
and at the end of treatment in CL patients. Cell-type specific transcriptomes,
composite signatures and time-response expression profiles will be contrasted
among patients with therapeutic cure or failure.

## Preparation

To address these, I added to the end of the sample sheet columns named
'condition', 'batch', 'donor', and 'time'.  These are filled in with shorthand
values according to the above.

## Global view

Before addressing the questions explicitly by subsetting the data, I want to get
a look at the samples as they are.

```{r pre_questions}
new_names <- pData(hs_valid)[["samplename"]]
hs_valid <- hs_valid %>%
  set_expt_batches(fact = "cellssource") %>%
  set_expt_conditions(fact = "typeofcells") %>%
  set_expt_samplenames(newnames = new_names)

all_norm <- sm(normalize_expt(hs_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")
pp(file = glue("images/tmrc3_pca_nolabels-v{ver}.png"), image = all_pca$plot)

write.csv(all_pca$table, file = "coords/hs_donor_pca_coords.csv")
plot_corheat(all_norm, plot_title = "Heirarchical clustering:
         cell types")$plot
```

## Examine samples relevant to clinical outcome

Now let us consider only the samples for which we have a clinical outcome.
These fall primarily into either 'cured' or 'failed', but some people have not
yet returned to the clinic after the first or second visit.  These are deemed
'lost'.

```{r all_clinical}
hs_clinical <- hs_valid %>%
  set_expt_conditions(fact = "clinicaloutcome") %>%
  set_expt_batches(fact = "typeofcells") %>%
  subset_expt(subset = "typeofcells!='pbmcs'&typeofcells!='macrophages'")

chosen_colors <- c("#D95F02", "#7570B3", "#1B9E77", "#FF0000", "#FF0000")
names(chosen_colors) <- c("cure", "failure", "lost", "null", "notapplicable")
hs_clinical <- set_expt_colors(hs_clinical, colors = chosen_colors)

newnames <- make.names(pData(hs_clinical)[["samplename"]], unique = TRUE)
hs_clinical <- set_expt_samplenames(hs_clinical, newnames = newnames)

hs_clinical_norm <- sm(normalize_expt(hs_clinical, filter = TRUE, transform = "log2",
                                      convert = "cpm", norm = "quant"))
clinical_pca <- plot_pca(hs_clinical_norm, plot_labels = FALSE,
                         size_column = "visitnumber", cis = NULL,
                         plot_title = "PCA - clinical samples")
pp(file = glue("images/all_clinical_nobatch_pca-v{ver}.png"), image = clinical_pca$plot,
   height = 8, width = 20)
```

### Repeat without the biopsy samples

```{r ibid_nobiopsy}
hs_clinical_nobiop <- hs_clinical %>%
  subset_expt(subset = "typeofcells!='biopsy'")

hs_clinical_nobiop_norm <- sm(normalize_expt(hs_clinical_nobiop, filter = TRUE, transform = "log2",
                                             convert = "cpm", norm = "quant"))
clinical_nobiop_pca <- plot_pca(hs_clinical_nobiop_norm, plot_labels = FALSE, cis = NULL,
                                plot_title = "PCA - clinical samples without biopsies")
pp(file = glue("images/all_clinical_nobiop_nobatch_pca-v{ver}.png"),
   image = clinical_nobiop_pca$plot)
```

### Attempt to correct for the surrogate variables

At this time we have two primary data structures of interest: hs_clinical and hs_clinical_nobiop

```{r clinical_sva}
hs_clinical_nb <- normalize_expt(hs_clinical, filter = TRUE, batch = "svaseq",
                                 transform = "log2", convert = "cpm")
clinical_batch_pca <- plot_pca(hs_clinical_nb, plot_labels = FALSE, cis = NULL,
                               size_column = "visitnumber", plot_title = "PCA - clinical samples")
clinical_batch_pca$plot

hs_clinical_nobiop_nb <- sm(normalize_expt(hs_clinical_nobiop, filter = TRUE, batch = "svaseq",
                                           transform = "log2", convert = "cpm"))
clinical_nobiop_batch_pca <- plot_pca(hs_clinical_nobiop_nb,
                                      plot_title = "PCA - clinical samples without biopsies",
                                      plot_labels = FALSE)
pp(file = "images/clinical_batch.png", image = clinical_nobiop_batch_pca$plot)
test <- plot_pca(hs_clinical_nobiop_nb, size_column = "visitnumber",
                 plot_title = "PCA - clinical samples without biopsies",
                 plot_labels = FALSE)
test$plot

clinical_nobiop_batch_tsne <- plot_tsne(hs_clinical_nobiop_nb,
                                        plot_title = "tSNE - clinical samples without biopsies",
                                        plot_labels = FALSE)
clinical_nobiop_batch_tsne$plot
```

#### Look at remaining variance with variancePartition

```{r variance_partition}
test <- simple_varpart(hs_clinical_nobiop)
test$partition_plot
```

## Perform DE of the clinical samples cure vs. fail

```{r clinical_de, fig.show="hide"}
individual_celltypes <- subset_expt(hs_clinical_nobiop, subset="condition!='lost'")
hs_clinic_de <- sm(all_pairwise(individual_celltypes, model_batch = "svaseq", filter = TRUE))

hs_clinic_table <- sm(combine_de_tables(
    hs_clinic_de,
    excel = glue::glue("excel/individual_celltypes_table-v{ver}.xlsx")))

hs_clinic_sig <- sm(extract_significant_genes(
    hs_clinic_table,
    excel = glue::glue("excel/individual_celltypes_sig-v{ver}.xlsx")))

hs_clinic_sig[["summary_df"]]
```

```{r de_heatmap}
hs_clinic_de[["comparison"]][["heat"]]
```

### Perform LRT with the clinical samples

I am not sure if we have enough samples across the three visit to
completely work as well as we would like, but there is only 1 way to
find out!  Now that I think about it, one thing which might be awesome
is to use cell type as an interacting factor...

#### With biopsy samples

I figure this might be a place where the biopsy samples might prove useful.

```{r lrt_test}
clinical_nolost <- subset_expt(hs_clinical, subset="condition!='lost'")
lrt_visit_clinical_test <- deseq_lrt(clinical_nolost, transform = "vst",
                                     interactor_column = "visitnumber",
                                     interest_column = "clinicaloutcome")
lrt_visit_clinical_test[["favorite_genes"]]

lrt_celltype_clinical_test <- deseq_lrt(clinical_nolost, transform = "vst",
                                        interactor_column = "typeofcells",
                                        interest_column = "clinicaloutcome")
lrt_celltype_clinical_test[["favorite_genes"]]
```

### Look at only the differential genes

A good suggestion from Theresa was to examine only the most variant
genes from failure vs. cure and see how they change the clustering/etc
results.  This is my attempt to address this query.

```{r small_explore}
hs_clinic_topn <- sm(extract_significant_genes(hs_clinic_table, n = 100))
table <- "failure_vs_cure"
wanted <- rbind(hs_clinic_topn[["deseq"]][["ups"]][[table]],
                hs_clinic_topn[["deseq"]][["downs"]][[table]])

small_expt <- exclude_genes_expt(hs_clinical_nobiop, ids = rownames(wanted), method = "keep")
small_norm <- sm(normalize_expt(small_expt, transform = "log2", convert = "cpm",
                                norm = "quant", filter = TRUE))
plot_pca(small_norm)$plot

small_nb <- normalize_expt(small_expt, transform = "log2", convert = "cpm",
                           batch = "svaseq", norm = "quant", filter = TRUE)
plot_pca(small_nb)$plot
```

```{r clinical_plot}
## DESeq2 MA plot of failure / cure
hs_clinic_table[["plots"]][["failure_vs_cure"]][["deseq_ma_plots"]]$plot

## DESeq2 Volcano plot of failure / cure
hs_clinic_table[["plots"]][["failure_vs_cure"]][["deseq_vol_plots"]]$plot
```

### g:Profiler results using the significant up and down genes

```{r perform_gprofiler}
ups <- hs_clinic_sig[["deseq"]][["ups"]][[1]]
downs <- hs_clinic_sig[["deseq"]][["downs"]][[1]]

hs_clinic_gprofiler_ups <- simple_gprofiler(ups)
hs_clinic_gprofiler_ups[["pvalue_plots"]][["bpp_plot_over"]]
hs_clinic_gprofiler_ups[["pvalue_plots"]][["mfp_plot_over"]]
hs_clinic_gprofiler_ups[["pvalue_plots"]][["reactome_plot_over"]]

##hs_try2 <- simple_gprofiler2(ups)

hs_clinic_gprofiler_downs <- simple_gprofiler(downs)
hs_clinic_gprofiler_downs[["pvalue_plots"]][["bpp_plot_over"]]
hs_clinic_gprofiler_downs[["pvalue_plots"]][["mfp_plot_over"]]
hs_clinic_gprofiler_downs[["pvalue_plots"]][["reactome_plot_over"]]
```

## Perform GSVA on the clinical samples

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

broad_c7 <- GSEABase::getGmt("reference/msigdb/c7.all.v7.2.entrez.gmt",
                             collectionType = GSEABase::BroadCollection(category = "c7"),
                             geneIdType = GSEABase::EntrezIdentifier())
hs_celltype_gsva_c7 <- sm(simple_gsva(individual_celltypes, signatures = broad_c7,
                                      msig_xml = "reference/msigdb_v7.2.xml", cores = 10))
hs_celltype_gsva_c7_sig <- sm(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"]]
```

# Individual Cell types

The following blocks split the samples into a few groups by sample type and look
at the distributions between them.

## Implementation details

Get top/bottom n genes for each cell type, using clinical outcome as the factor of interest.
For the moment, use sva for the DE analysis.
Provide cpms for the top/bottom n genes.

Start with top/bottom 200.
Perform default logFC and p-value as well.

### Shared contrasts

Here is the contrast we will use throughput, I am leaving open the option to add more.

```{r keepers}
keepers <- list(
  "fail_vs_cure" = c("failure", "cure"))
```

## Monocytes

### Evaluate Monocyte samples

```{r monocytes}
mono <- subset_expt(hs_valid, subset = "typeofcells=='monocytes'") %>%
  set_expt_conditions(fact = "clinicaloutcome") %>%
  set_expt_batches(fact = "donor") %>%
  set_expt_colors(colors = chosen_colors)
## FIXME set_expt_colors should speak up if there are mismatches here!!!

save_result <- save(mono, file = "rda/monocyte_expt.rda")
mono_norm <- normalize_expt(mono, convert = "cpm", filter = TRUE,
                            transform = "log2", norm = "quant")
plt <- plot_pca(mono_norm, plot_labels = FALSE)$plot
pp(file = glue("images/mono_pca_normalized-v{ver}.pdf"), image = plt)

mono_nb <- normalize_expt(mono, convert = "cpm", filter = TRUE,
                          transform = "log2", batch = "svaseq")
plt <- plot_pca(mono_nb, plot_labels = FALSE)$plot
pp(file = glue("images/mono_pca_normalized_batch-v{ver}.pdf"), image = plt)
```

### DE of Monocyte samples

#### Without sva

```{r de_monocyte, fig.show = "hide"}
mono_de <- sm(all_pairwise(mono, model_batch = FALSE, filter = TRUE))
mono_tables <- sm(combine_de_tables(
    mono_de, keepers = keepers,
    excel = glue::glue("excel/monocyte_clinical_all_tables-v{ver}.xlsx")))
written <- write_xlsx(data = mono_tables[["data"]][[1]],
                      excel = glue::glue("excel/monocyte_clinical_table-v{ver}.xlsx"))
mono_sig <- sm(extract_significant_genes(mono_tables, according_to = "deseq"))
written <- write_xlsx(data = mono_sig[["deseq"]][["ups"]][[1]],
                      excel = glue::glue("excel/monocyte_clinical_sigup-v{ver}.xlsx"))
written <- write_xlsx(data = mono_sig[["deseq"]][["downs"]][[1]],
                      excel = glue::glue("excel/monocyte_clinical_sigdown-v{ver}.xlsx"))

mono_pct_sig <- sm(extract_significant_genes(mono_tables, n = 200,
                                             lfc = NULL, p = NULL, according_to = "deseq"))
written <- write_xlsx(data = mono_pct_sig[["deseq"]][["ups"]][[1]],
                      excel = glue::glue("excel/monocyte_clinical_sigup_pct-v{ver}.xlsx"))
written <- write_xlsx(data = mono_pct_sig[["deseq"]][["downs"]][[1]],
                      excel = glue::glue("excel/monocyte_clinical_sigdown_pct-v{ver}.xlsx"))
mono_sig$summary_df

## Print out a table of the cpm values for other explorations.
mono_cpm <- sm(normalize_expt(mono, convert = "cpm"))
written <- write_xlsx(data = exprs(mono_cpm),
                      excel = glue::glue("excel/monocyte_cpm_before_batch-v{ver}.xlsx"))
mono_bcpm <- sm(normalize_expt(mono, filter = TRUE, convert = "cpm", batch = "svaseq"))
written <- write_xlsx(data = exprs(mono_bcpm),
                      excel = glue::glue("excel/monocyte_cpm_after_batch-v{ver}.xlsx"))
```

#### With sva

```{r de_mono_sva, fig.show = "hide"}
mono_de_sva <- sm(all_pairwise(mono, model_batch = "svaseq", filter = TRUE))
mono_tables_sva <- sm(combine_de_tables(
    mono_de_sva, keepers = keepers,
    excel = glue::glue("excel/monocyte_clinical_all_tables_sva-v{ver}.xlsx")))
mono_sig_sva <- sm(extract_significant_genes(
    mono_tables_sva,
    excel = glue::glue("excel/monocyte_clinical_sig_tables_sva-v{ver}.xlsx"),
    according_to = "deseq"))
```

#### Monocyte DE plots

First print out the DE plots without and then with sva estimates.

```{r mono_de_plots}
## DESeq2 MA plot of failure / cure
mono_tables[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]]$plot

## DESeq2 Volcano plot of failure / cure
mono_tables[["plots"]][["fail_vs_cure"]][["deseq_vol_plots"]]$plot

## DESeq2 MA plot of failure / cure with svaseq
mono_tables_sva[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]]$plot

## DESeq2 Volcano plot of failure / cure with svaseq
mono_tables_sva[["plots"]][["fail_vs_cure"]][["deseq_vol_plots"]]$plot
```

#### Monocyte ontology search

```{r mono_gprofiler}
ups <- mono_sig[["deseq"]][["ups"]][["fail_vs_cure"]]
downs <- mono_sig[["deseq"]][["downs"]][["fail_vs_cure"]]

mono_up_gp <- simple_gprofiler(sig_genes = ups)
mono_up_gp[["pvalue_plots"]][["bpp_plot_over"]]
mono_up_gp[["pvalue_plots"]][["mfp_plot_over"]]
mono_up_gp[["pvalue_plots"]][["reactome_plot_over"]]

mono_down_gp <- simple_gprofiler(sig_genes = downs)
mono_down_gp[["pvalue_plots"]][["bpp_plot_over"]]
mono_down_gp[["pvalue_plots"]][["mfp_plot_over"]]
mono_down_gp[["pvalue_plots"]][["reactome_plot_over"]]
```

#### Monocyte MSigDB query

```{r msig_mono_goseq, fig.show = "hide"}
broad_c7 <- GSEABase::getGmt("reference/msigdb/c7.all.v7.2.entrez.gmt",
                             collectionType = GSEABase::BroadCollection(category = "c7"),
                             geneIdType = GSEABase::EntrezIdentifier())

mono_up_goseq_msig <- goseq_msigdb(sig_genes = ups, signatures = broad_c7,
                                   signature_category = "c7", length_db = hs_length)

mono_down_goseq_msig <- goseq_msigdb(sig_genes = downs, signatures = broad_c7,
                                     signature_category = "c7", length_db = hs_length)
```

#### Plot of similar experiments

```{r msig_plots}
## Monocyte genes with increased expression in the failed samples
## share genes with the following experiments
mono_up_goseq_msig[["pvalue_plots"]][["mfp_plot_over"]]

## Monocyte genes with increased expression in the cured samples
## share genes with the following experiments
mono_down_goseq_msig[["pvalue_plots"]][["mfp_plot_over"]]
```

### Evaluate Neutrophil samples

```{r neutrophils}
neut <- subset_expt(hs_valid, subset = "typeofcells=='neutrophils'") %>%
  set_expt_conditions(fact = "clinicaloutcome") %>%
  set_expt_batches(fact = "donor") %>%
  set_expt_colors(colors = chosen_colors)

save_result <- save(neut, file = "rda/neutrophil_expt.rda")
neut_norm <- sm(normalize_expt(neut, convert = "cpm", filter = TRUE, transform = "log2"))
plt <- plot_pca(neut_norm, plot_labels = FALSE)$plot
pp(file = glue("images/neut_pca_normalized-v{ver}.pdf"), image = plt)

neut_nb <- sm(normalize_expt(neut, convert = "cpm", filter = TRUE,
                             transform = "log2", batch = "svaseq"))
plt <- plot_pca(neut_nb, plot_labels = FALSE)$plot
pp(file = glue("images/neut_pca_normalized_svaseq-v{ver}.pdf"), image = plt)
```

### DE of Netrophil samples

#### Without sva

```{r neutrophil_de, fig.show = "hide"}
neut_de <- sm(all_pairwise(neut, model_batch = FALSE, filter = TRUE))
neut_tables <- sm(combine_de_tables(
    neut_de, keepers = keepers,
    excel = glue::glue("excel/neutrophil_clinical_all_tables-v{ver}.xlsx")))
written <- write_xlsx(data = neut_tables[["data"]][[1]],
                      excel = glue::glue("excel/neutrophil_clinical_table-v{ver}.xlsx"))
neut_sig <- sm(extract_significant_genes(neut_tables, according_to = "deseq"))
written <- write_xlsx(data = neut_sig[["deseq"]][["ups"]][[1]],
                      excel = glue::glue("excel/neutrophil_clinical_sigup-v{ver}.xlsx"))
written <- write_xlsx(data = neut_sig[["deseq"]][["downs"]][[1]],
                      excel = glue::glue("excel/neutrophil_clinical_sigdown-v{ver}.xlsx"))

neut_pct_sig <- sm(extract_significant_genes(neut_tables, n = 200, lfc = NULL,
                                             p = NULL, according_to = "deseq"))
written <- write_xlsx(data = neut_sig[["deseq"]][["ups"]][[1]],
                      excel = glue::glue("excel/neutrophil_clinical_sigup_pct-v{ver}.xlsx"))
written <- write_xlsx(data = neut_sig[["deseq"]][["downs"]][[1]],
                      excel = glue::glue("excel/neutrophil_clinical_sigdown_pct-v{ver}.xlsx"))
neut_cpm <- sm(normalize_expt(neut, convert = "cpm"))
written <- write_xlsx(data = exprs(neut_cpm),
                      excel = glue::glue("excel/neutrophil_cpm_before_batch-v{ver}.xlsx"))
neut_bcpm <- sm(normalize_expt(neut, filter = TRUE, batch = "svaseq", convert = "cpm"))
written <- write_xlsx(data = exprs(neut_bcpm),
                      excel = glue::glue("excel/neutrophil_cpm_after_batch-v{ver}.xlsx"))
```

#### With sva

```{r de_neut_sva, fig.show = "hide"}
neut_de_sva <- sm(all_pairwise(neut, model_batch = "svaseq", filter = TRUE))
neut_tables_sva <- sm(combine_de_tables(
    neut_de_sva, keepers = keepers,
    excel = glue::glue("excel/neutrophil_clinical_all_tables_sva-v{ver}.xlsx")))
neut_sig_sva <- sm(extract_significant_genes(
    neut_tables_sva,
    excel = glue::glue("excel/neutrophil_clinical_sig_tables_sva-v{ver}.xlsx"),
    according_to = "deseq"))
```

#### Neutrophil DE plots

```{r neut_de_plots}
## DESeq2 MA plot of failure / cure
neut_tables[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]]$plot

## DESeq2 Volcano plot of failure / cure
neut_tables[["plots"]][["fail_vs_cure"]][["deseq_vol_plots"]]$plot

## DESeq2 MA plot of failure / cure with sva
neut_tables_sva[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]]$plot

## DESeq2 Volcano plot of failure / cure with sva
neut_tables_sva[["plots"]][["fail_vs_cure"]][["deseq_vol_plots"]]$plot
```

#### Neutrophil ontology search

```{r neut_gp}
ups <- neut_sig[["deseq"]][["ups"]][["fail_vs_cure"]]
downs <- neut_sig[["deseq"]][["downs"]][["fail_vs_cure"]]

neut_up_gp <- simple_gprofiler(sig_genes = ups)
neut_up_gp[["pvalue_plots"]][["bpp_plot_over"]]
neut_up_gp[["pvalue_plots"]][["mfp_plot_over"]]
neut_up_gp[["pvalue_plots"]][["reactome_plot_over"]]

neut_down_gp <- simple_gprofiler(downs)
neut_down_gp[["pvalue_plots"]][["bpp_plot_over"]]
neut_down_gp[["pvalue_plots"]][["mfp_plot_over"]]
neut_down_gp[["pvalue_plots"]][["reactome_plot_over"]]
```

#### Neutrophil GSVA query

```{r msig_neut_goseq, fig.show = "hide"}
neut_up_goseq_msig <- goseq_msigdb(sig_genes = ups, signatures = broad_c7,
                                   signature_category = "c7", length_db = hs_length)

neut_down_goseq_msig <- goseq_msigdb(sig_genes = downs, signatures = broad_c7,
                                     signature_category = "c7", length_db = hs_length)
```

#### Plot of similar experiments

```{r msig_plots_neut}
## Neutrophil genes with increased expression in the failed samples
## share genes with the following experiments
neut_up_goseq_msig[["pvalue_plots"]][["mfp_plot_over"]]

## Neutrophil genes with increased expression in the cured samples
## share genes with the following experiments
neut_down_goseq_msig[["pvalue_plots"]][["mfp_plot_over"]]
```

## Eosinophils

### Evaluate Eosinophil samples

```{r eosinophils}
eo <- subset_expt(hs_valid, subset = "typeofcells=='eosinophils'") %>%
  set_expt_conditions(fact = "clinicaloutcome") %>%
  set_expt_batches(fact = "donor") %>%
  set_expt_colors(colors = chosen_colors)

save_result <- save(eo, file = "rda/eosinophil_expt.rda")
eo_norm <- sm(normalize_expt(eo, convert = "cpm", transform = "log2",
                             norm = "quant", filter = TRUE))
plt <- plot_pca(eo_norm, plot_labels = FALSE)$plot
pp(file = glue("images/eo_pca_normalized-v{ver}.pdf"), image = plt)

eo_nb <- sm(normalize_expt(eo, convert = "cpm", transform = "log2",
                           filter = TRUE, batch = "svaseq"))
plot_pca(eo_nb)$plot
```

### DE of Eosinophil samples

#### Withouth sva

```{r eosinophil_de, fig.show = "hide"}
eo_de <- sm(all_pairwise(eo, model_batch = FALSE, filter = TRUE))
eo_tables <- sm(combine_de_tables(
    eo_de, keepers = keepers,
    excel = glue::glue("excel/eosinophil_clinical_all_tables-v{ver}.xlsx")))
written <- write_xlsx(data = eo_tables[["data"]][[1]],
                      excel = glue::glue("excel/eosinophil_clinical_table-v{ver}.xlsx"))
eo_sig <- sm(extract_significant_genes(eo_tables, according_to = "deseq"))
written <- write_xlsx(data = eo_sig[["deseq"]][["ups"]][[1]],
                      excel = glue::glue("excel/eosinophil_clinical_sigup-v{ver}.xlsx"))
written <- write_xlsx(data = eo_sig[["deseq"]][["downs"]][[1]],
                      excel = glue::glue("excel/eosinophil_clinical_sigdown-v{ver}.xlsx"))

eo_pct_sig <- sm(extract_significant_genes(eo_tables, n = 200,
                                           lfc = NULL, p = NULL, according_to = "deseq"))
written <- write_xlsx(data = eo_pct_sig[["deseq"]][["ups"]][[1]],
                      excel = glue::glue("excel/eosinophil_clinical_sigup_pct-v{ver}.xlsx"))
written <- write_xlsx(data = eo_pct_sig[["deseq"]][["downs"]][[1]],
                      excel = glue::glue("excel/eosinophil_clinical_sigdown_pct-v{ver}.xlsx"))

eo_cpm <- sm(normalize_expt(eo, convert = "cpm"))
written <- write_xlsx(data = exprs(eo_cpm),
                      excel = glue::glue("excel/eosinophil_cpm_before_batch-v{ver}.xlsx"))
eo_bcpm <- sm(normalize_expt(eo, filter = TRUE, batch = "svaseq", convert = "cpm"))
written <- write_xlsx(data = exprs(eo_bcpm),
                      excel = glue::glue("excel/eosinophil_cpm_after_batch-v{ver}.xlsx"))
```

#### With sva

```{r de_eo_sva, fig.show = "hide"}
eo_de_sva <- sm(all_pairwise(eo, model_batch = "svaseq", filter = TRUE))
eo_tables_sva <- sm(combine_de_tables(
    eo_de_sva, keepers = keepers,
    excel = glue::glue("excel/eosinophil_clinical_all_tables_sva-v{ver}.xlsx")))
eo_sig_sva <- sm(extract_significant_genes(
    eo_tables_sva,
    excel = glue::glue("excel/eosinophil_clinical_sig_tables_sva-v{ver}.xlsx"),
    according_to = "deseq"))
```

#### Eosinophil DE plots

```{r eo_de_plots}
## DESeq2 MA plot of failure / cure
eo_tables[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]]$plot

## DESeq2 Volcano plot of failure / cure
eo_tables[["plots"]][["fail_vs_cure"]][["deseq_vol_plots"]]$plot

## DESeq2 MA plot of failure / cure with sva
eo_tables_sva[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]]$plot

## DESeq2 Volcano plot of failure / cure with sva
eo_tables_sva[["plots"]][["fail_vs_cure"]][["deseq_vol_plots"]]$plot
```

#### Eosinophil ontology search

```{r eo_gprofiler}
ups <- eo_sig[["deseq"]][["ups"]][["fail_vs_cure"]]
downs <- eo_sig[["deseq"]][["downs"]][["fail_vs_cure"]]

eo_up_gp <- simple_gprofiler(sig_genes = ups)
eo_up_gp[["pvalue_plots"]][["bpp_plot_over"]]
eo_up_gp[["pvalue_plots"]][["mfp_plot_over"]]
eo_up_gp[["pvalue_plots"]][["reactome_plot_over"]]

eo_down_gp <- simple_gprofiler(downs)
eo_down_gp[["pvalue_plots"]][["bpp_plot_over"]]
eo_down_gp[["pvalue_plots"]][["mfp_plot_over"]]
eo_down_gp[["pvalue_plots"]][["reactome_plot_over"]]
```

#### Eosinophil MSigDB query

```{r msig_eo_goseq, fig.show = "hide"}
eo_up_goseq_msig <- goseq_msigdb(sig_genes = ups, signatures = broad_c7,
                                 signature_category = "c7", length_db = hs_length)

eo_down_goseq_msig <- goseq_msigdb(sig_genes = downs, signatures = broad_c7,
                                   signature_category = "c7", length_db = hs_length)
```

#### Plot of similar experiments

```{r msig_plots_eo}
## Eosinophil genes with increased expression in the failed samples
## share genes with the following experiments
eo_up_goseq_msig[["pvalue_plots"]][["mfp_plot_over"]]

## Eosinophil genes with increased expression in the cured samples
## share genes with the following experiments
eo_down_goseq_msig[["pvalue_plots"]][["mfp_plot_over"]]
```

## Biopsies

### Evaluate Biopsy samples

```{r biopsies}
biop <- subset_expt(hs_valid, subset = "typeofcells=='biopsy'") %>%
  set_expt_conditions(fact = "clinicaloutcome") %>%
  set_expt_batches(fact = "donor") %>%
  set_expt_colors(colors = chosen_colors)

save_result <- save(biop, file = "rda/biopsy_expt.rda")
biop_norm <- normalize_expt(biop, filter = TRUE, convert = "cpm",
                            transform = "log2", norm = "quant")
plt <- plot_pca(biop_norm, plot_labels = FALSE)$plot
pp(file = glue("images/biop_pca_normalized-v{ver}.pdf"), image = plt)

biop_nb <- sm(normalize_expt(biop, convert = "cpm", filter = TRUE,
                             transform = "log2", batch = "svaseq"))
plt <- plot_pca(biop_nb, plot_labels = FALSE)$plot
pp(file = glue("images/biop_pca_normalized_svaseq-v{ver}.pdf"), image = plt)
```

### DE of Biopsy samples

#### Without sva

```{r de_biopsy, fig.show = "hide"}
biop_de <- sm(all_pairwise(biop, model_batch = FALSE, filter = TRUE))
biop_tables <- combine_de_tables(biop_de, keepers = keepers,
                                 excel = glue::glue("excel/biopsy_clinical_all_tables-v{ver}.xlsx"))
written <- write_xlsx(data = biop_tables[["data"]][[1]],
                      excel = glue::glue("excel/biopsy_clinical_table-v{ver}.xlsx"))
biop_sig <- extract_significant_genes(biop_tables, according_to = "deseq")
##written <- write_xlsx(data = biop_sig[["deseq"]][["ups"]][[1]],
##                      excel = glue::glue("excel/biopsy_clinical_sigup-v{ver}.xlsx"))
written <- write_xlsx(data = biop_sig[["deseq"]][["downs"]][[1]],
                      excel = glue::glue("excel/biopsy_clinical_sigdown-v{ver}.xlsx"))
biop_pct_sig <- extract_significant_genes(biop_tables, n = 200, lfc = NULL, p = NULL, according_to = "deseq")
written <- write_xlsx(data = biop_pct_sig[["deseq"]][["ups"]][[1]],
                      excel = glue::glue("excel/biopsy_clinical_sigup_pct-v{ver}.xlsx"))
written <- write_xlsx(data = biop_pct_sig[["deseq"]][["downs"]][[1]],
                      excel = glue::glue("excel/biopsy_clinical_sigdown_pct-v{ver}.xlsx"))

biop_cpm <- sm(normalize_expt(biop, convert = "cpm"))
written <- write_xlsx(data = exprs(biop_cpm),
                      excel = glue::glue("excel/biopsy_cpm_before_batch-v{ver}.xlsx"))
biop_bcpm <- sm(normalize_expt(biop, filter = TRUE, batch = "svaseq", convert = "cpm"))
written <- write_xlsx(data = exprs(biop_bcpm),
                      excel = glue::glue("excel/biopsy_cpm_after_batch-v{ver}.xlsx"))
```

#### with sva

```{r de_biopsy_sva, fig.show = "hide"}
biop_de_sva <- sm(all_pairwise(biop, model_batch = "svaseq", filter = TRUE))
biop_tables_sva <- sm(combine_de_tables(
    biop_de_sva, keepers = keepers,
    excel = glue::glue("excel/biopsy_clinical_all_tables_sva-v{ver}.xlsx")))
biop_sig_sva <- sm(extract_significant_genes(
    biop_tables_sva,
    excel = glue::glue("excel/biopsy_clinical_sig_tables_sva-v{ver}.xlsx"),
    according_to = "deseq"))
```

#### Biopsy DE plots

```{r biop_de_plots}
## DESeq2 MA plot of failure / cure
biop_tables[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]]$plot

## DESeq2 Volcano plot of failure / cure
biop_tables[["plots"]][["fail_vs_cure"]][["deseq_vol_plots"]]$plot

## DESeq2 MA plot of failure / cure
biop_tables_sva[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]]$plot

## DESeq2 Volcano plot of failure / cure
biop_tables_sva[["plots"]][["fail_vs_cure"]][["deseq_vol_plots"]]$plot
```

# Look for shared genes among Monocytes/Neutrophils/Eosinophils

We have three variables containing the 'significant' DE genes for the
three cell types.  For this I am choosing (for the moment) to use the
sva data.

```{r shared_by_type}
## mono_sig_sva, neut_sig_sva, eo_sig_sva
sig_vectors <- list(
    "monocytes" = c(rownames(mono_sig_sva[["deseq"]][["ups"]][["fail_vs_cure"]]),
                    rownames(mono_sig_sva[["deseq"]][["downs"]][["fail_vs_cure"]])),
    "neutrophils" = c(rownames(neut_sig_sva[["deseq"]][["ups"]][["fail_vs_cure"]]),
                      rownames(neut_sig_sva[["deseq"]][["downs"]][["fail_vs_cure"]])),
    "eosinophils" =  c(rownames(eo_sig_sva[["deseq"]][["ups"]][["fail_vs_cure"]]),
                       rownames(eo_sig_sva[["deseq"]][["downs"]][["fail_vs_cure"]])))

shared_vector <- Vennerable::Venn(Sets = sig_vectors)
Vennerable::plot(shared_vector, doWeights = FALSE)

shared_ids <- shared_vector@IntersectionSets[["111"]]
shared_expt <- exclude_genes_expt(hs_clinical, ids = shared_ids, method = "keep")
shared_written <- write_expt(
    shared_expt, norm = "raw",
```

# Monocytes by visit

 1. Can you please share with us a PCA (SVA and non-SVA) of the
    monocytes of the TMRC3 project, but labeling them based on the visit
    (V1, V2, V3)?
 2. Can you please share DE lists of V1 vs V2, V1 vs V3, V1 vs. V2+V3
    and V2 vs V3?

```{r monocytes_by_visit}
visit_colors <- chosen_colors <- c("#D95F02", "#7570B3", "#1B9E77")
names(visit_colors) <- c(1, 2, 3)
mono_visit <- subset_expt(hs_valid, subset = "typeofcells=='monocytes'") %>%
  set_expt_conditions(fact = "visitnumber") %>%
  set_expt_batches(fact = "clinicaloutcome") %>%
  set_expt_colors(colors = chosen_colors)

mono_visit_norm <- normalize_expt(mono_visit, filter = TRUE, norm = "quant", convert = "cpm",
                                  transform = "log2")
mono_visit_pca <- plot_pca(mono_visit_norm)
pp(file = "images/monocyte_by_visit.png", image = mono_visit_pca$plot)

mono_visit_nb <- normalize_expt(mono_visit, filter = TRUE, convert = "cpm",
                                batch = "svaseq", transform = "log2")
mono_visit_nb_pca <- plot_pca(mono_visit_nb)
pp(file = "images/monocyte_by_visit_nb.png", image = mono_visit_nb_pca$plot)

table(pData(mono_visit_norm)$batch)
```

```{r mono_visit_de, fig.show = "hide"}
keepers <- list(
    "second_vs_first" = c("c2", "c1"),
    "third_vs_second" = c("c3", "c2"),
    "third_vs_first" = c("c3", "c1"))
mono_visit_de <- all_pairwise(mono_visit, model_batch = "svaseq", filter = TRUE)

mono_visit_tables <- combine_de_tables(
    mono_visit_de,
    keepers = keepers,
    excel = glue::glue("excel/mono_visit_tables-v{ver}.xlsx"))
```

```{r v1_vs_all}
new_factor <- as.character(pData(mono_visit)[["visitnumber"]])
not_one_idx <- new_factor != 1
new_factor[not_one_idx] <- "not_1"
mono_one_vs <- set_expt_conditions(mono_visit, new_factor)

mono_one_vs_de <- all_pairwise(mono_one_vs, model_batch = "svaseq", filter = TRUE)

mono_one_vs_tables <- combine_de_tables(
    mono_one_vs_de,
    excel = glue::glue("excel/mono_one_vs_tables-v{ver}.xlsx"))
```

# Test TSP

In writing the following, I quickly realized that tspair was not
joking when it said it is intended for small numbers of genes.  For a
full expressionset of human data it is struggling.  I like the idea,
it may prove worth while to spend some time optimizing the package so
that it is more usable.

```{r tsp, eval = FALSE}
expt <- hs_clinical_nobiop

simple_tsp <- function(expt, column = "condition") {
  facts <- levels(as.factor(pData(expt)[[column]]))
  retlist <- list()
  if (length(facts) < 2) {
    stop("This requires factors with at least 2 levels.")
  } else if (length(facts) == 2) {
    retlist <- simple_tsp_pair(expt, column = column)
  } else {
    for (first in 1:(length(facts) - 1)) {
      for (second in 2:(length(facts))) {
        if (first < second) {
          name <- glue::glue("{facts[first]}_vs_{facts[second]}")
          message("Starting ", name, ".")
          substring <- glue::glue("{column}=='{facts[first]}'|{column}=='{facts[second]}'")
          subby <- subset_expt(expt, subset=as.character(substring))
          retlist[[name]] <- simple_tsp_pair(subby, column = column)
        }
      }
    }
  }
}

simple_tsp_pair <- function(subby, column = "condition", repetitions = 50) {
  tsp_input <- subby[["expressionset"]]
  tsp_output <- tspcalc(tsp_input, column)
  tsp_scores <- tspsig(tsp_input, column, B = repetitions)
}

tsp1 <- tspcalc(tsp_input, "condition")

```

```{r saveme}
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)
```
