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_20210528.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 98 rows from the sample metadata because they were blank.## The sample definitions comprises: 146 rows(samples) and 74 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 121 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$plot## Warning: ggrepel: 92 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ncrna_lost_df <- as.data.frame(pData(hs_expt)[["ncrna_lost"]])
rownames(ncrna_lost_df) <- rownames(pData(hs_expt))
colnames(ncrna_lost_df) <- "ncrna_lost"
tmpdf <- merge(nonzero$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 121, now there are 118 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 118, now there are 100 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$plot,
height = 8, width = 20)
4.3.1 Repeat without the biopsy samples
hs_clinical_nobiop <- hs_clinical %>%
subset_expt(subset = "typeofcells!='biopsy'")## subset_expt(): There were 100, now there are 60 samples.hs_clinical_nobiop_norm <- sm(normalize_expt(hs_clinical_nobiop, filter = TRUE, transform = "log2",
convert = "cpm", norm = "quant"))
clinical_nobiop_pca <- plot_pca(hs_clinical_nobiop_norm, plot_labels = FALSE, cis = NULL,
plot_title = "PCA - clinical samples without biopsies")
pp(file = glue("images/all_clinical_nobiop_nobatch_pca-v{ver}.png"),
image = clinical_nobiop_pca$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 5346 low-count genes (14595 remaining).## batch_counts: Before batch/surrogate estimation, 87151 entries are x==0: 6%.## batch_counts: Before batch/surrogate estimation, 253186 entries are 0<x<1: 17%.## Setting 18898 low elements to zero.## transform_counts: Found 18898 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:102 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 60, now there are 45 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
## 1 261 265 337 344 326 365 73 220
## basic_V1 basic_V2
## 1 53 29hs_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 100, now there are 83 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 495 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testing## Working with 12 genes.## Warning: `distinct_()` was deprecated in dplyr 0.7.0.
## Please use `distinct()` instead.
## See vignette('programming') for more help## Working with 12 genes after filtering: minc > 3## Joining, by = "merge"
## Joining, by = "merge"
lrt_visit_clinical_test[["favorite_genes"]]## genes cluster
## ENSG00000103355 ENSG00000103355 1
## ENSG00000105205 ENSG00000105205 1
## ENSG00000112053 ENSG00000112053 2
## ENSG00000115155 ENSG00000115155 2
## ENSG00000119535 ENSG00000119535 2
## ENSG00000130433 ENSG00000130433 1
## ENSG00000154928 ENSG00000154928 2
## ENSG00000157551 ENSG00000157551 2
## ENSG00000163464 ENSG00000163464 1
## ENSG00000186529 ENSG00000186529 1
## ENSG00000188897 ENSG00000188897 2
## ENSG00000257743 ENSG00000257743 2lrt_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 59 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testing## Working with 1448 genes.## Working with 1446 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 1
## ENSG00000004777 ENSG00000004777 3
## ENSG00000004846 ENSG00000004846 4
## ENSG00000004961 ENSG00000004961 5
## ENSG00000005020 ENSG00000005020 6
## ENSG00000005075 ENSG00000005075 7
## ENSG00000005486 ENSG00000005486 7
## ENSG00000005801 ENSG00000005801 1
## ENSG00000006007 ENSG00000006007 8
## ENSG00000006652 ENSG00000006652 6
## ENSG00000006788 ENSG00000006788 4
## ENSG00000006831 ENSG00000006831 9
## ENSG00000007047 ENSG00000007047 10
## ENSG00000007341 ENSG00000007341 11
## ENSG00000007392 ENSG00000007392 12
## ENSG00000007923 ENSG00000007923 13
## ENSG00000007944 ENSG00000007944 6
## ENSG00000008056 ENSG00000008056 8
## ENSG00000008405 ENSG00000008405 14
## ENSG00000010704 ENSG00000010704 8
## ENSG00000010818 ENSG00000010818 11
## ENSG00000010932 ENSG00000010932 14
## ENSG00000011028 ENSG00000011028 2
## ENSG00000011426 ENSG00000011426 15
## ENSG00000011523 ENSG00000011523 6
## ENSG00000012963 ENSG00000012963 2
## ENSG00000013306 ENSG00000013306 13
## ENSG00000013503 ENSG00000013503 16
## ENSG00000013583 ENSG00000013583 17
## ENSG00000015532 ENSG00000015532 12
## ENSG00000017427 ENSG00000017427 15
## ENSG00000019144 ENSG00000019144 5
## ENSG00000020129 ENSG00000020129 1
## ENSG00000020256 ENSG00000020256 12
## ENSG00000020577 ENSG00000020577 5
## ENSG00000020633 ENSG00000020633 18
## ENSG00000021355 ENSG00000021355 8
## ENSG00000023171 ENSG00000023171 19
## ENSG00000023516 ENSG00000023516 1
## ENSG00000024526 ENSG00000024526 14
## ENSG00000026950 ENSG00000026950 20
## ENSG00000027847 ENSG00000027847 21
## ENSG00000028203 ENSG00000028203 1
## ENSG00000030066 ENSG00000030066 12
## ENSG00000035720 ENSG00000035720 12
## ENSG00000037897 ENSG00000037897 5
## ENSG00000038945 ENSG00000038945 5
## ENSG00000039560 ENSG00000039560 2
## ENSG00000040199 ENSG00000040199 1
## ENSG00000042062 ENSG00000042062 15
## ENSG00000042088 ENSG00000042088 1
## ENSG00000042445 ENSG00000042445 2
## ENSG00000044459 ENSG00000044459 13
## ENSG00000046647 ENSG00000046647 1
## ENSG00000047365 ENSG00000047365 17
## ENSG00000048342 ENSG00000048342 6
## ENSG00000048828 ENSG00000048828 10
## ENSG00000049541 ENSG00000049541 22
## ENSG00000050327 ENSG00000050327 14
## ENSG00000050820 ENSG00000050820 15
## ENSG00000053900 ENSG00000053900 23
## ENSG00000054277 ENSG00000054277 13
## ENSG00000054282 ENSG00000054282 21
## ENSG00000054983 ENSG00000054983 19
## ENSG00000055130 ENSG00000055130 13
## ENSG00000055332 ENSG00000055332 8
## ENSG00000055483 ENSG00000055483 21
## ENSG00000057704 ENSG00000057704 7
## ENSG00000058600 ENSG00000058600 1
## ENSG00000059573 ENSG00000059573 1
## ENSG00000059588 ENSG00000059588 1
## ENSG00000060138 ENSG00000060138 17
## ENSG00000060558 ENSG00000060558 18
## ENSG00000060762 ENSG00000060762 10
## ENSG00000060982 ENSG00000060982 5
## ENSG00000061918 ENSG00000061918 1
## ENSG00000062038 ENSG00000062038 14
## ENSG00000062598 ENSG00000062598 6
## ENSG00000063761 ENSG00000063761 21
## ENSG00000064313 ENSG00000064313 10
## ENSG00000064763 ENSG00000064763 20
## ENSG00000065060 ENSG00000065060 21
## ENSG00000065150 ENSG00000065150 1
## ENSG00000065809 ENSG00000065809 24
## ENSG00000066279 ENSG00000066279 15
## ENSG00000066379 ENSG00000066379 2
## ENSG00000066455 ENSG00000066455 15
## ENSG00000066651 ENSG00000066651 5
## ENSG00000066926 ENSG00000066926 1
## ENSG00000067533 ENSG00000067533 1
## ENSG00000068305 ENSG00000068305 10
## ENSG00000068784 ENSG00000068784 20
## ENSG00000069020 ENSG00000069020 15
## ENSG00000069248 ENSG00000069248 1
## ENSG00000069345 ENSG00000069345 7
## ENSG00000069998 ENSG00000069998 13
## ENSG00000070087 ENSG00000070087 22
## ENSG00000070269 ENSG00000070269 20
## ENSG00000071073 ENSG00000071073 9
## ENSG00000071242 ENSG00000071242 23
## ENSG00000071462 ENSG00000071462 13
## ENSG00000071575 ENSG00000071575 15
## ENSG00000071655 ENSG00000071655 13
## ENSG00000072121 ENSG00000072121 25
## ENSG00000072506 ENSG00000072506 13
## ENSG00000072657 ENSG00000072657 11
## ENSG00000072858 ENSG00000072858 12
## ENSG00000073417 ENSG00000073417 21
## ENSG00000073737 ENSG00000073737 20
## ENSG00000073969 ENSG00000073969 5
## ENSG00000074319 ENSG00000074319 11
## ENSG00000074842 ENSG00000074842 13
## ENSG00000074935 ENSG00000074935 1
## ENSG00000075340 ENSG00000075340 1
## ENSG00000075643 ENSG00000075643 1
## ENSG00000075785 ENSG00000075785 8
## ENSG00000076242 ENSG00000076242 1
## ENSG00000076351 ENSG00000076351 1
## ENSG00000076716 ENSG00000076716 5
## ENSG00000076984 ENSG00000076984 10
## ENSG00000077152 ENSG00000077152 15
## ENSG00000077616 ENSG00000077616 12
## ENSG00000077782 ENSG00000077782 2
## ENSG00000077935 ENSG00000077935 13
## ENSG00000078053 ENSG00000078053 5
## ENSG00000078098 ENSG00000078098 2
## ENSG00000078114 ENSG00000078114 2
## ENSG00000078269 ENSG00000078269 26
## ENSG00000078403 ENSG00000078403 12
## ENSG00000078589 ENSG00000078589 23
## ENSG00000079134 ENSG00000079134 1
## ENSG00000079215 ENSG00000079215 1
## ENSG00000079263 ENSG00000079263 11
## ENSG00000079308 ENSG00000079308 1
## ENSG00000079337 ENSG00000079337 11
## ENSG00000079482 ENSG00000079482 26
## ENSG00000079616 ENSG00000079616 12
## ENSG00000080200 ENSG00000080200 1
## ENSG00000081059 ENSG00000081059 16
## ENSG00000081386 ENSG00000081386 1
## ENSG00000081913 ENSG00000081913 8
## ENSG00000082213 ENSG00000082213 1
## ENSG00000082458 ENSG00000082458 15
## ENSG00000082516 ENSG00000082516 1
## ENSG00000083290 ENSG00000083290 13
## ENSG00000083307 ENSG00000083307 4
## ENSG00000083457 ENSG00000083457 9
## ENSG00000083828 ENSG00000083828 7
## ENSG00000083844 ENSG00000083844 16
## ENSG00000083845 ENSG00000083845 1
## ENSG00000084207 ENSG00000084207 13
## ENSG00000085840 ENSG00000085840 16
## ENSG00000085871 ENSG00000085871 27
## ENSG00000085982 ENSG00000085982 1
## ENSG00000085999 ENSG00000085999 1
## ENSG00000086730 ENSG00000086730 8
## ENSG00000087116 ENSG00000087116 5
## ENSG00000087269 ENSG00000087269 1
## ENSG00000088325 ENSG00000088325 15
## ENSG00000088726 ENSG00000088726 1
## ENSG00000088827 ENSG00000088827 17
## ENSG00000088992 ENSG00000088992 18
## ENSG00000089012 ENSG00000089012 15
## ENSG00000089048 ENSG00000089048 1
## ENSG00000089123 ENSG00000089123 1
## ENSG00000089127 ENSG00000089127 25
## ENSG00000089195 ENSG00000089195 13
## ENSG00000089558 ENSG00000089558 10
## ENSG00000089818 ENSG00000089818 7
## ENSG00000090013 ENSG00000090013 17
## ENSG00000090565 ENSG00000090565 1
## ENSG00000090857 ENSG00000090857 18
## ENSG00000090889 ENSG00000090889 14
## ENSG00000091127 ENSG00000091127 5
## ENSG00000091409 ENSG00000091409 15
## ENSG00000091640 ENSG00000091640 13
## ENSG00000091972 ENSG00000091972 14
## ENSG00000091986 ENSG00000091986 15
## ENSG00000092067 ENSG00000092067 24
## ENSG00000092847 ENSG00000092847 10
## ENSG00000092871 ENSG00000092871 20
## ENSG00000093217 ENSG00000093217 5
## ENSG00000094841 ENSG00000094841 1
## ENSG00000095319 ENSG00000095319 1
## ENSG00000097021 ENSG00000097021 1
## ENSG00000099139 ENSG00000099139 17
## ENSG00000099381 ENSG00000099381 21
## ENSG00000099715 ENSG00000099715 26
## ENSG00000099783 ENSG00000099783 18
## ENSG00000100036 ENSG00000100036 13
## ENSG00000100060 ENSG00000100060 24
## ENSG00000100065 ENSG00000100065 1
## ENSG00000100124 ENSG00000100124 15
## ENSG00000100216 ENSG00000100216 13
## ENSG00000100281 ENSG00000100281 18
## ENSG00000100288 ENSG00000100288 1
## ENSG00000100292 ENSG00000100292 17
## ENSG00000100335 ENSG00000100335 21
## ENSG00000100353 ENSG00000100353 26
## ENSG00000100376 ENSG00000100376 13
## ENSG00000100413 ENSG00000100413 21
## ENSG00000100558 ENSG00000100558 1
## ENSG00000100596 ENSG00000100596 6
## ENSG00000100600 ENSG00000100600 5
## ENSG00000100652 ENSG00000100652 20
## ENSG00000100767 ENSG00000100767 11
## ENSG00000100842 ENSG00000100842 2
## ENSG00000100908 ENSG00000100908 11
## ENSG00000100941 ENSG00000100941 7
## ENSG00000100997 ENSG00000100997 13
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## ENSG00000181045 ENSG00000181045 17
## ENSG00000181218 ENSG00000181218 13
## ENSG00000181350 ENSG00000181350 13
## ENSG00000181381 ENSG00000181381 6
## ENSG00000181392 ENSG00000181392 23
## ENSG00000181666 ENSG00000181666 1
## ENSG00000181754 ENSG00000181754 15
## ENSG00000181852 ENSG00000181852 18
## ENSG00000181873 ENSG00000181873 1
## ENSG00000182118 ENSG00000182118 5
## ENSG00000182158 ENSG00000182158 12
## ENSG00000182183 ENSG00000182183 27
## ENSG00000182263 ENSG00000182263 14
## ENSG00000182378 ENSG00000182378 1
## ENSG00000182578 ENSG00000182578 17
## ENSG00000182704 ENSG00000182704 2
## ENSG00000182901 ENSG00000182901 22
## ENSG00000182952 ENSG00000182952 11
## ENSG00000183044 ENSG00000183044 6
## ENSG00000183087 ENSG00000183087 1
## ENSG00000183117 ENSG00000183117 17
## ENSG00000183150 ENSG00000183150 11
## ENSG00000183172 ENSG00000183172 13
## ENSG00000183185 ENSG00000183185 26
## ENSG00000183283 ENSG00000183283 7
## ENSG00000183323 ENSG00000183323 3
## ENSG00000183337 ENSG00000183337 24
## ENSG00000183431 ENSG00000183431 24
## ENSG00000183520 ENSG00000183520 5
## ENSG00000183578 ENSG00000183578 5
## ENSG00000183617 ENSG00000183617 13
## ENSG00000183763 ENSG00000183763 1
## ENSG00000183785 ENSG00000183785 31
## ENSG00000183801 ENSG00000183801 15
## ENSG00000183853 ENSG00000183853 2
## ENSG00000183955 ENSG00000183955 24
## ENSG00000184009 ENSG00000184009 26
## ENSG00000184292 ENSG00000184292 15
## ENSG00000184384 ENSG00000184384 8
## ENSG00000184402 ENSG00000184402 6
## ENSG00000184545 ENSG00000184545 2
## ENSG00000184661 ENSG00000184661 15
## ENSG00000184898 ENSG00000184898 11
## ENSG00000184900 ENSG00000184900 13
## ENSG00000184924 ENSG00000184924 13
## ENSG00000184979 ENSG00000184979 25
## ENSG00000185033 ENSG00000185033 8
## ENSG00000185043 ENSG00000185043 7
## ENSG00000185049 ENSG00000185049 21
## ENSG00000185055 ENSG00000185055 9
## ENSG00000185271 ENSG00000185271 4
## ENSG00000185272 ENSG00000185272 20
## ENSG00000185561 ENSG00000185561 14
## ENSG00000185619 ENSG00000185619 7
## ENSG00000185627 ENSG00000185627 22
## ENSG00000185669 ENSG00000185669 7
## ENSG00000185686 ENSG00000185686 14
## ENSG00000185736 ENSG00000185736 14
## ENSG00000185825 ENSG00000185825 8
## ENSG00000185920 ENSG00000185920 14
## ENSG00000186026 ENSG00000186026 15
## ENSG00000186073 ENSG00000186073 1
## ENSG00000186193 ENSG00000186193 9
## ENSG00000186197 ENSG00000186197 4
## ENSG00000186280 ENSG00000186280 5
## ENSG00000186281 ENSG00000186281 1
## ENSG00000186376 ENSG00000186376 3
## ENSG00000186470 ENSG00000186470 11
## ENSG00000186645 ENSG00000186645 6
## ENSG00000186654 ENSG00000186654 25
## ENSG00000186665 ENSG00000186665 1
## ENSG00000186812 ENSG00000186812 11
## ENSG00000186818 ENSG00000186818 17
## ENSG00000186951 ENSG00000186951 21
## ENSG00000187105 ENSG00000187105 12
## ENSG00000187134 ENSG00000187134 15
## ENSG00000187147 ENSG00000187147 18
## ENSG00000187164 ENSG00000187164 17
## ENSG00000187187 ENSG00000187187 1
## ENSG00000187231 ENSG00000187231 8
## ENSG00000187554 ENSG00000187554 8
## ENSG00000187566 ENSG00000187566 1
## ENSG00000187569 ENSG00000187569 28
## ENSG00000187726 ENSG00000187726 18
## ENSG00000187758 ENSG00000187758 1
## ENSG00000187792 ENSG00000187792 12
## ENSG00000188033 ENSG00000188033 7
## ENSG00000188037 ENSG00000188037 8
## ENSG00000188211 ENSG00000188211 13
## ENSG00000188234 ENSG00000188234 6
## ENSG00000188636 ENSG00000188636 13
## ENSG00000188725 ENSG00000188725 5
## ENSG00000188886 ENSG00000188886 8
## ENSG00000188921 ENSG00000188921 6
## ENSG00000188938 ENSG00000188938 8
## ENSG00000189067 ENSG00000189067 22
## ENSG00000189077 ENSG00000189077 7
## ENSG00000189159 ENSG00000189159 20
## ENSG00000189164 ENSG00000189164 1
## ENSG00000189195 ENSG00000189195 11
## ENSG00000189337 ENSG00000189337 11
## ENSG00000189366 ENSG00000189366 1
## ENSG00000189430 ENSG00000189430 11
## ENSG00000196072 ENSG00000196072 7
## ENSG00000196123 ENSG00000196123 5
## ENSG00000196126 ENSG00000196126 13
## ENSG00000196141 ENSG00000196141 2
## ENSG00000196199 ENSG00000196199 20
## ENSG00000196227 ENSG00000196227 7
## ENSG00000196230 ENSG00000196230 1
## ENSG00000196247 ENSG00000196247 11
## ENSG00000196305 ENSG00000196305 1
## ENSG00000196358 ENSG00000196358 6
## ENSG00000196369 ENSG00000196369 8
## ENSG00000196378 ENSG00000196378 21
## ENSG00000196388 ENSG00000196388 1
## ENSG00000196405 ENSG00000196405 12
## ENSG00000196465 ENSG00000196465 1
## ENSG00000196526 ENSG00000196526 14
## ENSG00000196584 ENSG00000196584 15
## ENSG00000196639 ENSG00000196639 1
## ENSG00000196653 ENSG00000196653 1
## ENSG00000196743 ENSG00000196743 5
## ENSG00000196747 ENSG00000196747 23
## ENSG00000196872 ENSG00000196872 11
## ENSG00000196923 ENSG00000196923 7
## ENSG00000197093 ENSG00000197093 11
## ENSG00000197283 ENSG00000197283 12
## ENSG00000197302 ENSG00000197302 15
## ENSG00000197343 ENSG00000197343 6
## ENSG00000197442 ENSG00000197442 6
## ENSG00000197461 ENSG00000197461 1
## ENSG00000197603 ENSG00000197603 1
## ENSG00000197747 ENSG00000197747 13
## ENSG00000197771 ENSG00000197771 22
## ENSG00000197785 ENSG00000197785 1
## ENSG00000197857 ENSG00000197857 3
## ENSG00000197935 ENSG00000197935 15
## ENSG00000197992 ENSG00000197992 11
## ENSG00000198178 ENSG00000198178 28
## ENSG00000198286 ENSG00000198286 1
## ENSG00000198336 ENSG00000198336 15
## ENSG00000198353 ENSG00000198353 5
## ENSG00000198431 ENSG00000198431 8
## ENSG00000198455 ENSG00000198455 21
## ENSG00000198502 ENSG00000198502 13
## ENSG00000198673 ENSG00000198673 19
## ENSG00000198700 ENSG00000198700 1
## ENSG00000198730 ENSG00000198730 14
## ENSG00000198838 ENSG00000198838 12
## ENSG00000198848 ENSG00000198848 17
## ENSG00000198870 ENSG00000198870 13
## ENSG00000198876 ENSG00000198876 11
## ENSG00000198959 ENSG00000198959 5
## ENSG00000198964 ENSG00000198964 3
## ENSG00000203814 ENSG00000203814 11
## ENSG00000204103 ENSG00000204103 17
## ENSG00000204104 ENSG00000204104 1
## ENSG00000204161 ENSG00000204161 3
## ENSG00000204291 ENSG00000204291 15
## ENSG00000204388 ENSG00000204388 2
## ENSG00000204389 ENSG00000204389 11
## ENSG00000204574 ENSG00000204574 2
## ENSG00000204590 ENSG00000204590 22
## ENSG00000204815 ENSG00000204815 18
## ENSG00000204909 ENSG00000204909 11
## ENSG00000204991 ENSG00000204991 12
## ENSG00000205038 ENSG00000205038 1
## ENSG00000205236 ENSG00000205236 6
## ENSG00000205339 ENSG00000205339 1
## ENSG00000205362 ENSG00000205362 2
## ENSG00000205571 ENSG00000205571 12
## ENSG00000205639 ENSG00000205639 21
## ENSG00000205730 ENSG00000205730 26
## ENSG00000205937 ENSG00000205937 18
## ENSG00000211448 ENSG00000211448 14
## ENSG00000213214 ENSG00000213214 14
## ENSG00000213588 ENSG00000213588 15
## ENSG00000213694 ENSG00000213694 17
## ENSG00000213719 ENSG00000213719 8
## ENSG00000213859 ENSG00000213859 1
## ENSG00000213918 ENSG00000213918 23
## ENSG00000213923 ENSG00000213923 4
## ENSG00000213988 ENSG00000213988 26
## ENSG00000214113 ENSG00000214113 5
## ENSG00000214226 ENSG00000214226 2
## ENSG00000214706 ENSG00000214706 1
## ENSG00000214872 ENSG00000214872 6
## ENSG00000215009 ENSG00000215009 12
## ENSG00000215784 ENSG00000215784 25
## ENSG00000219481 ENSG00000219481 12
## ENSG00000221994 ENSG00000221994 23
## ENSG00000232040 ENSG00000232040 14
## ENSG00000233608 ENSG00000233608 1
## ENSG00000236320 ENSG00000236320 14
## ENSG00000239887 ENSG00000239887 2
## ENSG00000239920 ENSG00000239920 20
## ENSG00000240445 ENSG00000240445 27
## ENSG00000241058 ENSG00000241058 12
## ENSG00000241106 ENSG00000241106 15
## ENSG00000242612 ENSG00000242612 1
## ENSG00000243772 ENSG00000243772 2
## ENSG00000244165 ENSG00000244165 13
## ENSG00000244242 ENSG00000244242 10
## ENSG00000244405 ENSG00000244405 5
## ENSG00000245848 ENSG00000245848 17
## ENSG00000248405 ENSG00000248405 28
## ENSG00000248993 ENSG00000248993 17
## ENSG00000249242 ENSG00000249242 1
## ENSG00000250264 ENSG00000250264 6
## ENSG00000253304 ENSG00000253304 14
## ENSG00000254979 ENSG00000254979 21
## ENSG00000255872 ENSG00000255872 27
## ENSG00000256229 ENSG00000256229 11
## ENSG00000256235 ENSG00000256235 21
## ENSG00000257207 ENSG00000257207 4
## ENSG00000257335 ENSG00000257335 7
## ENSG00000258643 ENSG00000258643 1
## ENSG00000259330 ENSG00000259330 21
## ENSG00000260861 ENSG00000260861 6
## ENSG00000261652 ENSG00000261652 15
## ENSG00000263001 ENSG00000263001 21
## ENSG00000263528 ENSG00000263528 17
## ENSG00000263715 ENSG00000263715 15
## ENSG00000266028 ENSG00000266028 8
## ENSG00000266302 ENSG00000266302 2
## ENSG00000267127 ENSG00000267127 15
## ENSG00000268350 ENSG00000268350 26
## ENSG00000269028 ENSG00000269028 27
## ENSG00000271503 ENSG00000271503 1
## ENSG00000272325 ENSG00000272325 24
## ENSG00000273213 ENSG00000273213 25
## ENSG00000273559 ENSG00000273559 6
## ENSG00000273802 ENSG00000273802 11
## ENSG00000274810 ENSG00000274810 15
## ENSG00000275395 ENSG00000275395 5
## ENSG00000276070 ENSG00000276070 16
## ENSG00000276085 ENSG00000276085 11
## ENSG00000277075 ENSG00000277075 15
## ENSG00000277117 ENSG00000277117 13
## ENSG00000277632 ENSG00000277632 5
## ENSG00000278828 ENSG00000278828 23
## ENSG00000280433 ENSG00000280433 24
## ENSG00000280670 ENSG00000280670 12
## ENSG00000280969 ENSG00000280969 22
## ENSG00000282988 ENSG00000282988 20
## ENSG00000283149 ENSG00000283149 26
## ENSG00000283977 ENSG00000283977 18
## ENSG00000285000 ENSG00000285000 15
## ENSG00000285130 ENSG00000285130 13
## ENSG00000285446 ENSG00000285446 26
## ENSG00000285708 ENSG00000285708 6
## ENSG00000285816 ENSG00000285816 84.4.2 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 60 samples which kept less than 90 percent counts.## X1017n1 X1017m1 X1034n1 X1034n2 X1034m2 X1034m2. X2052e1 X2052m2
## 0.2455 0.5989 1.8031 2.0923 1.3180 1.3074 0.2487 0.7206
## X2052n2 X2052m3 X2052n3 X2065m1 X2065n1 X2066m1 X2066n1 X2065m2
## 1.0604 0.9167 0.8721 1.0112 2.4173 0.6574 0.4900 0.7175
## X2065n2 X2065e2 X2066m2 X2068m1 X2068n1 X2068e1 X2072m1 X2072n1
## 0.4644 0.8749 0.7053 0.6257 0.4623 0.8699 0.5861 0.3516
## X2072e1 X2073m1 X2073n1 X2073e1 X2068m2 X2068n2 X2068e2 X2072m2
## 0.6949 0.8227 1.4188 0.5182 0.5956 0.3624 0.5391 0.6187
## X2072n2 X2072e2 X2073m2 X2073n2 X2073e2 X2066m3 X2068m3 X2068n3
## 0.3782 0.3675 1.1677 2.1225 0.5954 0.7238 0.5836 0.4605
## X2068e3 X2072m3 X2072n3 X2072e3 X2073m3 X2073n3 X2073e3 X2162m1
## 0.4189 0.8524 1.4788 0.4314 0.7648 0.5064 0.3692 0.5618
## X2162n1 X2162e1 X2168n1 X2168e1 X2168m2 X2168n2 X2168e2 X2168m3
## 0.3488 0.5504 1.8125 0.6549 1.1547 1.7708 0.5357 1.4724
## X2168n3 X2168e3 X2172n1 X2172e1
## 2.8236 0.6684 0.2496 0.2975small_norm <- sm(normalize_expt(small_expt, transform = "log2", convert = "cpm",
norm = "quant", filter = TRUE))
plot_pca(small_norm)$plot## Warning: ggrepel: 2 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, 1 entries are x==0: 0%.## batch_counts: Before batch/surrogate estimation, 246 entries are 0<x<1: 2%.## Setting 13 low elements to zero.## transform_counts: Found 13 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 326 genes against hsapiens.## GO search found 70 hits.## Performing gProfiler KEGG search of 326 genes against hsapiens.## KEGG search found 10 hits.## Performing gProfiler REAC search of 326 genes against hsapiens.## REAC search found 8 hits.## Performing gProfiler MI search of 326 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 326 genes against hsapiens.## TF search found 40 hits.## Performing gProfiler CORUM search of 326 genes against hsapiens.## CORUM search found 1 hits.## Performing gProfiler HP search of 326 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 365 genes against hsapiens.## GO search found 110 hits.## Performing gProfiler KEGG search of 365 genes against hsapiens.## KEGG search found 7 hits.## Performing gProfiler REAC search of 365 genes against hsapiens.## REAC search found 6 hits.## Performing gProfiler MI search of 365 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 365 genes against hsapiens.## TF search found 12 hits.## Performing gProfiler CORUM search of 365 genes against hsapiens.## CORUM search found 2 hits.## Performing gProfiler HP search of 365 genes against hsapiens.## HP search found 2 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 118, now there are 22 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 8966 low-count genes (10975 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 8966 low-count genes (10975 remaining).## batch_counts: Before batch/surrogate estimation, 1202 entries are x==0: 0%.## batch_counts: Before batch/surrogate estimation, 13643 entries are 0<x<1: 6%.## Setting 423 low elements to zero.## transform_counts: Found 423 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 153 genes against hsapiens.## GO search found 33 hits.## Performing gProfiler KEGG search of 153 genes against hsapiens.## KEGG search found 2 hits.## Performing gProfiler REAC search of 153 genes against hsapiens.## REAC search found 1 hits.## Performing gProfiler MI search of 153 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 153 genes against hsapiens.## TF search found 2 hits.## Performing gProfiler CORUM search of 153 genes against hsapiens.## CORUM search found 1 hits.## Performing gProfiler HP search of 153 genes against hsapiens.## HP search found 0 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 293 genes against hsapiens.## GO search found 60 hits.## Performing gProfiler KEGG search of 293 genes against hsapiens.## KEGG search found 2 hits.## Performing gProfiler REAC search of 293 genes against hsapiens.## REAC search found 1 hits.## Performing gProfiler MI search of 293 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 293 genes against hsapiens.## TF search found 31 hits.## Performing gProfiler CORUM search of 293 genes against hsapiens.## CORUM search found 2 hits.## Performing gProfiler HP search of 293 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: 153, after conversion: 151.## Before conversion: 227921, after conversion: 35341.## Found 136 go_db genes and 151 length_db genes out of 151.mono_down_goseq_msig <- goseq_msigdb(sig_genes = downs, signatures = broad_c7,
signature_category = "c7", length_db = hs_length)## Before conversion: 293, after conversion: 290.## Before conversion: 227921, after conversion: 35341.## Found 278 go_db genes and 290 length_db genes out of 290.5.2.2.6 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 118, now there are 22 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)## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure
## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure
## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure
## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure
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 211 genes against hsapiens.## GO search found 31 hits.## Performing gProfiler KEGG search of 211 genes against hsapiens.## KEGG search found 3 hits.## Performing gProfiler REAC search of 211 genes against hsapiens.## REAC search found 2 hits.## Performing gProfiler MI search of 211 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 211 genes against hsapiens.## TF search found 29 hits.## Performing gProfiler CORUM search of 211 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 211 genes against hsapiens.## HP search found 0 hits.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)## Performing gProfiler GO search of 199 genes against hsapiens.## GO search found 4 hits.## Performing gProfiler KEGG search of 199 genes against hsapiens.## KEGG search found 0 hits.## Performing gProfiler REAC search of 199 genes against hsapiens.## REAC search found 0 hits.## Performing gProfiler MI search of 199 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 199 genes against hsapiens.## TF search found 21 hits.## Performing gProfiler CORUM search of 199 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 199 genes against hsapiens.## HP search found 3 hits.neut_down_gp[["pvalue_plots"]][["bpp_plot_over"]]
neut_down_gp[["pvalue_plots"]][["mfp_plot_over"]]
neut_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: 211, after conversion: 209.## Before conversion: 227921, after conversion: 35341.## Found 195 go_db genes and 209 length_db genes out of 209.neut_down_goseq_msig <- goseq_msigdb(sig_genes = downs, signatures = broad_c7,
signature_category = "c7", length_db = hs_length)## Before conversion: 199, after conversion: 194.## Before conversion: 227921, after conversion: 35341.## Found 187 go_db genes and 194 length_db genes out of 194.5.2.4.6 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 118, now there are 16 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 191 genes against hsapiens.## GO search found 77 hits.## Performing gProfiler KEGG search of 191 genes against hsapiens.## KEGG search found 9 hits.## Performing gProfiler REAC search of 191 genes against hsapiens.## REAC search found 8 hits.## Performing gProfiler MI search of 191 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 191 genes against hsapiens.## TF search found 56 hits.## Performing gProfiler CORUM search of 191 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 191 genes against hsapiens.## HP search found 0 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 144 genes against hsapiens.## GO search found 1 hits.## Performing gProfiler KEGG search of 144 genes against hsapiens.## KEGG search found 0 hits.## Performing gProfiler REAC search of 144 genes against hsapiens.## REAC search found 0 hits.## Performing gProfiler MI search of 144 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 144 genes against hsapiens.## TF search found 12 hits.## Performing gProfiler CORUM search of 144 genes against hsapiens.## CORUM search found 1 hits.## Performing gProfiler HP search of 144 genes against hsapiens.## HP search found 0 hits.eo_down_gp[["pvalue_plots"]][["bpp_plot_over"]]
eo_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: 191, after conversion: 188.## Before conversion: 227921, after conversion: 35341.## Found 179 go_db genes and 188 length_db genes out of 188.eo_down_goseq_msig <- goseq_msigdb(sig_genes = downs, signatures = broad_c7,
signature_category = "c7", length_db = hs_length)## Before conversion: 144, after conversion: 143.## Before conversion: 227921, after conversion: 35341.## Found 134 go_db genes and 143 length_db genes out of 143.5.3.2.6 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 118, now there are 40 samples.save_result <- save(biop, file = "rda/biopsy_expt.rda")
biop_norm <- normalize_expt(biop, filter = TRUE, convert = "cpm",
transform = "log2", norm = "quant")## Removing 5816 low-count genes (14125 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-v202105.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
6 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.
## 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_expt, ids = shared_ids, method = "keep")## Before removal, there were 19941 genes, now there are 38.## There are 121 samples which kept less than 90 percent counts.## TMRC30001 TMRC30002 TMRC30003 TMRC30004 TMRC30005 TMRC30006 TMRC30007 TMRC30008
## 0.08433 0.07140 0.06880 0.08134 0.07967 0.06525 0.10994 0.06922
## TMRC30009 TMRC30010 TMRC30015 TMRC30011 TMRC30012 TMRC30013 TMRC30016 TMRC30017
## 0.10907 0.08957 0.25138 0.13128 0.06370 0.06461 0.21878 0.14126
## TMRC30050 TMRC30052 TMRC30071 TMRC30056 TMRC30058 TMRC30105 TMRC30094 TMRC30080
## 0.06459 0.08234 0.10140 0.08270 0.09909 0.09420 0.13933 0.14131
## TMRC30103 TMRC30018 TMRC30107 TMRC30083 TMRC30019 TMRC30082 TMRC30093 TMRC30113
## 0.13529 0.12991 0.04443 0.06135 0.14385 0.17123 0.23140 0.37602
## TMRC30096 TMRC30014 TMRC30021 TMRC30029 TMRC30020 TMRC30038 TMRC30039 TMRC30023
## 0.10313 0.12347 0.16778 0.24821 0.13283 0.11167 0.12315 0.20589
## TMRC30025 TMRC30022 TMRC30046 TMRC30047 TMRC30048 TMRC30026 TMRC30030 TMRC30031
## 0.14862 0.13398 0.04618 0.07009 0.10786 0.14798 0.06735 0.12466
## TMRC30032 TMRC30024 TMRC30040 TMRC30033 TMRC30049 TMRC30053 TMRC30054 TMRC30115
## 0.12914 0.06676 0.11183 0.10240 0.04361 0.07279 0.08957 0.03969
## TMRC30037 TMRC30027 TMRC30028 TMRC30034 TMRC30035 TMRC30036 TMRC30044 TMRC30055
## 0.07992 0.20481 0.14991 0.07302 0.11682 0.11005 0.15492 0.05409
## TMRC30068 TMRC30070 TMRC30041 TMRC30042 TMRC30043 TMRC30045 TMRC30059 TMRC30060
## 0.08124 0.09943 0.08690 0.13909 0.23641 0.12245 1.03627 1.14102
## TMRC30061 TMRC30062 TMRC30063 TMRC30051 TMRC30064 TMRC30065 TMRC30066 TMRC30067
## 0.67217 1.05293 0.69864 0.64334 0.71518 0.78964 0.56160 0.59049
## TMRC30057 TMRC30069 TMRC30116 TMRC30074 TMRC30072 TMRC30076 TMRC30077 TMRC30078
## 0.50188 0.46336 0.11202 0.12167 0.05365 0.11092 0.09091 0.05271
## TMRC30088 TMRC30079 TMRC30134 TMRC30135 TMRC30097 TMRC30075 TMRC30085 TMRC30086
## 0.13880 0.10385 0.07728 0.11041 0.13849 0.12601 0.08196 0.18299
## TMRC30087 TMRC30101 TMRC30089 TMRC30090 TMRC30081 TMRC30092 TMRC30104 TMRC30106
## 0.22271 0.10961 0.09371 0.09055 0.10138 0.10842 0.09405 0.09578
## TMRC30114 TMRC30095 TMRC30108 TMRC30130 TMRC30124 TMRC30131 TMRC30109 TMRC30084
## 0.13182 0.14455 0.12949 0.14117 0.12820 0.15445 0.07478 0.09660
## TMRC30098 TMRC30099 TMRC30100 TMRC30110 TMRC30111 TMRC30102 TMRC30091 TMRC30112
## 0.10686 0.10227 0.05695 0.07657 0.11567 0.10563 0.10685 0.06535
## TMRC30073
## 0.12999shared_written <- sm(write_expt(shared_expt, excel="excel/genes_shared_across_celltypes.xlsx"))## Error in if (any(abs(rowSums(as.matrix(varPart)) - 1) > 1e-04)) { :
## missing value where TRUE/FALSE needed## Error in quantile.default(prop_median, p = c(1, 3)/4) :
## missing values and NaN's not allowed if 'na.rm' is FALSE## Error in if (any(abs(rowSums(as.matrix(varPart)) - 1) > 1e-04)) { :
## missing value where TRUE/FALSE needed7 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 118, now there are 22 samples.mono_visit_norm <- normalize_expt(mono_visit, filter = TRUE, norm = "quant", convert = "cpm",
transform = "log2")## Removing 8966 low-count genes (10975 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$plot)
mono_visit_nb <- normalize_expt(mono_visit, filter = TRUE, convert = "cpm",
batch = "svaseq", transform = "log2")## Removing 8966 low-count genes (10975 remaining).## batch_counts: Before batch/surrogate estimation, 1202 entries are x==0: 0%.## batch_counts: Before batch/surrogate estimation, 13643 entries are 0<x<1: 6%.## Setting 333 low elements to zero.## transform_counts: Found 333 values equal to 0, adding 1 to the matrix.mono_visit_nb_pca <- plot_pca(mono_visit_nb)
pp(file = "images/monocyte_by_visit_nb.png", image = mono_visit_nb_pca$plot)
table(pData(mono_visit_norm)$batch)##
## cure failure lost
## 6 10 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, 1202 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 (10975 remaining).## batch_counts: Before batch/surrogate estimation, 1202 entries are x==0: 0%.## batch_counts: Before batch/surrogate estimation, 13643 entries are 0<x<1: 6%.## Setting 333 low elements to zero.## transform_counts: Found 333 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"))## Deleting the file excel/mono_visit_tables-v202105.xlsx before writing the tables.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, 1202 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 (10975 remaining).## batch_counts: Before batch/surrogate estimation, 1202 entries are x==0: 0%.## batch_counts: Before batch/surrogate estimation, 13643 entries are 0<x<1: 6%.## Setting 333 low elements to zero.## transform_counts: Found 333 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"))## Deleting the file excel/mono_one_vs_tables-v202105.xlsx before writing the tables.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 68b1ce610bf0c750d9a3ed2f6bd2a529b1744c29## This is hpgltools commit: Thu May 27 17:01:01 2021 -0400: 68b1ce610bf0c750d9a3ed2f6bd2a529b1744c29## Saving to tmrc3_02sample_estimation_v202105.rda.xztmp <- loadme(filename = savefile)---
title: "TMRC3 Comprehensive Data Analysis: 202105"
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 <- "202105"
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_20210528.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_expt, ids = shared_ids, method = "keep")
shared_written <- sm(write_expt(shared_expt, excel="excel/genes_shared_across_celltypes.xlsx"))
```

# 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)
```
