samplesheet <- "sample_sheets/tmrc3_samples_20210610.xlsx"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.
3.1.1 Hisat2 expressionsets
The first thing to note is the large range in coverage. There are multiple samples with coverage which is too low to use. These will be removed shortly.
In the following block I immediately exclude any non-coding reads as well.
## Create the expressionset and immediately pass it to a filter
## removing the non protein coding genes.
sanitize_columns <- c("visitnumber", "clinicaloutcome", "donor",
"typeofcells", "clinicalpresentation",
"condition", "batch")
hs_expt <- create_expt(samplesheet,
file_column = "hg38100hisatfile",
savefile = glue::glue("rda/hs_expt_all-v{ver}.rda"),
gene_info = hs_annot) %>%
exclude_genes_expt(column = "gene_biotype", method = "keep",
pattern = "protein_coding", meta_column = "ncrna_lost") %>%
sanitize_expt_metadata(columns = sanitize_columns) %>%
set_expt_factors(columns = sanitize_columns, class = "factor")## Reading the sample metadata.## Dropped 71 rows from the sample metadata because they were blank.## The sample definitions comprises: 173 rows(samples) and 75 columns(metadata fields).## Warning in create_expt(samplesheet, file_column = "hg38100hisatfile", savefile =
## glue::glue("rda/hs_expt_all-v{ver}.rda"), : Some samples were removed when cross
## referencing the samples against the count data.## Matched 21452 annotations and counts.## Bringing together the count matrix and gene information.## Some annotations were lost in merging, setting them to 'undefined'.## The final expressionset has 21481 rows and 155 columns.## Before removal, there were 21481 genes, now there are 19941.## There are 17 samples which kept less than 90 percent counts.## TMRC30015 TMRC30017 TMRC30019 TMRC30044 TMRC30045 TMRC30154 TMRC30097 TMRC30075
## 79.24 85.72 89.75 80.34 73.33 83.20 89.90 86.97
## TMRC30087 TMRC30101 TMRC30104 TMRC30114 TMRC30127 TMRC30120 TMRC30128 TMRC30131
## 83.63 88.41 80.29 87.62 89.49 79.16 82.53 86.82
## TMRC30073
## 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$plot## Warning: ggrepel: 127 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ncrna_lost_df <- as.data.frame(pData(hs_expt)[["ncrna_lost"]])
rownames(ncrna_lost_df) <- rownames(pData(hs_expt))
colnames(ncrna_lost_df) <- "ncrna_lost"
tmpdf <- merge(nonzero$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 155, now there are 152 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 152, now there are 132 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)## Warning in set_expt_colors(hs_clinical, colors = chosen_colors): Colors for the
## following categories are not being used: nullnotapplicable.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$plot,
height = 8, width = 20)
4.3.1 Repeat without the biopsy samples
hs_clinical_nobiop <- hs_clinical %>%
subset_expt(subset = "typeofcells!='biopsy'")## subset_expt(): There were 132, now there are 83 samples.hs_clinical_nobiop_norm <- sm(normalize_expt(hs_clinical_nobiop, filter = TRUE, transform = "log2",
convert = "cpm", norm = "quant"))
clinical_nobiop_pca <- plot_pca(hs_clinical_nobiop_norm, plot_labels = FALSE, cis = NULL,
plot_title = "PCA - clinical samples without biopsies")
pp(file = glue("images/all_clinical_nobiop_nobatch_pca-v{ver}.png"),
image = clinical_nobiop_pca$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 5271 low-count genes (14670 remaining).## batch_counts: Before batch/surrogate estimation, 122582 entries are x==0: 6%.## batch_counts: Before batch/surrogate estimation, 355614 entries are 0<x<1: 18%.## Setting 25766 low elements to zero.## transform_counts: Found 25766 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 83, now there are 68 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
## 1 183 181 263 213 249 232 96 166
## basic_V1 basic_V2
## 1 46 26hs_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 132, now there are 115 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 276 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testing## Working with 0 genes.## Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extentlrt_visit_clinical_test[["favorite_genes"]]## Error in eval(expr, envir, enclos): object 'lrt_visit_clinical_test' not foundlrt_celltype_clinical_test <- deseq_lrt(clinical_nolost, transform = "vst",
interactor_column = "typeofcells",
interest_column = "clinicaloutcome")## converting counts to integer mode## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 53 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testing## Working with 1177 genes.## Warning: `distinct_()` was deprecated in dplyr 0.7.0.
## Please use `distinct()` instead.
## See vignette('programming') for more help## Working with 1175 genes after filtering: minc > 3## Joining, by = "merge"
## Joining, by = "merge"
lrt_celltype_clinical_test[["favorite_genes"]]## genes cluster
## ENSG00000001497 ENSG00000001497 1
## ENSG00000003137 ENSG00000003137 2
## ENSG00000004455 ENSG00000004455 3
## ENSG00000005020 ENSG00000005020 4
## ENSG00000005075 ENSG00000005075 4
## ENSG00000005108 ENSG00000005108 5
## ENSG00000005175 ENSG00000005175 6
## ENSG00000005486 ENSG00000005486 7
## ENSG00000006747 ENSG00000006747 3
## ENSG00000007047 ENSG00000007047 8
## ENSG00000007392 ENSG00000007392 9
## ENSG00000008056 ENSG00000008056 10
## ENSG00000008086 ENSG00000008086 11
## ENSG00000008438 ENSG00000008438 12
## ENSG00000010270 ENSG00000010270 8
## ENSG00000010278 ENSG00000010278 9
## ENSG00000010704 ENSG00000010704 13
## ENSG00000010803 ENSG00000010803 14
## ENSG00000010818 ENSG00000010818 13
## ENSG00000011028 ENSG00000011028 15
## ENSG00000011426 ENSG00000011426 5
## ENSG00000011465 ENSG00000011465 15
## ENSG00000012983 ENSG00000012983 9
## ENSG00000013306 ENSG00000013306 16
## ENSG00000013583 ENSG00000013583 17
## ENSG00000017427 ENSG00000017427 18
## ENSG00000019144 ENSG00000019144 2
## ENSG00000019169 ENSG00000019169 17
## ENSG00000020129 ENSG00000020129 1
## ENSG00000020256 ENSG00000020256 9
## ENSG00000020577 ENSG00000020577 2
## ENSG00000020633 ENSG00000020633 19
## ENSG00000021355 ENSG00000021355 20
## ENSG00000023171 ENSG00000023171 12
## ENSG00000026751 ENSG00000026751 2
## ENSG00000026950 ENSG00000026950 11
## ENSG00000028203 ENSG00000028203 1
## ENSG00000030066 ENSG00000030066 9
## ENSG00000035141 ENSG00000035141 3
## ENSG00000035403 ENSG00000035403 21
## ENSG00000035720 ENSG00000035720 9
## ENSG00000038210 ENSG00000038210 3
## ENSG00000038945 ENSG00000038945 2
## ENSG00000039560 ENSG00000039560 2
## ENSG00000040199 ENSG00000040199 1
## ENSG00000042088 ENSG00000042088 1
## ENSG00000044446 ENSG00000044446 12
## ENSG00000044459 ENSG00000044459 17
## ENSG00000047634 ENSG00000047634 17
## ENSG00000048828 ENSG00000048828 4
## ENSG00000050820 ENSG00000050820 18
## ENSG00000054277 ENSG00000054277 16
## ENSG00000055130 ENSG00000055130 16
## ENSG00000055332 ENSG00000055332 13
## ENSG00000057704 ENSG00000057704 4
## ENSG00000058600 ENSG00000058600 16
## ENSG00000059588 ENSG00000059588 1
## ENSG00000060138 ENSG00000060138 17
## ENSG00000060656 ENSG00000060656 2
## ENSG00000060762 ENSG00000060762 19
## ENSG00000060982 ENSG00000060982 2
## ENSG00000061918 ENSG00000061918 3
## ENSG00000062598 ENSG00000062598 11
## ENSG00000064201 ENSG00000064201 12
## ENSG00000064763 ENSG00000064763 12
## ENSG00000065150 ENSG00000065150 1
## ENSG00000065809 ENSG00000065809 7
## ENSG00000065882 ENSG00000065882 4
## ENSG00000066455 ENSG00000066455 5
## ENSG00000066651 ENSG00000066651 2
## ENSG00000067533 ENSG00000067533 1
## ENSG00000068305 ENSG00000068305 8
## ENSG00000068784 ENSG00000068784 11
## ENSG00000069020 ENSG00000069020 5
## ENSG00000069248 ENSG00000069248 1
## ENSG00000069345 ENSG00000069345 7
## ENSG00000069696 ENSG00000069696 11
## ENSG00000069998 ENSG00000069998 16
## ENSG00000071575 ENSG00000071575 18
## ENSG00000072121 ENSG00000072121 17
## ENSG00000072657 ENSG00000072657 6
## ENSG00000073150 ENSG00000073150 4
## ENSG00000073331 ENSG00000073331 13
## ENSG00000073417 ENSG00000073417 14
## ENSG00000073737 ENSG00000073737 11
## ENSG00000073754 ENSG00000073754 2
## ENSG00000074211 ENSG00000074211 5
## ENSG00000074657 ENSG00000074657 2
## ENSG00000074935 ENSG00000074935 3
## ENSG00000075340 ENSG00000075340 17
## ENSG00000075914 ENSG00000075914 1
## ENSG00000076242 ENSG00000076242 3
## ENSG00000076716 ENSG00000076716 2
## ENSG00000077782 ENSG00000077782 2
## ENSG00000077935 ENSG00000077935 16
## ENSG00000078081 ENSG00000078081 18
## ENSG00000078098 ENSG00000078098 18
## ENSG00000078269 ENSG00000078269 15
## ENSG00000078589 ENSG00000078589 22
## ENSG00000078596 ENSG00000078596 3
## ENSG00000079215 ENSG00000079215 2
## ENSG00000079308 ENSG00000079308 1
## ENSG00000079337 ENSG00000079337 6
## ENSG00000079482 ENSG00000079482 15
## ENSG00000081386 ENSG00000081386 3
## ENSG00000081913 ENSG00000081913 20
## ENSG00000082458 ENSG00000082458 3
## ENSG00000082516 ENSG00000082516 1
## ENSG00000083168 ENSG00000083168 4
## ENSG00000083457 ENSG00000083457 14
## ENSG00000083817 ENSG00000083817 1
## ENSG00000083828 ENSG00000083828 4
## ENSG00000083845 ENSG00000083845 1
## ENSG00000084090 ENSG00000084090 1
## ENSG00000084112 ENSG00000084112 4
## ENSG00000084207 ENSG00000084207 16
## ENSG00000084652 ENSG00000084652 2
## ENSG00000084734 ENSG00000084734 18
## ENSG00000084754 ENSG00000084754 17
## ENSG00000085871 ENSG00000085871 7
## ENSG00000087116 ENSG00000087116 15
## ENSG00000087237 ENSG00000087237 6
## ENSG00000087269 ENSG00000087269 16
## ENSG00000087586 ENSG00000087586 5
## ENSG00000088205 ENSG00000088205 15
## ENSG00000088280 ENSG00000088280 2
## ENSG00000088543 ENSG00000088543 3
## ENSG00000088726 ENSG00000088726 3
## ENSG00000088826 ENSG00000088826 1
## ENSG00000088827 ENSG00000088827 17
## ENSG00000088836 ENSG00000088836 1
## ENSG00000088992 ENSG00000088992 19
## ENSG00000089012 ENSG00000089012 18
## ENSG00000089127 ENSG00000089127 2
## ENSG00000089818 ENSG00000089818 4
## ENSG00000090013 ENSG00000090013 17
## ENSG00000090612 ENSG00000090612 1
## ENSG00000090674 ENSG00000090674 7
## ENSG00000091409 ENSG00000091409 5
## ENSG00000091640 ENSG00000091640 16
## ENSG00000091972 ENSG00000091972 5
## ENSG00000092445 ENSG00000092445 2
## ENSG00000095002 ENSG00000095002 3
## ENSG00000095464 ENSG00000095464 9
## ENSG00000097021 ENSG00000097021 3
## ENSG00000099260 ENSG00000099260 3
## ENSG00000099715 ENSG00000099715 16
## ENSG00000099783 ENSG00000099783 8
## ENSG00000100036 ENSG00000100036 16
## ENSG00000100065 ENSG00000100065 3
## ENSG00000100124 ENSG00000100124 5
## ENSG00000100138 ENSG00000100138 1
## ENSG00000100216 ENSG00000100216 16
## ENSG00000100221 ENSG00000100221 7
## ENSG00000100281 ENSG00000100281 19
## ENSG00000100288 ENSG00000100288 1
## ENSG00000100292 ENSG00000100292 17
## ENSG00000100335 ENSG00000100335 23
## ENSG00000100353 ENSG00000100353 15
## ENSG00000100354 ENSG00000100354 4
## ENSG00000100376 ENSG00000100376 16
## ENSG00000100413 ENSG00000100413 23
## ENSG00000100422 ENSG00000100422 4
## ENSG00000100558 ENSG00000100558 1
## ENSG00000100583 ENSG00000100583 11
## ENSG00000100596 ENSG00000100596 8
## ENSG00000100600 ENSG00000100600 15
## ENSG00000100721 ENSG00000100721 2
## ENSG00000100842 ENSG00000100842 18
## ENSG00000100908 ENSG00000100908 6
## ENSG00000100997 ENSG00000100997 1
## ENSG00000101000 ENSG00000101000 5
## ENSG00000101188 ENSG00000101188 10
## ENSG00000101189 ENSG00000101189 23
## ENSG00000101255 ENSG00000101255 17
## ENSG00000101342 ENSG00000101342 2
## ENSG00000101347 ENSG00000101347 13
## ENSG00000101361 ENSG00000101361 3
## ENSG00000101384 ENSG00000101384 3
## ENSG00000101391 ENSG00000101391 16
## ENSG00000101608 ENSG00000101608 6
## ENSG00000101665 ENSG00000101665 23
## ENSG00000101695 ENSG00000101695 9
## ENSG00000101842 ENSG00000101842 2
## ENSG00000101916 ENSG00000101916 13
## ENSG00000102048 ENSG00000102048 2
## ENSG00000102178 ENSG00000102178 16
## ENSG00000102221 ENSG00000102221 15
## ENSG00000102290 ENSG00000102290 16
## ENSG00000102384 ENSG00000102384 5
## ENSG00000102409 ENSG00000102409 3
## ENSG00000102572 ENSG00000102572 8
## ENSG00000102738 ENSG00000102738 1
## ENSG00000102854 ENSG00000102854 17
## ENSG00000102967 ENSG00000102967 13
## ENSG00000102978 ENSG00000102978 20
## ENSG00000103047 ENSG00000103047 16
## ENSG00000103257 ENSG00000103257 5
## ENSG00000103274 ENSG00000103274 16
## ENSG00000103740 ENSG00000103740 3
## ENSG00000104133 ENSG00000104133 12
## ENSG00000104213 ENSG00000104213 2
## ENSG00000104218 ENSG00000104218 16
## ENSG00000104267 ENSG00000104267 1
## ENSG00000104321 ENSG00000104321 2
## ENSG00000104635 ENSG00000104635 3
## ENSG00000104689 ENSG00000104689 23
## ENSG00000104738 ENSG00000104738 1
## ENSG00000104936 ENSG00000104936 2
## ENSG00000104969 ENSG00000104969 8
## ENSG00000104980 ENSG00000104980 16
## ENSG00000105146 ENSG00000105146 5
## ENSG00000105245 ENSG00000105245 24
## ENSG00000105281 ENSG00000105281 16
## ENSG00000105366 ENSG00000105366 9
## ENSG00000105499 ENSG00000105499 2
## ENSG00000105612 ENSG00000105612 17
## ENSG00000105656 ENSG00000105656 4
## ENSG00000105928 ENSG00000105928 2
## ENSG00000105939 ENSG00000105939 11
## ENSG00000105948 ENSG00000105948 6
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## ENSG00000157933 ENSG00000157933 7
## ENSG00000157985 ENSG00000157985 15
## ENSG00000158062 ENSG00000158062 17
## ENSG00000158292 ENSG00000158292 9
## ENSG00000158373 ENSG00000158373 6
## ENSG00000158402 ENSG00000158402 5
## ENSG00000158406 ENSG00000158406 6
## ENSG00000158710 ENSG00000158710 4
## ENSG00000158715 ENSG00000158715 14
## ENSG00000158815 ENSG00000158815 12
## ENSG00000158856 ENSG00000158856 9
## ENSG00000159216 ENSG00000159216 14
## ENSG00000159261 ENSG00000159261 5
## ENSG00000159346 ENSG00000159346 4
## ENSG00000159784 ENSG00000159784 15
## ENSG00000160094 ENSG00000160094 14
## ENSG00000160113 ENSG00000160113 3
## ENSG00000160179 ENSG00000160179 4
## ENSG00000160191 ENSG00000160191 24
## ENSG00000160271 ENSG00000160271 15
## ENSG00000160789 ENSG00000160789 1
## ENSG00000160791 ENSG00000160791 2
## ENSG00000160867 ENSG00000160867 6
## ENSG00000160932 ENSG00000160932 13
## ENSG00000161533 ENSG00000161533 13
## ENSG00000161692 ENSG00000161692 11
## ENSG00000161835 ENSG00000161835 14
## ENSG00000161960 ENSG00000161960 3
## ENSG00000161981 ENSG00000161981 9
## ENSG00000162139 ENSG00000162139 16
## ENSG00000162367 ENSG00000162367 9
## ENSG00000162377 ENSG00000162377 15
## ENSG00000162390 ENSG00000162390 9
## ENSG00000162408 ENSG00000162408 23
## ENSG00000162614 ENSG00000162614 1
## ENSG00000162627 ENSG00000162627 2
## ENSG00000162645 ENSG00000162645 13
## ENSG00000162654 ENSG00000162654 13
## ENSG00000162669 ENSG00000162669 15
## ENSG00000162714 ENSG00000162714 17
## ENSG00000162722 ENSG00000162722 14
## ENSG00000162729 ENSG00000162729 1
## ENSG00000162757 ENSG00000162757 5
## ENSG00000162877 ENSG00000162877 18
## ENSG00000162928 ENSG00000162928 3
## ENSG00000163116 ENSG00000163116 6
## ENSG00000163121 ENSG00000163121 18
## ENSG00000163156 ENSG00000163156 4
## ENSG00000163328 ENSG00000163328 13
## ENSG00000163399 ENSG00000163399 16
## ENSG00000163449 ENSG00000163449 2
## ENSG00000163466 ENSG00000163466 4
## ENSG00000163513 ENSG00000163513 11
## ENSG00000163521 ENSG00000163521 17
## ENSG00000163644 ENSG00000163644 18
## ENSG00000163666 ENSG00000163666 2
## ENSG00000163702 ENSG00000163702 17
## ENSG00000163840 ENSG00000163840 13
## ENSG00000163879 ENSG00000163879 5
## ENSG00000163950 ENSG00000163950 6
## ENSG00000163995 ENSG00000163995 15
## ENSG00000164116 ENSG00000164116 1
## ENSG00000164124 ENSG00000164124 17
## ENSG00000164125 ENSG00000164125 17
## ENSG00000164136 ENSG00000164136 17
## ENSG00000164211 ENSG00000164211 14
## ENSG00000164292 ENSG00000164292 9
## ENSG00000164296 ENSG00000164296 1
## ENSG00000164309 ENSG00000164309 5
## ENSG00000164379 ENSG00000164379 15
## ENSG00000164406 ENSG00000164406 1
## ENSG00000164440 ENSG00000164440 2
## ENSG00000164466 ENSG00000164466 9
## ENSG00000164543 ENSG00000164543 11
## ENSG00000164649 ENSG00000164649 9
## ENSG00000164741 ENSG00000164741 2
## ENSG00000164818 ENSG00000164818 16
## ENSG00000165028 ENSG00000165028 9
## ENSG00000165097 ENSG00000165097 4
## ENSG00000165259 ENSG00000165259 16
## ENSG00000165283 ENSG00000165283 1
## ENSG00000165527 ENSG00000165527 19
## ENSG00000165661 ENSG00000165661 12
## ENSG00000165685 ENSG00000165685 17
## ENSG00000165716 ENSG00000165716 9
## ENSG00000165733 ENSG00000165733 1
## ENSG00000165804 ENSG00000165804 15
## ENSG00000165819 ENSG00000165819 22
## ENSG00000165943 ENSG00000165943 7
## ENSG00000165949 ENSG00000165949 2
## ENSG00000165966 ENSG00000165966 3
## ENSG00000166016 ENSG00000166016 12
## ENSG00000166123 ENSG00000166123 1
## ENSG00000166164 ENSG00000166164 17
## ENSG00000166199 ENSG00000166199 1
## ENSG00000166257 ENSG00000166257 18
## ENSG00000166448 ENSG00000166448 2
## ENSG00000166484 ENSG00000166484 19
## ENSG00000166503 ENSG00000166503 3
## ENSG00000166508 ENSG00000166508 22
## ENSG00000166510 ENSG00000166510 5
## ENSG00000166529 ENSG00000166529 9
## ENSG00000166592 ENSG00000166592 17
## ENSG00000166669 ENSG00000166669 15
## ENSG00000166750 ENSG00000166750 1
## ENSG00000166788 ENSG00000166788 1
## ENSG00000166801 ENSG00000166801 24
## ENSG00000166881 ENSG00000166881 1
## ENSG00000166928 ENSG00000166928 17
## ENSG00000166949 ENSG00000166949 14
## ENSG00000167193 ENSG00000167193 20
## ENSG00000167291 ENSG00000167291 15
## ENSG00000167543 ENSG00000167543 9
## ENSG00000167566 ENSG00000167566 10
## ENSG00000167680 ENSG00000167680 15
## ENSG00000167772 ENSG00000167772 2
## ENSG00000167858 ENSG00000167858 24
## ENSG00000167925 ENSG00000167925 8
## ENSG00000167994 ENSG00000167994 15
## ENSG00000167995 ENSG00000167995 13
## ENSG00000168016 ENSG00000168016 11
## ENSG00000168038 ENSG00000168038 4
## ENSG00000168209 ENSG00000168209 7
## ENSG00000168256 ENSG00000168256 17
## ENSG00000168264 ENSG00000168264 19
## ENSG00000168268 ENSG00000168268 1
## ENSG00000168273 ENSG00000168273 16
## ENSG00000168421 ENSG00000168421 9
## ENSG00000168439 ENSG00000168439 2
## ENSG00000168569 ENSG00000168569 1
## ENSG00000168679 ENSG00000168679 11
## ENSG00000168685 ENSG00000168685 2
## ENSG00000168795 ENSG00000168795 3
## ENSG00000168961 ENSG00000168961 8
## ENSG00000168994 ENSG00000168994 2
## ENSG00000169047 ENSG00000169047 1
## ENSG00000169239 ENSG00000169239 23
## ENSG00000169432 ENSG00000169432 6
## ENSG00000169499 ENSG00000169499 12
## ENSG00000169860 ENSG00000169860 1
## ENSG00000169871 ENSG00000169871 6
## ENSG00000169908 ENSG00000169908 5
## ENSG00000169919 ENSG00000169919 23
## ENSG00000169946 ENSG00000169946 5
## ENSG00000170027 ENSG00000170027 2
## ENSG00000170037 ENSG00000170037 16
## ENSG00000170175 ENSG00000170175 18
## ENSG00000170234 ENSG00000170234 11
## ENSG00000170298 ENSG00000170298 9
## ENSG00000170370 ENSG00000170370 18
## ENSG00000170458 ENSG00000170458 10
## ENSG00000170473 ENSG00000170473 1
## ENSG00000170558 ENSG00000170558 6
## ENSG00000170581 ENSG00000170581 13
## ENSG00000170677 ENSG00000170677 16
## ENSG00000170989 ENSG00000170989 9
## ENSG00000171100 ENSG00000171100 12
## ENSG00000171126 ENSG00000171126 15
## ENSG00000171135 ENSG00000171135 2
## ENSG00000171208 ENSG00000171208 16
## ENSG00000171262 ENSG00000171262 3
## ENSG00000171365 ENSG00000171365 17
## ENSG00000171403 ENSG00000171403 18
## ENSG00000171604 ENSG00000171604 17
## ENSG00000171617 ENSG00000171617 14
## ENSG00000171729 ENSG00000171729 15
## ENSG00000171812 ENSG00000171812 17
## ENSG00000171877 ENSG00000171877 3
## ENSG00000172123 ENSG00000172123 21
## ENSG00000172159 ENSG00000172159 13
## ENSG00000172331 ENSG00000172331 6
## ENSG00000172403 ENSG00000172403 18
## ENSG00000172428 ENSG00000172428 16
## ENSG00000172594 ENSG00000172594 2
## ENSG00000172716 ENSG00000172716 2
## ENSG00000172794 ENSG00000172794 12
## ENSG00000172828 ENSG00000172828 1
## ENSG00000172888 ENSG00000172888 9
## ENSG00000172893 ENSG00000172893 3
## ENSG00000173124 ENSG00000173124 18
## ENSG00000173156 ENSG00000173156 2
## ENSG00000173166 ENSG00000173166 17
## ENSG00000173198 ENSG00000173198 12
## ENSG00000173391 ENSG00000173391 14
## ENSG00000173409 ENSG00000173409 1
## ENSG00000173457 ENSG00000173457 1
## ENSG00000173511 ENSG00000173511 3
## ENSG00000173530 ENSG00000173530 17
## ENSG00000173801 ENSG00000173801 1
## ENSG00000173818 ENSG00000173818 23
## ENSG00000173917 ENSG00000173917 2
## ENSG00000173947 ENSG00000173947 1
## ENSG00000174177 ENSG00000174177 14
## ENSG00000174197 ENSG00000174197 9
## ENSG00000174236 ENSG00000174236 3
## ENSG00000174238 ENSG00000174238 20
## ENSG00000174371 ENSG00000174371 3
## ENSG00000174442 ENSG00000174442 3
## ENSG00000174705 ENSG00000174705 2
## ENSG00000174943 ENSG00000174943 12
## ENSG00000174989 ENSG00000174989 9
## ENSG00000175130 ENSG00000175130 7
## ENSG00000175414 ENSG00000175414 2
## ENSG00000175544 ENSG00000175544 1
## ENSG00000175691 ENSG00000175691 3
## ENSG00000175899 ENSG00000175899 2
## ENSG00000176105 ENSG00000176105 3
## ENSG00000176125 ENSG00000176125 18
## ENSG00000176148 ENSG00000176148 14
## ENSG00000176903 ENSG00000176903 3
## ENSG00000177119 ENSG00000177119 16
## ENSG00000177294 ENSG00000177294 13
## ENSG00000177311 ENSG00000177311 3
## ENSG00000177380 ENSG00000177380 2
## ENSG00000177409 ENSG00000177409 6
## ENSG00000177469 ENSG00000177469 1
## ENSG00000177479 ENSG00000177479 23
## ENSG00000177627 ENSG00000177627 2
## ENSG00000177646 ENSG00000177646 1
## ENSG00000177917 ENSG00000177917 11
## ENSG00000177989 ENSG00000177989 13
## ENSG00000178105 ENSG00000178105 1
## ENSG00000178209 ENSG00000178209 23
## ENSG00000178338 ENSG00000178338 1
## ENSG00000178573 ENSG00000178573 2
## ENSG00000178685 ENSG00000178685 4
## ENSG00000178700 ENSG00000178700 18
## ENSG00000178726 ENSG00000178726 13
## ENSG00000178741 ENSG00000178741 16
## ENSG00000178896 ENSG00000178896 20
## ENSG00000178982 ENSG00000178982 16
## ENSG00000179044 ENSG00000179044 13
## ENSG00000179144 ENSG00000179144 2
## ENSG00000179262 ENSG00000179262 23
## ENSG00000179271 ENSG00000179271 1
## ENSG00000179409 ENSG00000179409 1
## ENSG00000179455 ENSG00000179455 1
## ENSG00000179776 ENSG00000179776 2
## ENSG00000179889 ENSG00000179889 9
## ENSG00000179988 ENSG00000179988 9
## ENSG00000180044 ENSG00000180044 3
## ENSG00000180251 ENSG00000180251 6
## ENSG00000180376 ENSG00000180376 9
## ENSG00000180537 ENSG00000180537 3
## ENSG00000180543 ENSG00000180543 1
## ENSG00000180628 ENSG00000180628 4
## ENSG00000181038 ENSG00000181038 22
## ENSG00000181045 ENSG00000181045 17
## ENSG00000181218 ENSG00000181218 17
## ENSG00000181350 ENSG00000181350 1
## ENSG00000181392 ENSG00000181392 11
## ENSG00000181409 ENSG00000181409 13
## ENSG00000181523 ENSG00000181523 17
## ENSG00000181852 ENSG00000181852 19
## ENSG00000181873 ENSG00000181873 16
## ENSG00000181929 ENSG00000181929 6
## ENSG00000182158 ENSG00000182158 9
## ENSG00000182173 ENSG00000182173 9
## ENSG00000182179 ENSG00000182179 12
## ENSG00000182183 ENSG00000182183 22
## ENSG00000182240 ENSG00000182240 9
## ENSG00000182263 ENSG00000182263 5
## ENSG00000182378 ENSG00000182378 1
## ENSG00000182379 ENSG00000182379 2
## ENSG00000182504 ENSG00000182504 24
## ENSG00000182557 ENSG00000182557 14
## ENSG00000182901 ENSG00000182901 20
## ENSG00000182952 ENSG00000182952 24
## ENSG00000182973 ENSG00000182973 11
## ENSG00000182986 ENSG00000182986 1
## ENSG00000183044 ENSG00000183044 12
## ENSG00000183087 ENSG00000183087 2
## ENSG00000183091 ENSG00000183091 6
## ENSG00000183117 ENSG00000183117 2
## ENSG00000183172 ENSG00000183172 16
## ENSG00000183185 ENSG00000183185 15
## ENSG00000183283 ENSG00000183283 4
## ENSG00000183421 ENSG00000183421 5
## ENSG00000183431 ENSG00000183431 7
## ENSG00000183527 ENSG00000183527 1
## ENSG00000183569 ENSG00000183569 14
## ENSG00000183578 ENSG00000183578 2
## ENSG00000183617 ENSG00000183617 16
## ENSG00000183647 ENSG00000183647 3
## ENSG00000183762 ENSG00000183762 13
## ENSG00000183763 ENSG00000183763 3
## ENSG00000183785 ENSG00000183785 21
## ENSG00000183801 ENSG00000183801 1
## ENSG00000183853 ENSG00000183853 15
## ENSG00000183943 ENSG00000183943 14
## ENSG00000183955 ENSG00000183955 7
## ENSG00000184060 ENSG00000184060 17
## ENSG00000184113 ENSG00000184113 3
## ENSG00000184384 ENSG00000184384 13
## ENSG00000184402 ENSG00000184402 11
## ENSG00000184787 ENSG00000184787 16
## ENSG00000184831 ENSG00000184831 5
## ENSG00000184898 ENSG00000184898 24
## ENSG00000184979 ENSG00000184979 21
## ENSG00000185024 ENSG00000185024 14
## ENSG00000185033 ENSG00000185033 13
## ENSG00000185043 ENSG00000185043 7
## ENSG00000185477 ENSG00000185477 15
## ENSG00000185480 ENSG00000185480 3
## ENSG00000185561 ENSG00000185561 5
## ENSG00000185614 ENSG00000185614 3
## ENSG00000185650 ENSG00000185650 13
## ENSG00000185669 ENSG00000185669 4
## ENSG00000185686 ENSG00000185686 3
## ENSG00000185736 ENSG00000185736 6
## ENSG00000185842 ENSG00000185842 6
## ENSG00000186193 ENSG00000186193 16
## ENSG00000186197 ENSG00000186197 5
## ENSG00000186281 ENSG00000186281 1
## ENSG00000186470 ENSG00000186470 24
## ENSG00000186818 ENSG00000186818 15
## ENSG00000186951 ENSG00000186951 14
## ENSG00000187079 ENSG00000187079 5
## ENSG00000187097 ENSG00000187097 9
## ENSG00000187105 ENSG00000187105 9
## ENSG00000187166 ENSG00000187166 8
## ENSG00000187210 ENSG00000187210 9
## ENSG00000187231 ENSG00000187231 13
## ENSG00000187566 ENSG00000187566 1
## ENSG00000187569 ENSG00000187569 24
## ENSG00000187758 ENSG00000187758 3
## ENSG00000187808 ENSG00000187808 17
## ENSG00000187997 ENSG00000187997 3
## ENSG00000188037 ENSG00000188037 10
## ENSG00000188452 ENSG00000188452 11
## ENSG00000188554 ENSG00000188554 6
## ENSG00000188636 ENSG00000188636 16
## ENSG00000188672 ENSG00000188672 24
## ENSG00000188886 ENSG00000188886 13
## ENSG00000188921 ENSG00000188921 12
## ENSG00000188938 ENSG00000188938 20
## ENSG00000189001 ENSG00000189001 5
## ENSG00000189013 ENSG00000189013 15
## ENSG00000189067 ENSG00000189067 20
## ENSG00000189077 ENSG00000189077 4
## ENSG00000189159 ENSG00000189159 22
## ENSG00000189195 ENSG00000189195 24
## ENSG00000189337 ENSG00000189337 6
## ENSG00000189420 ENSG00000189420 19
## ENSG00000196072 ENSG00000196072 4
## ENSG00000196081 ENSG00000196081 3
## ENSG00000196123 ENSG00000196123 17
## ENSG00000196126 ENSG00000196126 16
## ENSG00000196141 ENSG00000196141 18
## ENSG00000196199 ENSG00000196199 11
## ENSG00000196247 ENSG00000196247 6
## ENSG00000196305 ENSG00000196305 1
## ENSG00000196358 ENSG00000196358 4
## ENSG00000196369 ENSG00000196369 13
## ENSG00000196378 ENSG00000196378 14
## ENSG00000196388 ENSG00000196388 1
## ENSG00000196405 ENSG00000196405 9
## ENSG00000196526 ENSG00000196526 3
## ENSG00000196549 ENSG00000196549 13
## ENSG00000196597 ENSG00000196597 18
## ENSG00000196652 ENSG00000196652 6
## ENSG00000196872 ENSG00000196872 6
## ENSG00000196923 ENSG00000196923 4
## ENSG00000196943 ENSG00000196943 14
## ENSG00000197008 ENSG00000197008 5
## ENSG00000197093 ENSG00000197093 6
## ENSG00000197122 ENSG00000197122 16
## ENSG00000197124 ENSG00000197124 15
## ENSG00000197283 ENSG00000197283 9
## ENSG00000197451 ENSG00000197451 16
## ENSG00000197461 ENSG00000197461 3
## ENSG00000197557 ENSG00000197557 5
## ENSG00000197603 ENSG00000197603 1
## ENSG00000197653 ENSG00000197653 22
## ENSG00000197766 ENSG00000197766 12
## ENSG00000197782 ENSG00000197782 3
## ENSG00000197857 ENSG00000197857 22
## ENSG00000197992 ENSG00000197992 6
## ENSG00000198056 ENSG00000198056 3
## ENSG00000198133 ENSG00000198133 14
## ENSG00000198178 ENSG00000198178 24
## ENSG00000198286 ENSG00000198286 1
## ENSG00000198393 ENSG00000198393 14
## ENSG00000198612 ENSG00000198612 1
## ENSG00000198624 ENSG00000198624 8
## ENSG00000198690 ENSG00000198690 4
## ENSG00000198700 ENSG00000198700 16
## ENSG00000198795 ENSG00000198795 5
## ENSG00000198805 ENSG00000198805 16
## ENSG00000198829 ENSG00000198829 18
## ENSG00000198959 ENSG00000198959 2
## ENSG00000203546 ENSG00000203546 1
## ENSG00000203814 ENSG00000203814 13
## ENSG00000204103 ENSG00000204103 17
## ENSG00000204388 ENSG00000204388 2
## ENSG00000204389 ENSG00000204389 6
## ENSG00000204520 ENSG00000204520 6
## ENSG00000204568 ENSG00000204568 1
## ENSG00000204590 ENSG00000204590 20
## ENSG00000204869 ENSG00000204869 18
## ENSG00000204920 ENSG00000204920 6
## ENSG00000204991 ENSG00000204991 18
## ENSG00000205038 ENSG00000205038 3
## ENSG00000205060 ENSG00000205060 1
## ENSG00000205090 ENSG00000205090 11
## ENSG00000205362 ENSG00000205362 18
## ENSG00000205420 ENSG00000205420 15
## ENSG00000205639 ENSG00000205639 14
## ENSG00000205730 ENSG00000205730 15
## ENSG00000205937 ENSG00000205937 19
## ENSG00000213588 ENSG00000213588 1
## ENSG00000213859 ENSG00000213859 3
## ENSG00000213923 ENSG00000213923 3
## ENSG00000214872 ENSG00000214872 4
## ENSG00000215788 ENSG00000215788 2
## ENSG00000219481 ENSG00000219481 9
## ENSG00000221963 ENSG00000221963 13
## ENSG00000222009 ENSG00000222009 20
## ENSG00000223609 ENSG00000223609 18
## ENSG00000225921 ENSG00000225921 1
## ENSG00000227051 ENSG00000227051 18
## ENSG00000235750 ENSG00000235750 13
## ENSG00000239920 ENSG00000239920 11
## ENSG00000240021 ENSG00000240021 1
## ENSG00000240445 ENSG00000240445 5
## ENSG00000241058 ENSG00000241058 9
## ENSG00000244165 ENSG00000244165 16
## ENSG00000244242 ENSG00000244242 4
## ENSG00000244405 ENSG00000244405 15
## ENSG00000248405 ENSG00000248405 24
## ENSG00000249242 ENSG00000249242 3
## ENSG00000250264 ENSG00000250264 4
## ENSG00000254827 ENSG00000254827 17
## ENSG00000254979 ENSG00000254979 19
## ENSG00000256043 ENSG00000256043 16
## ENSG00000256235 ENSG00000256235 14
## ENSG00000257335 ENSG00000257335 4
## ENSG00000259330 ENSG00000259330 23
## ENSG00000260861 ENSG00000260861 4
## ENSG00000261652 ENSG00000261652 18
## ENSG00000261832 ENSG00000261832 12
## ENSG00000263001 ENSG00000263001 23
## ENSG00000263528 ENSG00000263528 17
## ENSG00000263715 ENSG00000263715 1
## ENSG00000266028 ENSG00000266028 13
## ENSG00000266338 ENSG00000266338 1
## ENSG00000266412 ENSG00000266412 13
## ENSG00000266524 ENSG00000266524 1
## ENSG00000268182 ENSG00000268182 9
## ENSG00000268350 ENSG00000268350 15
## ENSG00000273559 ENSG00000273559 4
## ENSG00000273802 ENSG00000273802 24
## ENSG00000275895 ENSG00000275895 7
## ENSG00000276023 ENSG00000276023 5
## ENSG00000277075 ENSG00000277075 6
## ENSG00000277224 ENSG00000277224 11
## ENSG00000278599 ENSG00000278599 4
## ENSG00000282988 ENSG00000282988 6
## ENSG00000283149 ENSG00000283149 15
## ENSG00000283977 ENSG00000283977 19
## ENSG00000285253 ENSG00000285253 1
## ENSG00000285708 ENSG00000285708 12
## ENSG00000286239 ENSG00000286239 13
## ENSG00000286261 ENSG00000286261 94.4.2 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 83 samples which kept less than 90 percent counts.## X1017n1 X1017m1 X1034n1 X1034n2 X1034m2 X1034m2. X2052e1 X2052m2
## 0.4344 0.4209 2.3933 2.8302 1.3082 1.2597 0.6909 0.6058
## X2052n2 X2052m3 X2052n3 X2065m1 X2065n1 X2066m1 X2066n1 X2065m2
## 1.3510 0.6899 1.1861 1.1063 3.1233 0.4222 0.7710 0.6235
## X2065n2 X2065e2 X2066m2 X2066n2 X2066e2 X2068m1 X2068n1 X2068e1
## 0.8864 1.3413 0.5375 0.9750 1.1821 0.5670 0.8161 1.3970
## X2072m1 X2072n1 X2072e1 X2071m1 X2071n1 X2073m1 X2073n1 X2073e1
## 0.5170 0.6085 0.8754 0.7574 1.7531 0.6883 1.8493 0.9073
## X2068m2 X2068n2 X2068e2 X2072m2 X2072n2 X2072e2 X2073m2 X2073n2
## 0.3942 0.5760 0.8514 0.4248 0.5737 0.6222 1.0724 2.7964
## X2073e2 X2066m3 X2066n3 X2066e3 X2065e3 X2068m3 X2068n3 X2068e3
## 0.8723 0.4794 0.6782 0.6991 0.6026 0.4552 0.7345 0.8428
## X2072m3 X2072n3 X2072e3 X2073m3 X2073n3 X2073e3 X2162m1 X2162n1
## 0.7395 1.9132 0.8600 0.5342 0.7511 0.6015 0.4937 0.6971
## X2162e1 X2162n2 X2162e2 X2162n3 X2162e3 X2167m1 X2167n1 X2167e1
## 1.0733 0.9258 1.1809 0.7148 0.7263 0.6428 0.9420 1.3436
## X2168m1 X2168n1 X2168e1 X2168m2 X2168n2 X2168e2 X2167m2 X2167n3
## 1.0901 2.2668 1.1321 0.9822 2.2914 0.9692 0.4135 1.4135
## X2167e3 X2168m3 X2168n3 X2168e3 X2172n1 X2172e1 X1168m1 X1168n1
## 1.4795 1.3873 3.6918 1.1862 0.3999 0.5349 0.7780 1.3948
## X1168m2 X1168e2 X1168n3
## 0.7665 0.5925 0.9169small_norm <- sm(normalize_expt(small_expt, transform = "log2", convert = "cpm",
norm = "quant", filter = TRUE))
plot_pca(small_norm)$plot## Warning: ggrepel: 21 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, 894 entries are 0<x<1: 5%.## Setting 801 low elements to zero.## transform_counts: Found 801 values equal to 0, adding 1 to the matrix.plot_pca(small_nb)$plot## Warning: ggrepel: 79 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## DESeq2 MA plot of failure / cure
hs_clinic_table[["plots"]][["failure_vs_cure"]][["deseq_ma_plots"]]$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 249 genes against hsapiens.## GO search found 106 hits.## Performing gProfiler KEGG search of 249 genes against hsapiens.## KEGG search found 10 hits.## Performing gProfiler REAC search of 249 genes against hsapiens.## REAC search found 10 hits.## Performing gProfiler MI search of 249 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 249 genes against hsapiens.## TF search found 55 hits.## Performing gProfiler CORUM search of 249 genes against hsapiens.## CORUM search found 1 hits.## Performing gProfiler HP search of 249 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 232 genes against hsapiens.## GO search found 58 hits.## Performing gProfiler KEGG search of 232 genes against hsapiens.## KEGG search found 8 hits.## Performing gProfiler REAC search of 232 genes against hsapiens.## REAC search found 5 hits.## Performing gProfiler MI search of 232 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 232 genes against hsapiens.## TF search found 11 hits.## Performing gProfiler CORUM search of 232 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 232 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 152, now there are 28 samples.## Warning in set_expt_colors(., colors = chosen_colors): Colors for the following
## categories are not being used: nullnotapplicable.## 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 8906 low-count genes (11035 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 8906 low-count genes (11035 remaining).## batch_counts: Before batch/surrogate estimation, 1433 entries are x==0: 0%.## batch_counts: Before batch/surrogate estimation, 18423 entries are 0<x<1: 6%.## Setting 512 low elements to zero.## transform_counts: Found 512 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 126 genes against hsapiens.## GO search found 32 hits.## Performing gProfiler KEGG search of 126 genes against hsapiens.## KEGG search found 6 hits.## Performing gProfiler REAC search of 126 genes against hsapiens.## REAC search found 3 hits.## Performing gProfiler MI search of 126 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 126 genes against hsapiens.## TF search found 17 hits.## Performing gProfiler CORUM search of 126 genes against hsapiens.## CORUM search found 1 hits.## Performing gProfiler HP search of 126 genes against hsapiens.## HP search found 20 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 281 genes against hsapiens.## GO search found 98 hits.## Performing gProfiler KEGG search of 281 genes against hsapiens.## KEGG search found 1 hits.## Performing gProfiler REAC search of 281 genes against hsapiens.## REAC search found 8 hits.## Performing gProfiler MI search of 281 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 281 genes against hsapiens.## TF search found 126 hits.## Performing gProfiler CORUM search of 281 genes against hsapiens.## CORUM search found 2 hits.## Performing gProfiler HP search of 281 genes against hsapiens.## HP search found 1 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: 126, after conversion: 127.## Before conversion: 227921, after conversion: 35341.## Found 122 go_db genes and 127 length_db genes out of 127.mono_down_goseq_msig <- goseq_msigdb(sig_genes = downs, signatures = broad_c7,
signature_category = "c7", length_db = hs_length)## Before conversion: 281, after conversion: 280.## Before conversion: 227921, after conversion: 35341.## Found 269 go_db genes and 280 length_db genes out of 280.5.2.2.6 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 152, now there are 31 samples.## Warning in set_expt_colors(., colors = chosen_colors): Colors for the following
## categories are not being used: nullnotapplicable.save_result <- save(neut, file = "rda/neutrophil_expt.rda")
neut_norm <- sm(normalize_expt(neut, convert = "cpm", filter = TRUE, transform = "log2"))
plt <- plot_pca(neut_norm, plot_labels = FALSE)$plot
pp(file = glue("images/neut_pca_normalized-v{ver}.pdf"), image = plt)
neut_nb <- sm(normalize_expt(neut, convert = "cpm", filter = TRUE,
transform = "log2", batch = "svaseq"))
plt <- plot_pca(neut_nb, plot_labels = FALSE)$plot
pp(file = glue("images/neut_pca_normalized_svaseq-v{ver}.pdf"), image = plt)
5.2.4 DE of Netrophil samples
5.2.4.1 Without sva
neut_de <- sm(all_pairwise(neut, model_batch = FALSE, filter = TRUE))neut_tables <- sm(combine_de_tables(
neut_de, keepers = keepers,
excel = glue::glue("excel/neutrophil_clinical_all_tables-v{ver}.xlsx")))
written <- write_xlsx(data = neut_tables[["data"]][[1]],
excel = glue::glue("excel/neutrophil_clinical_table-v{ver}.xlsx"))
neut_sig <- sm(extract_significant_genes(neut_tables, according_to = "deseq"))
written <- write_xlsx(data = neut_sig[["deseq"]][["ups"]][[1]],
excel = glue::glue("excel/neutrophil_clinical_sigup-v{ver}.xlsx"))
written <- write_xlsx(data = neut_sig[["deseq"]][["downs"]][[1]],
excel = glue::glue("excel/neutrophil_clinical_sigdown-v{ver}.xlsx"))
neut_pct_sig <- sm(extract_significant_genes(neut_tables, n = 200, lfc = NULL,
p = NULL, according_to = "deseq"))
written <- write_xlsx(data = neut_sig[["deseq"]][["ups"]][[1]],
excel = glue::glue("excel/neutrophil_clinical_sigup_pct-v{ver}.xlsx"))
written <- write_xlsx(data = neut_sig[["deseq"]][["downs"]][[1]],
excel = glue::glue("excel/neutrophil_clinical_sigdown_pct-v{ver}.xlsx"))
neut_cpm <- sm(normalize_expt(neut, convert = "cpm"))
written <- write_xlsx(data = exprs(neut_cpm),
excel = glue::glue("excel/neutrophil_cpm_before_batch-v{ver}.xlsx"))
neut_bcpm <- sm(normalize_expt(neut, filter = TRUE, batch = "svaseq", convert = "cpm"))
written <- write_xlsx(data = exprs(neut_bcpm),
excel = glue::glue("excel/neutrophil_cpm_after_batch-v{ver}.xlsx"))5.2.4.2 With sva
neut_de_sva <- sm(all_pairwise(neut, model_batch = "svaseq", filter = TRUE))neut_tables_sva <- sm(combine_de_tables(
neut_de_sva, keepers = keepers,
excel = glue::glue("excel/neutrophil_clinical_all_tables_sva-v{ver}.xlsx")))
neut_sig_sva <- sm(extract_significant_genes(
neut_tables_sva,
excel = glue::glue("excel/neutrophil_clinical_sig_tables_sva-v{ver}.xlsx"),
according_to = "deseq"))5.2.4.3 Neutrophil DE plots
## DESeq2 MA plot of failure / cure
neut_tables[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]]$plot
## DESeq2 Volcano plot of failure / cure
neut_tables[["plots"]][["fail_vs_cure"]][["deseq_vol_plots"]]$plot
## DESeq2 MA plot of failure / cure with sva
neut_tables_sva[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]]$plot
## DESeq2 Volcano plot of failure / cure with sva
neut_tables_sva[["plots"]][["fail_vs_cure"]][["deseq_vol_plots"]]$plot
5.2.4.4 Neutrophil ontology search
ups <- neut_sig[["deseq"]][["ups"]][["fail_vs_cure"]]
downs <- neut_sig[["deseq"]][["downs"]][["fail_vs_cure"]]
neut_up_gp <- simple_gprofiler(sig_genes = ups)## Performing gProfiler GO search of 179 genes against hsapiens.## GO search found 41 hits.## Performing gProfiler KEGG search of 179 genes against hsapiens.## KEGG search found 3 hits.## Performing gProfiler REAC search of 179 genes against hsapiens.## REAC search found 5 hits.## Performing gProfiler MI search of 179 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 179 genes against hsapiens.## TF search found 35 hits.## Performing gProfiler CORUM search of 179 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 179 genes against hsapiens.## HP search found 4 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 138 genes against hsapiens.## GO search found 0 hits.## Performing gProfiler KEGG search of 138 genes against hsapiens.## KEGG search found 0 hits.## Performing gProfiler REAC search of 138 genes against hsapiens.## REAC search found 0 hits.## Performing gProfiler MI search of 138 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 138 genes against hsapiens.## TF search found 2 hits.## Performing gProfiler CORUM search of 138 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 138 genes against hsapiens.## HP search found 0 hits.neut_down_gp[["pvalue_plots"]][["bpp_plot_over"]]## NULLneut_down_gp[["pvalue_plots"]][["mfp_plot_over"]]## NULLneut_down_gp[["pvalue_plots"]][["reactome_plot_over"]]## NULL5.2.4.5 Neutrophil GSVA query
neut_up_goseq_msig <- goseq_msigdb(sig_genes = ups, signatures = broad_c7,
signature_category = "c7", length_db = hs_length)## Before conversion: 179, after conversion: 178.## Before conversion: 227921, after conversion: 35341.## Found 171 go_db genes and 178 length_db genes out of 178.neut_down_goseq_msig <- goseq_msigdb(sig_genes = downs, signatures = broad_c7,
signature_category = "c7", length_db = hs_length)## Before conversion: 138, after conversion: 133.## Before conversion: 227921, after conversion: 35341.## Found 128 go_db genes and 133 length_db genes out of 133.5.2.4.6 Plot of similar experiments
## Neutrophil genes with increased expression in the failed samples
## share genes with the following experiments
neut_up_goseq_msig[["pvalue_plots"]][["mfp_plot_over"]]
## Neutrophil genes with increased expression in the cured samples
## share genes with the following experiments
neut_down_goseq_msig[["pvalue_plots"]][["mfp_plot_over"]]
5.3 Eosinophils
5.3.1 Evaluate Eosinophil samples
eo <- subset_expt(hs_valid, subset = "typeofcells=='eosinophils'") %>%
set_expt_conditions(fact = "clinicaloutcome") %>%
set_expt_batches(fact = "donor") %>%
set_expt_colors(colors = chosen_colors)## subset_expt(): There were 152, now there are 24 samples.## Warning in set_expt_colors(., colors = chosen_colors): Colors for the following
## categories are not being used: nullnotapplicable.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 110 genes against hsapiens.## GO search found 102 hits.## Performing gProfiler KEGG search of 110 genes against hsapiens.## KEGG search found 7 hits.## Performing gProfiler REAC search of 110 genes against hsapiens.## REAC search found 9 hits.## Performing gProfiler MI search of 110 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 110 genes against hsapiens.## TF search found 50 hits.## Performing gProfiler CORUM search of 110 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 110 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 38 genes against hsapiens.## GO search found 0 hits.## Performing gProfiler KEGG search of 38 genes against hsapiens.## KEGG search found 0 hits.## Performing gProfiler REAC search of 38 genes against hsapiens.## REAC search found 0 hits.## Performing gProfiler MI search of 38 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 38 genes against hsapiens.## TF search found 0 hits.## Performing gProfiler CORUM search of 38 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 38 genes against hsapiens.## HP search found 0 hits.eo_down_gp[["pvalue_plots"]][["bpp_plot_over"]]## NULLeo_down_gp[["pvalue_plots"]][["mfp_plot_over"]]## NULLeo_down_gp[["pvalue_plots"]][["reactome_plot_over"]]## NULL5.3.2.5 Eosinophil MSigDB query
eo_up_goseq_msig <- goseq_msigdb(sig_genes = ups, signatures = broad_c7,
signature_category = "c7", length_db = hs_length)## Before conversion: 110, after conversion: 110.## Before conversion: 227921, after conversion: 35341.## Found 108 go_db genes and 110 length_db genes out of 110.eo_down_goseq_msig <- goseq_msigdb(sig_genes = downs, signatures = broad_c7,
signature_category = "c7", length_db = hs_length)## Before conversion: 38, after conversion: 37.## Before conversion: 227921, after conversion: 35341.## Found 33 go_db genes and 37 length_db genes out of 37.5.3.2.6 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 152, now there are 49 samples.## Warning in set_expt_colors(., colors = chosen_colors): Colors for the following
## categories are not being used: nullnotapplicable.save_result <- save(biop, file = "rda/biopsy_expt.rda")
biop_norm <- normalize_expt(biop, filter = TRUE, convert = "cpm",
transform = "log2", norm = "quant")## Removing 5753 low-count genes (14188 remaining).## transform_counts: Found 16 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"))
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_clinical, ids = shared_ids, method = "keep")## Before removal, there were 19941 genes, now there are 21.## There are 132 samples which kept less than 90 percent counts.## X1017n1 X1017m1 X1034n1 X1034bp1 X1034n2 X1034m2 X1034m2. X2050bp1
## 0.10770 0.10087 0.95547 0.33773 1.09654 0.51353 0.49517 0.16070
## X2052bp1 X2052e1 X2052m2 X2052n2 X2052m3 X2052n3 X2065m1 X2065n1
## 0.32817 0.03808 0.12555 0.24676 0.16692 0.23432 0.16654 0.52827
## X2065bp1 X2066m1 X2066n1 X2066bp1 X2065m2 X2065n2 X2065e2 X2066m2
## 0.34346 0.07692 0.13150 0.27139 0.07087 0.04962 0.03702 0.06761
## X2066n2 X2066e2 X2068m1 X2068n1 X2068e1 X2068bp1 X2072m1 X2072n1
## 0.06831 0.02495 0.04981 0.04888 0.01947 0.17676 0.06351 0.06770
## X2072e1 X2072bp1 X2071m1 X2071n1 X2071bp1 X2073m1 X2073n1 X2073e1
## 0.03133 0.18845 0.12420 0.39016 0.23609 0.13451 0.45494 0.04816
## X2073bp1 X2068m2 X2068n2 X2068e2 X2072m2 X2072n2 X2072e2 X2073m2
## 0.25557 0.06535 0.07704 0.01960 0.09097 0.11434 0.03699 0.27127
## X2073n2 X2073e2 X2066m3 X2066n3 X2066e3 X2065e3 X2068m3 X2068n3
## 0.94140 0.12439 0.08602 0.10998 0.02804 0.02263 0.06194 0.06636
## X2068e3 X2072m3 X2072n3 X2072e3 X2159bp1 X2073m3 X2073n3 X2073e3
## 0.02747 0.18905 0.57829 0.06830 0.21495 0.13146 0.21920 0.05208
## X2162m1 X2162n1 X2162e1 X2162bp1 X2162n2 X2162e2 X2162n3 X2162e3
## 0.06606 0.06570 0.04768 0.21141 0.08682 0.07029 0.13566 0.04089
## X2167m1 X2167n1 X2167e1 X2167bp1 X2168m1 X2168n1 X2168e1 X2168m2
## 0.13729 0.06095 0.11876 0.18832 0.32153 0.76205 0.16761 0.26397
## X2168n2 X2168e2 X2167m2 X2167n3 X2167e3 X2168m3 X2168n3 X2168e3
## 0.75628 0.13855 0.07451 0.06879 0.08244 0.35171 0.98796 0.18522
## X2172n1 X2172e1 X2172bp1 X2173bp1 X1154bp1 X2008.1 X2008.2 X2008.3
## 0.06621 0.03115 0.34453 0.23774 0.23862 0.18147 0.13122 0.19344
## X1029.1 X1029.2 X1029.3 X1036.1 X1036.2 X1036.3 X1037.1 X1037.2
## 0.18687 0.17736 0.15556 0.29831 0.23483 0.18661 0.28122 0.13634
## X1037.3 X1031.1 X1031.2 X1031.3 X2002.1 X2002.3 X2009.1 X2009.2
## 0.14125 0.30709 0.16886 0.19346 0.26122 0.12688 0.19448 0.14828
## X2010.1 X2010.2 X2010.3 X1019.1 X1019.2 X1019.3 X2004.1 X2004.2
## 0.24210 0.24601 0.15844 0.41003 0.27201 0.23768 0.17984 0.17389
## X2004.3 X2001.1 X2001.2 X2001.3 X2003.1 X2003.2 X2003.3 X1168m1
## 0.14816 0.24701 0.15282 0.10547 0.39666 0.16892 0.14402 0.24770
## X1168n1 X1168m2 X1168e2 X1168n3
## 0.39685 0.23465 0.05943 0.27880shared_written <- write_expt(shared_expt,
excel = glue::glue("excel/shared_across_celltypes-v{ver}.xlsx"))## Writing the first sheet, containing a legend and some summary data.## Warning: ggrepel: 126 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 110 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## `geom_smooth()` using formula 'y ~ x'## `geom_smooth()` using formula 'y ~ x'## plot labels was not set and there are more than 100 samples, disabling it.
## plot labels was not set and there are more than 100 samples, disabling it.##
## Total:0 s## Error in quantile.default(prop_median, p = c(1, 3)/4) :
## missing values and NaN's not allowed if 'na.rm' is FALSE## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 2 rows containing missing values (geom_bar).## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 2 rows containing missing values (geom_bar).## Warning: ggrepel: 130 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 118 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 4 rows containing missing values (geom_dl).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 4 rows containing missing values (geom_dl).## 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##
## Total:0 s7 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 152, now there are 28 samples.mono_visit_norm <- normalize_expt(mono_visit, filter = TRUE, norm = "quant", convert = "cpm",
transform = "log2")## Removing 8906 low-count genes (11035 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$plot)
mono_visit_nb <- normalize_expt(mono_visit, filter = TRUE, convert = "cpm",
batch = "svaseq", transform = "log2")## Removing 8906 low-count genes (11035 remaining).## batch_counts: Before batch/surrogate estimation, 1433 entries are x==0: 0%.## batch_counts: Before batch/surrogate estimation, 18423 entries are 0<x<1: 6%.## Setting 401 low elements to zero.## transform_counts: Found 401 values equal to 0, adding 1 to the matrix.mono_visit_nb_pca <- plot_pca(mono_visit_nb)
pp(file = "images/monocyte_by_visit_nb.png", image = mono_visit_nb_pca$plot)
table(pData(mono_visit_norm)$batch)##
## cure failure lost
## 9 13 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, 1433 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 (11035 remaining).## batch_counts: Before batch/surrogate estimation, 1433 entries are x==0: 0%.## batch_counts: Before batch/surrogate estimation, 18423 entries are 0<x<1: 6%.## Setting 401 low elements to zero.## transform_counts: Found 401 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-v202106.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, 1433 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 (11035 remaining).## batch_counts: Before batch/surrogate estimation, 1433 entries are x==0: 0%.## batch_counts: Before batch/surrogate estimation, 18423 entries are 0<x<1: 6%.## Setting 384 low elements to zero.## transform_counts: Found 384 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-v202106.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 72947fcc6afe09da22d71967059edd84e3063341## This is hpgltools commit: Tue Jun 1 15:57:56 2021 -0400: 72947fcc6afe09da22d71967059edd84e3063341## Saving to tmrc3_02sample_estimation_v202106.rda.xztmp <- loadme(filename = savefile)---
title: "TMRC3 Comprehensive Data Analysis: 202106"
author: "atb abelew@gmail.com"
date: "`r Sys.Date()`"
output:
 html_document:
  code_download: true
  code_folding: show
  fig_caption: true
  fig_height: 7
  fig_width: 7
  highlight: default
  keep_md: false
  mode: selfcontained
  number_sections: true
  self_contained: true
  theme: readable
  toc: true
  toc_float:
   collapsed: false
   smooth_scroll: false
---

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

```{r options, include = FALSE}
library(hpgltools)
tt <- sm(devtools::load_all("~/hpgltools"))
knitr::opts_knit$set(progress = TRUE,
                     verbose = TRUE,
                     width = 120,
                     echo = TRUE)
knitr::opts_chunk$set(error = TRUE,
                      fig.width = 12,
                      fig.height = 12,
                      dpi = 96)
old_options <- options(digits = 4,
                       stringsAsFactors = FALSE,
                       knitr.duplicate.label = "allow")
ggplot2::theme_set(ggplot2::theme_bw(base_size = 12))
ver <- "202106"
rundate <- format(Sys.Date(), format = "%Y%m%d")

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

```{r samplesheet}
samplesheet <- "sample_sheets/tmrc3_samples_20210610.xlsx"
```

# 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.


### Hisat2 expressionsets

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

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

```{r all_new_hisat2}
## Create the expressionset and immediately pass it to a filter
## removing the non protein coding genes.
sanitize_columns <- c("visitnumber", "clinicaloutcome", "donor",
                      "typeofcells", "clinicalpresentation",
                      "condition", "batch")
hs_expt <- create_expt(samplesheet,
                       file_column = "hg38100hisatfile",
                       savefile = glue::glue("rda/hs_expt_all-v{ver}.rda"),
                       gene_info = hs_annot) %>%
  exclude_genes_expt(column = "gene_biotype", method = "keep",
                     pattern = "protein_coding", meta_column = "ncrna_lost") %>%
  sanitize_expt_metadata(columns = sanitize_columns) %>%
  set_expt_factors(columns = sanitize_columns, class = "factor")

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

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

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

#### Initial metrics

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

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

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

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

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

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

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

## Minimum coverage sample filtering

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

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

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

# Project Aims

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

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

## Preparation

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

## Global view

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

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

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

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

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

## Examine samples relevant to clinical outcome

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

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

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

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

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

### Repeat without the biopsy samples

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

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

### Attempt to correct for the surrogate variables

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

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

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

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

#### Look at remaining variance with variancePartition

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

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

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

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

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

hs_clinic_sig[["summary_df"]]
```

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

### Perform LRT with the clinical samples

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

#### With biopsy samples

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

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

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

### Look at only the differential genes

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

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

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

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

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

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

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

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

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

##hs_try2 <- simple_gprofiler2(ups)

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

## Perform GSVA on the clinical samples

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

broad_c7 <- GSEABase::getGmt("reference/msigdb/c7.all.v7.2.entrez.gmt",
                             collectionType = GSEABase::BroadCollection(category = "c7"),
                             geneIdType = GSEABase::EntrezIdentifier())
hs_celltype_gsva_c7 <- sm(simple_gsva(individual_celltypes, signatures = broad_c7,
                                      msig_xml = "reference/msigdb_v7.2.xml", cores = 10))
hs_celltype_gsva_c7_sig <- sm(get_sig_gsva_categories(
    hs_celltype_gsva_c7,
    excel = "excel/individual_celltypes_gsva_c7.xlsx"))
```

### Print some plots of the GSVA outputs

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

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

# Individual Cell types

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

## Implementation details

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

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

### Shared contrasts

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

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

## Monocytes

### Evaluate Monocyte samples

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

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

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

### DE of Monocyte samples

#### Without sva

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

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

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

#### With sva

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

#### Monocyte DE plots

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

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

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

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

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

#### Monocyte ontology search

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

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

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

#### Monocyte MSigDB query

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

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

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

#### Plot of similar experiments

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

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

### Evaluate Neutrophil samples

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

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

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

### DE of Netrophil samples

#### Without sva

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

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

#### With sva

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

#### Neutrophil DE plots

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

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

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

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

#### Neutrophil ontology search

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

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

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

#### Neutrophil GSVA query

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

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

#### Plot of similar experiments

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

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

## Eosinophils

### Evaluate Eosinophil samples

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

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

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

### DE of Eosinophil samples

#### Withouth sva

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

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

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

#### With sva

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

#### Eosinophil DE plots

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

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

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

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

#### Eosinophil ontology search

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

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

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

#### Eosinophil MSigDB query

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

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

#### Plot of similar experiments

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

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

## Biopsies

### Evaluate Biopsy samples

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

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

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

### DE of Biopsy samples

#### Without sva

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

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

#### with sva

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

#### Biopsy DE plots

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

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

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

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

# Look for shared genes among Monocytes/Neutrophils/Eosinophils

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

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

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

shared_ids <- shared_vector@IntersectionSets[["111"]]
shared_expt <- exclude_genes_expt(hs_clinical, ids = shared_ids, method = "keep")
shared_written <- write_expt(shared_expt,
                             excel = glue::glue("excel/shared_across_celltypes-v{ver}.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)
```
