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05f-agirre_gcb_healthy_pca_deseq.R
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05f-agirre_gcb_healthy_pca_deseq.R
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################################################################################
################################################################################
################################################################################
################################################################################
######################## BULK AGIRRE GCB PCA & DESEQ2 ##########################
## Plan:
## 1. PCA of bulk GCB data
## 2. Identift TEs upregulated in all portions of the GCB
## 3. Identify TEs specific to LZ, DZ, PB, MB
#################################### SETUP #####################################
library(knitr)
library(tidyverse)
library(matrixStats)
library(data.table)
library(PCAtools)
library(DESeq2)
library(ggplot2)
library(ggsci)
library(ggpubr)
library(edgeR)
library(ashr)
library(cowplot)
library(wesanderson)
library(UpSetR)
library(EnhancedVolcano)
################################### LOAD DATA ##################################
load("r_outputs/02-GCB_Agirre_filt_counts.Rdata")
load("r_outputs/01-metadata.Rdata")
load("r_outputs/01-refs.Rdata")
retro.annot.v2 <- read.csv("/efs/projects/hematological_malignancies_te_analysis/refs/TE_annotation.v2.0.tsv",
sep = "\t")
rownames(retro.annot.v2) <- retro.annot.v2$Locus
remove(BL_metadata, DLBCL_metadata, FL_metadata, all_metadata, bulk_metadata)
################################# METADATA SETUP ###############################
agirre_metadata$Cell <- agirre_metadata$source_name
agirre_metadata$Cell[agirre_metadata$Cell == "Naive"] <- "Naive B"
agirre_metadata$Cell[agirre_metadata$Cell == "Centroblast"] <- "Dark Zone Germinal Center B"
agirre_metadata$Cell[agirre_metadata$Cell == "Centrocyte"] <- "Light Zone Germinal Center B"
agirre_metadata$Cell[agirre_metadata$Cell == "Memory"] <- "Memory B"
agirre_metadata$Cell[agirre_metadata$Cell == "Tonsilar plasma cell"] <- "Plasmablasts"
agirre_metadata$Cell[agirre_metadata$Cell == "Bone Marrow plasma cell"] <- "Bone Marrow plasma cell"
############################# FUNCTION SCREE PLOT ##############################
pca_standard <- function(tform, metadata, var) {
removeVar <- var
pca.obj <- PCAtools::pca(assay(tform),
metadata=metadata,
removeVar=removeVar)
cat(sprintf('Removed %d pct low variance variables, %d retained\n',
removeVar*100, length(pca.obj$xvars)))
varline <- 50
varline.x <- min(which(cumsum(pca.obj$variance) >= varline))
horn <- PCAtools::parallelPCA(assay(tform), removeVar = removeVar)
elbow <- PCAtools::findElbowPoint(pca.obj$variance)
screeplot <-PCAtools::screeplot(pca.obj,
axisLabSize = 6,
components = getComponents(pca.obj, 1:30),
title=paste("Retrotranscriptome SCREE",
metadata$cancer_type[1],
sep=" "),
hline=varline, vline=c(varline.x, horn$n, elbow)
) +
geom_label(aes(x=varline.x+1, y=50,
label = paste0(varline, '% var'), vjust = -1)) +
geom_label(aes(x = horn$n + 1, y = 50,
label = 'Horn\'s', vjust = -1)) +
geom_label(aes(x = elbow + 1, y = 50,
label = 'Elbow method', vjust = -1))
cat(sprintf('%d PCs for Elbow method\n', elbow))
cat(sprintf('%d PCs for Horn method\n', horn$n))
cat(sprintf('%d PCs needed to explain %d percent of variation\n',
varline.x, varline))
return(pca.obj)
}
################################################################################
################################################################################
################################################################################
############################### GCB AGIRRE DESEQ ###############################
### DESeq2 (HERVs Only)
bulk.countdat <- GCB_Agirre.filt.herv
cat(sprintf('%d variables\n', nrow(bulk.countdat)))
stopifnot(all(colnames(bulk.countdat) == rownames(agirre_metadata)))
Agirre.dds <- DESeq2::DESeqDataSetFromMatrix(countData = bulk.countdat,
colData = agirre_metadata,
design = ~ Cell + 0)
Agirre.dds <- DESeq2::DESeq(Agirre.dds, parallel=T)
Agirre.tform <- DESeq2::varianceStabilizingTransformation(Agirre.dds, blind=FALSE)
## PCA
Agirre.herv.pca.obj <-
pca_standard(tform = Agirre.tform,
metadata = agirre_metadata,
var = 0.1)
# 4 PCs for Elbow method
# 6 PCs for Horn method
# 3 PCs needed to explain 50 percent of variation
######################## GCB BULK DESEQ (HERVS + GENES) ########################
### DESeq2 (HERVs + Genes)
bulk.g.countdat <- GCB_Agirre.filt.comb
cat(sprintf('%d variables\n', nrow(bulk.g.countdat)))
stopifnot(all(colnames(bulk.g.countdat) == rownames(agirre_metadata)))
Agirre.g.dds <- DESeq2::DESeqDataSetFromMatrix(countData = bulk.g.countdat,
colData = agirre_metadata,
design = ~ Cell + 0)
Agirre.g.dds <- DESeq2::DESeq(Agirre.g.dds, parallel=T)
Agirre.g.tform <- DESeq2::varianceStabilizingTransformation(Agirre.g.dds, blind=FALSE)
## PCA
Agirre.g.pca.obj <-
pca_standard(tform = Agirre.g.tform,
metadata = agirre_metadata,
var = 0.1)
# 4 PCs for Elbow method
# 6 PCs for Horn method
# 2 PCs needed to explain 50 percent of variation
########################## GCB BULK DESEQ (GENES ONLY) #########################
### DESeq2 (HERVs + Genes)
bulk.gonly.countdat <- GCB_Agirre.filt.tx
cat(sprintf('%d variables\n', nrow(bulk.gonly.countdat)))
stopifnot(all(colnames(bulk.gonly.countdat) == rownames(agirre_metadata)))
Agirre.gonly.dds <- DESeq2::DESeqDataSetFromMatrix(countData = bulk.gonly.countdat,
colData = agirre_metadata,
design = ~ Cell + 0)
Agirre.gonly.dds <- DESeq2::DESeq(Agirre.gonly.dds, parallel=T)
Agirre.gonly.tform <- DESeq2::varianceStabilizingTransformation(Agirre.gonly.dds, blind=FALSE)
## PCA
Agirre.gonly.pca.obj <-
pca_standard(tform = Agirre.gonly.tform,
metadata = agirre_metadata,
var = 0.1)
# 4 PCs for Elbow method
# 5 PCs for Horn method
# 2 PCs needed to explain 50 percent of variation
##################### AGIRRE GCB BIPLOTS HERVs & GENES #########################
## Biplot with projects (HERVs and genes)
pdf("plots/05f-gcb_agirre_pca_genes_hervs.pdf", height=5, width=6)
biplot(Agirre.g.pca.obj,
lab = NULL,
showLoadings = FALSE,
boxedLoadingsNames = TRUE,
fillBoxedLoadings = alpha("white", 3/4),
pointSize = 2,
encircle = FALSE,
sizeLoadingsNames = 4,
lengthLoadingsArrowsFactor = 1.5,
drawConnectors = TRUE,
colby = "Cell",
colkey = c("Naive B" = pal_jco("default", alpha = 0.8)(7)[1],
"Memory B" = pal_jco("default", alpha = 0.8)(7)[3],
"Dark Zone Germinal Center B" = pal_jco("default", alpha = 0.8)(57)[4],
"Light Zone Germinal Center B" = pal_jco("default", alpha = 0.8)(7)[5],
"Plasmablasts" = pal_jco("default", alpha = 0.8)(7)[6],
"Bone Marrow plasma cell" = pal_jco("default", alpha = 0.8)(7)[7]),
legendPosition = "right") +
theme_cowplot() +
theme(aspect.ratio = 1)
dev.off()
pdf("plots/05f-gcb_agirre_pca_hervs.pdf", height=5, width=6)
## Biplot with projects (HERVs only)
biplot(Agirre.herv.pca.obj,
lab = NULL,
showLoadings = FALSE,
boxedLoadingsNames = TRUE,
fillBoxedLoadings = alpha("white", 3/4),
pointSize = 2,
encircle = FALSE,
sizeLoadingsNames = 4,
lengthLoadingsArrowsFactor = 1.5,
drawConnectors = TRUE,
colby = "Cell",
colkey = c("Naive B" = pal_jco("default", alpha = 0.8)(7)[1],
"Memory B" = pal_jco("default", alpha = 0.8)(7)[3],
"Dark Zone Germinal Center B" = pal_jco("default", alpha = 0.8)(57)[4],
"Light Zone Germinal Center B" = pal_jco("default", alpha = 0.8)(7)[5],
"Plasmablasts" = pal_jco("default", alpha = 0.8)(7)[6],
"Bone Marrow plasma cell" = pal_jco("default", alpha = 0.8)(7)[7]),
legendPosition = "right") +
theme_cowplot() +
theme(aspect.ratio = 1)
dev.off()
pdf("plots/05f-gcb_agirre_pca_genes.pdf", height=5, width=6)
## Biplot with projects (HERVs only)
biplot(Agirre.gonly.pca.obj,
lab = NULL,
showLoadings = FALSE,
boxedLoadingsNames = TRUE,
fillBoxedLoadings = alpha("white", 3/4),
pointSize = 2,
encircle = FALSE,
sizeLoadingsNames = 4,
lengthLoadingsArrowsFactor = 1.5,
drawConnectors = TRUE,
colby = "Cell",
colkey = c("Naive B" = pal_jco("default", alpha = 0.8)(7)[1],
"Memory B" = pal_jco("default", alpha = 0.8)(7)[3],
"Dark Zone Germinal Center B" = pal_jco("default", alpha = 0.8)(7)[4],
"Light Zone Germinal Center B" = pal_jco("default", alpha = 0.8)(7)[5],
"Plasmablasts" = pal_jco("default", alpha = 0.8)(7)[6],
"Bone Marrow plasma cell" = pal_jco("default", alpha = 0.8)(7)[7]),
legendPosition = "right") +
theme_cowplot() +
theme(aspect.ratio = 1)
dev.off()
################################ SET THRESHOLDS ################################
fc=2 # fold change
l2fc=log2(fc) # log 2 fold change
lfc.cutoff <- 1.5
pval=0.001 # p value threshold
################################################################################
################################################################################
################################################################################
####################### ANALYSIS 1: EACH CELL TYPE VS ALL ######################
################################################################################
################################################################################
################################################################################
############################## TOP GENES & HERVS ###############################
# resultsNames(Agirre.g.dds)
# [1] "CellBone.Marrow.plasma.cell" "CellDark.Zone.Germinal.Center.B" "CellLight.Zone.Germinal.Center.B"
# [4] "CellMemory.B""CellNaive.B" "CellPlasmablasts"
agirre_res <- list(
"BMPC" = DESeq2::results(Agirre.g.dds, contrast=c(+1, -1/5, -1/5, -1/5, -1/5, -1/5), alpha=pval),
"DZ" = DESeq2::results(Agirre.g.dds, contrast=c(-1/5, +1, -1/5, -1/5, -1/5, -1/5), alpha=pval),
"LZ" = DESeq2::results(Agirre.g.dds, contrast=c(-1/5, -1/5, +1, -1/5, -1/5, -1/5), alpha=pval),
"MB" = DESeq2::results(Agirre.g.dds, contrast=c(-1/5, -1/5, -1/5, +1, -1/5, -1/5), alpha=pval),
"NB" = DESeq2::results(Agirre.g.dds, contrast=c(-1/5, -1/5, -1/5, -1/5, +1, -1/5), alpha=pval),
"PB" = DESeq2::results(Agirre.g.dds, contrast=c(-1/5, -1/5, -1/5, -1/5, -1/5, +1), alpha=pval),
"DZvLZ" = DESeq2::results(Agirre.g.dds, contrast=c("Cell", "Dark Zone Germinal Center B",
"Light Zone Germinal Center B"), alpha=pval),
"MBvsNB" = DESeq2::results(Agirre.g.dds, contrast=c("Cell", "Memory B", "Naive B"), alpha=pval)
)
agirre_res <- lapply(agirre_res, function(r) {
r$display <- gene_table[rownames(r),]$display
r$class <- gene_table[rownames(r),]$gene_type
r
})
sig <- lapply(agirre_res, function(r) {
s <- subset(r, padj < pval & abs(log2FoldChange) > lfc.cutoff)
s[order(s$padj),]
})
for (n in names(sig)) {
cat("\n#--- Contrast", n, "---#\n")
summary(sig[[n]])
}
########################### TOP GENES & HERVS NO PB ############################
# resultsNames(Agirre.g.dds)
# [1] "CellBone.Marrow.plasma.cell" "CellDark.Zone.Germinal.Center.B" "CellLight.Zone.Germinal.Center.B"
# [4] "CellMemory.B""CellNaive.B" "CellPlasmablasts"
agirre_res_no_pb <- list(
"DZ" = DESeq2::results(Agirre.g.dds, contrast=c(0, +1, -1/3, -1/3, -1/3, 0), alpha=pval),
"LZ" = DESeq2::results(Agirre.g.dds, contrast=c(0, -1/3, +1, -1/3, -1/3, 0), alpha=pval),
"MB" = DESeq2::results(Agirre.g.dds, contrast=c(0, -1/3, -1/3, +1, -1/3, 0), alpha=pval),
"NB" = DESeq2::results(Agirre.g.dds, contrast=c(0, -1/3, -1/3, -1/3, +1, 0), alpha=pval),
"DZvLZ" = DESeq2::results(Agirre.g.dds, contrast=c("Cell", "Dark Zone Germinal Center B",
"Light Zone Germinal Center B"), alpha=pval),
"MBvsNB" = DESeq2::results(Agirre.g.dds, contrast=c("Cell", "Memory B", "Naive B"), alpha=pval)
)
agirre_res_no_pb <- lapply(agirre_res_no_pb, function(r) {
r$display <- gene_table[rownames(r),]$display
r$class <- gene_table[rownames(r),]$gene_type
r
})
sig_no_pb <- lapply(agirre_res_no_pb, function(r) {
s <- subset(r, padj < pval & abs(log2FoldChange) > lfc.cutoff)
s[order(s$padj),]
})
for (n in names(sig_no_pb)) {
cat("\n#--- Contrast", n, "---#\n")
summary(sig_no_pb[[n]])
}
############################### TOP HERVs ONLY #################################
# resultsNames(Agirre.dds)
# [1] "CellBone.Marrow.plasma.cell" "CellDark.Zone.Germinal.Center.B" "CellLight.Zone.Germinal.Center.B"
# [4] "CellMemory.B""CellNaive.B" "CellPlasmablasts"
agirre_res_herv <- list(
"BMPC" = DESeq2::results(Agirre.dds, contrast=c(+1, -1/5, -1/5, -1/5, -1/5, -1/5), alpha=pval),
"DZ" = DESeq2::results(Agirre.dds, contrast=c(-1/5, +1, -1/5, -1/5, -1/5, -1/5), alpha=pval),
"LZ" = DESeq2::results(Agirre.dds, contrast=c(-1/5, -1/5, +1, -1/5, -1/5, -1/5), alpha=pval),
"MB" = DESeq2::results(Agirre.dds, contrast=c(-1/5, -1/5, -1/5, +1, -1/5, -1/5), alpha=pval),
"NB" = DESeq2::results(Agirre.dds, contrast=c(-1/5, -1/5, -1/5, -1/5, +1, -1/5), alpha=pval),
"PB" = DESeq2::results(Agirre.dds, contrast=c(-1/5, -1/5, -1/5, -1/5, -1/5, +1), alpha=pval),
"DZvLZ" = DESeq2::results(Agirre.dds, contrast=c("Cell", "Dark Zone Germinal Center B",
"Light Zone Germinal Center B"), alpha=pval),
"MBvsNB" = DESeq2::results(Agirre.dds, contrast=c("Cell", "Memory B", "Naive B"), alpha=pval)
)
agirre_res_herv <- lapply(agirre_res_herv, function(r) {
r$display <- gene_table[rownames(r),]$display
r$class <- gene_table[rownames(r),]$gene_type
r
})
sig_herv <- lapply(agirre_res_herv, function(r) {
s <- subset(r, padj < pval & abs(log2FoldChange) > lfc.cutoff)
s[order(s$padj),]
})
sink(file = "r_outputs/05f-agirre_herv_deseq.txt")
for (n in names(sig_herv)) {
cat("\n#--- Contrast", n, "---#\n")
summary(sig_herv[[n]])
}
sink(file = NULL)
sink(file = "r_outputs/05f-agirre_herv_deseq_top5.txt")
for (n in names(sig_herv)) {
cat("\n#--- Contrast", n, "---#\n")
print(head(sig_herv[[n]]))
}
sink(file = NULL)
############################ TOP HERVs ONLY NO PB ##############################
agirre_res_herv_no_pb <- list(
"DZ" = DESeq2::results(Agirre.dds, contrast=c(0, +1, -1/3, -1/3, -1/3, 0), alpha=pval),
"LZ" = DESeq2::results(Agirre.dds, contrast=c(0, -1/3, +1, -1/3, -1/3, 0), alpha=pval),
"MB" = DESeq2::results(Agirre.dds, contrast=c(0, -1/3, -1/3, +1, -1/3, 0), alpha=pval),
"NB" = DESeq2::results(Agirre.dds, contrast=c(0, -1/3, -1/3, -1/3, +1, 0), alpha=pval),
"DZvLZ" = DESeq2::results(Agirre.dds, contrast=c("Cell", "Dark Zone Germinal Center B",
"Light Zone Germinal Center B"), alpha=pval),
"MBvsNB" = DESeq2::results(Agirre.dds, contrast=c("Cell", "Memory B", "Naive B"), alpha=pval)
)
agirre_res_herv_no_pb <- lapply(agirre_res_herv_no_pb, function(r) {
r$display <- gene_table[rownames(r),]$display
r$class <- gene_table[rownames(r),]$gene_type
r
})
sig_herv_no_pb <- lapply(agirre_res_herv_no_pb, function(r) {
s <- subset(r, padj < pval & abs(log2FoldChange) > lfc.cutoff)
s[order(s$padj),]
})
sink(file = "r_outputs/05f-agirre_herv_no_pb_deseq.txt")
for (n in names(sig_herv_no_pb)) {
cat("\n#--- Contrast", n, "---#\n")
summary(sig_herv_no_pb[[n]])
}
sink(file = NULL)
sink(file = "r_outputs/05f-agirre_herv_no_pb_deseq_top5.txt")
for (n in names(sig_herv_no_pb)) {
cat("\n#--- Contrast", n, "---#\n")
print(head(sig_herv_no_pb[[n]]))
}
sink(file = NULL)
############################ UPSET GENES & HERVS ###############################
upvars_agirre <- lapply(sig[1:6], function(r) rownames(subset(r, log2FoldChange>0)))
downvars_agirre <- lapply(sig[1:6], function(r) rownames(subset(r, log2FoldChange<0)))
pdf("plots/05f-gcb_agirre_upvars.pdf", height=5, width=7)
upset(fromList(upvars_agirre), sets=c("BMPC", "DZ", "LZ", "MB", "NB", "PB"),
keep.order = T, order.by='degree', decreasing=F,
text.scale = c(1.5, 1.5, 1, 1, 1.7, 1))
dev.off()
pdf("plots/05f-gcb_agirre_upset_dnwars.pdf", height=5, width=7)
upset(fromList(downvars_agirre), sets=c("BMPC", "DZ", "LZ", "MB", "NB", "PB"),
keep.order = T, order.by='degree', decreasing=F,
text.scale = c(1.5, 1.5, 1, 1, 1.7, 1))
dev.off()
up.binmat.agirre <- fromList(upvars_agirre)
rn <- do.call(c, upvars_agirre)
rn <- rn[!duplicated(rn)]
rownames(up.binmat.agirre) <- rn
rm(rn)
dn.binmat.agirre <- fromList(downvars_agirre)
rn <- do.call(c, downvars_agirre)
rn <- rn[!duplicated(rn)]
rownames(dn.binmat.agirre) <- rn
rm(rn)
######################### UPSET GENES & HERVS NO PB ############################
upvars_agirre_no_pb <- lapply(sig_no_pb[1:4], function(r) rownames(subset(r, log2FoldChange>0)))
downvars_agirre_no_pb <- lapply(sig_no_pb[1:4], function(r) rownames(subset(r, log2FoldChange<0)))
pdf("plots/05f-gcb_agirre_no_pb_upvars.pdf", height=5, width=7)
upset(fromList(upvars_agirre_no_pb), sets=c("DZ", "LZ", "MB", "NB"),
keep.order = T, order.by='degree', decreasing=F,
text.scale = c(1.5, 1.5, 1, 1, 1.7, 1))
dev.off()
pdf("plots/05f-gcb_agirre_no_pb_upset_dnwars.pdf", height=5, width=7)
upset(fromList(downvars_agirre_no_pb), sets=c("DZ", "LZ", "MB", "NB"),
keep.order = T, order.by='degree', decreasing=F,
text.scale = c(1.5, 1.5, 1, 1, 1.7, 1))
dev.off()
up.binmat.agirre.no.pb <- fromList(upvars_agirre_no_pb)
rn <- do.call(c, upvars_agirre_no_pb)
rn <- rn[!duplicated(rn)]
rownames(up.binmat.agirre.no.pb) <- rn
rm(rn)
dn.binmat.agirre.no.pb <- fromList(downvars_agirre_no_pb)
rn <- do.call(c, downvars_agirre_no_pb)
rn <- rn[!duplicated(rn)]
rownames(dn.binmat.agirre.no.pb) <- rn
rm(rn)
############################# UPSET HERVs ONLY #################################
upvars_agirre_hervs <- lapply(sig_herv[1:6], function(r) rownames(subset(r, log2FoldChange>0)))
downvars_agirre_hervs <- lapply(sig_herv[1:6], function(r) rownames(subset(r, log2FoldChange<0)))
pdf("plots/05f-gcb_agirre_upset_upvars_hervs.pdf", height=5, width=7)
upset(fromList(upvars_agirre_hervs), sets=c("BMPC", "DZ", "LZ", "MB", "NB", "PB"),
keep.order = T, order.by='degree', decreasing=F)
dev.off()
pdf("plots/05f-gcb_agirre_upset_dnvars_hervs.pdf", height=5, width=7)
upset(fromList(downvars_agirre_hervs), sets=c("BMPC", "DZ", "LZ", "MB", "NB", "PB"),
keep.order = T, order.by='degree', decreasing=F)
dev.off()
up.binmat.hervs.agirre <- fromList(upvars_agirre_hervs)
rn <- do.call(c, upvars_agirre_hervs)
rn <- rn[!duplicated(rn)]
rownames(up.binmat.hervs.agirre) <- rn
rm(rn)
dn.binmat.hervs.agirre <- fromList(downvars_agirre_hervs)
rn <- do.call(c, downvars_agirre_hervs)
rn <- rn[!duplicated(rn)]
rownames(dn.binmat.hervs.agirre) <- rn
rm(rn)
########################## UPSET HERVs ONLY NO PB ##############################
upvars_agirre_hervs_no_pb <- lapply(sig_herv_no_pb[1:4], function(r) rownames(subset(r, log2FoldChange>0)))
downvars_agirre_hervs_no_pb <- lapply(sig_herv_no_pb[1:4], function(r) rownames(subset(r, log2FoldChange<0)))
pdf("plots/05f-gcb_agirre_upset_upvars_hervs_no_pb.pdf", height=5, width=7)
upset(fromList(upvars_agirre_hervs_no_pb), sets=c("DZ", "LZ", "MB", "NB"),
keep.order = T, order.by='degree', decreasing=F)
dev.off()
pdf("plots/05f-gcb_agirre_upset_dnvars_hervs_no_pb.pdf", height=5, width=7)
upset(fromList(downvars_agirre_hervs_no_pb), sets=c("DZ", "LZ", "MB", "NB"),
keep.order = T, order.by='degree', decreasing=F)
dev.off()
up.binmat.hervs.agirre.no.pb <- fromList(upvars_agirre_hervs_no_pb)
rn <- do.call(c, upvars_agirre_hervs_no_pb)
rn <- rn[!duplicated(rn)]
rownames(up.binmat.hervs.agirre.no.pb) <- rn
rm(rn)
dn.binmat.hervs.agirre.no.pb <- fromList(downvars_agirre_hervs_no_pb)
rn <- do.call(c, downvars_agirre_hervs_no_pb)
rn <- rn[!duplicated(rn)]
rownames(dn.binmat.hervs.agirre.no.pb) <- rn
rm(rn)
################################# HEATMAPS #####################################
################################### SETUP ######################################
# Colors for cells
cols <- rgb_gsea(palette = c("default"), n = 14, alpha = 0.7, reverse = FALSE)
# Annotation column
df <- as.data.frame(colData(Agirre.dds)[,c("BioSample","Cell")])
df <- subset(df, select = -c(1))
# Create colors for each group
annoCol <- c("#0073C2CC", "#868686CC", "#CD534CCC", "#7AA6DCCC",
"#003C67CC", "#8F7700CC")
names(annoCol) <- c("Naive B", "Memory B", "Dark Zone Germinal Center B",
"Light Zone Germinal Center B", "Plasmablasts",
"Bone Marrow plasma cell")
annoCol2 <- pal_aaas("default", alpha=0.7)(3)
names(annoCol2) <- unique(retro.annot.v2$TE_type)
annoCol <- list(Cell = annoCol, TE_type = annoCol2)
######################### UPREGULATED IN ALL GROUPS ############################
top.genes.hervs <- rownames(up.binmat.agirre)
top.hervs <- rownames(up.binmat.hervs.agirre)
annoRow <- as.data.frame(retro.annot.v2[,c("TE_type", "Locus")])
annoRow <- annoRow[top.hervs,]
annoRow <- subset(annoRow, select = -c(2))
pdf("plots/05f-gcb_agirre_top_hervs_upregulated_all.pdf", height=10, width=10)
pheatmap(assay(Agirre.tform)[top.hervs,],
main="Upregulated HERVs, all clusters",
cluster_rows=TRUE,
show_rownames=FALSE,
show_colnames = FALSE,
color = cols,
scale="row",
breaks=seq(-3,3,length.out=14),
labels_row = gene_table[top.hervs,]$display,
cluster_cols=TRUE,
treeheight_row=0,
annotation_col=df,
annotation_row=annoRow,
annotation_colors = annoCol)
dev.off()
pdf("plots/05f-gcb_agirre_top_hervs_genes_upregulated_all.pdf", height=10, width=10)
pheatmap(assay(Agirre.g.tform)[top.genes.hervs,],
main="Upregulated Genes and HERVs, all clusters",
cluster_rows=TRUE,
show_rownames=FALSE,
show_colnames = FALSE,
color = cols,
scale="row",
breaks=seq(-3,3,length.out=14),
labels_row = gene_table[top.genes.hervs,]$display,
cluster_cols=TRUE,
treeheight_row=0,
annotation_col=df,
annotation_colors = annoCol)
dev.off()
########################### DE HERVs PER GC SITE ###############################
makeheatmap <- function(topgenes, ...) {
args <- list(...)
mat <- assay(Agirre.tform)[unique(topgenes), ]
rowdist <- as.dist((1 - cor(t(mat), method='spearman'))/2)
annotation_col <- df
cols <- rgb_gsea(palette = c("default"), n = 14, alpha = 0.7, reverse = FALSE)
annoRow <- as.data.frame(retro.annot.v2[,c("TE_type", "Locus")])
annoRow <- annoRow[topgenes,]
annoRow <- subset(annoRow, select = -c(2))
pheatmap(mat,
color=cols,
scale="row", breaks=seq(-3,3,length.out=14),
clustering_distance_rows = rowdist,
clustering_method="average",
annotation_col = df,
annotation_colors = annoCol,
annotation_row = annoRow,
show_colnames=F,
show_rownames = T,
fontsize = 8, fontsize_row = 6, border_color = NA,
legend=T,
treeheight_col=10,
treeheight_row=10,
cutree_cols=4,
...
)
}
for(clust in c("BMPC", "DZ", "LZ", "NB", "PB")) {
tg <- rownames(sig_herv[[clust]][1:75,])
p <- makeheatmap(tg, main=paste0('DE in cluster ', clust))
pdf(paste0("plots/05f-gcb_agirre_top_de_hervs_", clust, ".pdf"), height=7, width=7)
print(p)
dev.off()
}
for(clust in c("MB")) {
tg <- rownames(sig_herv[[clust]][1:20,])
p <- makeheatmap(tg, main=paste0('DE in cluster ', clust))
pdf(paste0("plots/05f-gcb_agirre_top_de_hervs_", clust, ".pdf"), height=5, width=7)
print(p)
dev.off()
}
################################ FAMILY LEVEL UP ###############################
upreg_agirre_hervs_df <- do.call(rbind, lapply(upvars_agirre_hervs, data.frame))
colnames(upreg_agirre_hervs_df) <- c("herv")
upreg_agirre_hervs_df$cell_type <- rownames(upreg_agirre_hervs_df)
upreg_agirre_hervs_df$cell_type <- gsub("\\..*","",upreg_agirre_hervs_df$cell_type)
upreg_agirre_hervs_df$family <- retro.annot$family[match(upreg_agirre_hervs_df$herv,
retro.annot$locus)]
upreg_agirre_families <-
upreg_agirre_hervs_df %>% dplyr::count(family, cell_type, sort = TRUE)
upreg_agirre_families<-upreg_agirre_families[!(upreg_agirre_families$cell_type=="DZvLZ"),]
pdf("plots/05f-gcb_agirre_upreg_families_all.pdf", height=8, width=6)
ggplot(upreg_agirre_families, aes(fill=reorder(family, -n), y=cell_type, x=n)) +
geom_bar(position="fill", stat="identity", colour="black", size=0.3) +
scale_fill_manual(values = c(pal_futurama("planetexpress")(12),
pal_npg("nrc", alpha = 0.7)(10),
pal_jco("default", alpha=0.7)(10),
pal_nejm("default", alpha=0.7)(8),
pal_tron("legacy", alpha=0.7)(7),
pal_lancet("lanonc", alpha=0.7)(9),
pal_startrek("uniform", alpha=0.7)(7)),
breaks = unique(retro.annot$family),
labels = unique(retro.annot$family)) +
coord_flip() +
theme_pubclean() +
guides(fill=guide_legend(title="TE Family")) +
ylab("Upregulated HERVs by Family") +
xlab("Proportion of HERVs") +
theme(legend.position = c("right")) +
guides(fill = guide_legend(title = "HERV family", ncol = 2))
dev.off()
############################### FAMILY LEVEL DOWN ##############################
down_agirre_hervs_df <- do.call(rbind, lapply(downvars_agirre_hervs, data.frame))
colnames(down_agirre_hervs_df) <- c("herv")
down_agirre_hervs_df$cell_type <- rownames(down_agirre_hervs_df)
down_agirre_hervs_df$cell_type <- gsub("\\..*","",down_agirre_hervs_df$cell_type)
down_agirre_hervs_df$family <- retro.annot$family[match(down_agirre_hervs_df$herv,
retro.annot$locus)]
downreg_agirre_families <-
down_agirre_hervs_df %>% dplyr::count(family, cell_type, sort = TRUE)
downreg_agirre_families<-downreg_agirre_families[!(downreg_agirre_families$cell_type=="DZvLZ"),]
pdf("plots/05f-gcb_agirre_downreg_families_all.pdf", height=8, width=6)
ggplot(downreg_agirre_families, aes(fill=reorder(family, -n), y=cell_type, x=n)) +
geom_bar(position="fill", stat="identity", colour="black", size=0.3) +
scale_fill_manual(values = c(pal_futurama("planetexpress")(12),
pal_npg("nrc", alpha = 0.7)(10),
pal_jco("default", alpha=0.7)(10),
pal_nejm("default", alpha=0.7)(8),
pal_tron("legacy", alpha=0.7)(7),
pal_lancet("lanonc", alpha=0.7)(9),
pal_startrek("uniform", alpha=0.7)(7)),
breaks = unique(retro.annot$family),
labels = unique(retro.annot$family)) +
coord_flip() +
theme_pubclean() +
guides(fill=guide_legend(title="TE Family")) +
ylab("Downregulated HERVs by Family") +
xlab("Proportion of HERVs") +
theme(legend.position = c("right")) +
guides(fill = guide_legend(title = "HERV family", ncol = 2))
dev.off()
############################# MERGED FAMILY UP/DOWN ############################
updown_family <- do.call(rbind, (list(Upregulated = upreg_agirre_families,
Dowregulated = downreg_agirre_families)))
updown_family$expression <- rownames(updown_family)
updown_family$expression <- gsub("\\..*","",updown_family$expression)
rownames(updown_family)<-NULL
pdf("plots/05f-gcb_agirre_updown_families_all.pdf", height=8, width=10)
ggplot(updown_family, aes(fill=reorder(family, -n), y=cell_type, x=n)) +
geom_bar(position="fill", stat="identity", colour="black", size=0.3) +
scale_fill_manual(values = c(pal_futurama("planetexpress")(12),
pal_npg("nrc", alpha = 0.7)(10),
pal_jco("default", alpha=0.7)(10),
pal_nejm("default", alpha=0.7)(8),
pal_tron("legacy", alpha=0.7)(7),
pal_lancet("lanonc", alpha=0.7)(9),
pal_startrek("uniform", alpha=0.7)(7)),
breaks = unique(retro.annot$family),
labels = unique(retro.annot$family)) +
coord_flip() +
theme_pubclean() +
guides(fill=guide_legend(title="TE Family")) +
ylab("Downregulated HERVs by Family") +
xlab("Proportion of HERVs") +
theme(legend.position = c("right")) +
guides(fill = guide_legend(title = "HERV family", ncol = 2)) +
facet_wrap(~ expression, ncol = 2)
dev.off()
#################################### TE TYPE ###################################
rownames(upreg_agirre_hervs_df) <- NULL
upreg_agirre_hervs_df$type <- retro.annot.v2$TE_type[
match(upreg_agirre_hervs_df$herv, retro.annot.v2$Locus)]
upreg_agirre_te_type <-
upreg_agirre_hervs_df %>% dplyr::count(type, cell_type, sort = TRUE)
pdf("plots/05f-gcb_agirre_hervs_type.pdf", height=4, width=4)
ggplot(upreg_agirre_te_type, aes(fill=reorder(type, -n), y=cell_type, x=n)) +
geom_bar(position="stack", stat="identity", colour="black", size=0.3) +
scale_fill_manual(values = c(pal_aaas("default", alpha=0.7)(3))) +
coord_flip() +
theme_pubclean() +
guides(fill=guide_legend(title="HERV Type")) +
ylab("Upregulated HERVs by Type") +
xlab("Number of HERVs") +
theme(legend.position = c("right")) +
theme(aspect.ratio = 1)
dev.off()
rownames(down_agirre_hervs_df) <- NULL
down_agirre_hervs_df$type <- retro.annot.v2$TE_type[
match(down_agirre_hervs_df$herv, retro.annot.v2$Locus)]
downreg_agirre_te_type <-
down_agirre_hervs_df %>% dplyr::count(type, cell_type, sort = TRUE)
pdf("plots/05f-gcb_agirre_downreg_hervs_type.pdf", height=4, width=4)
ggplot(downreg_agirre_te_type, aes(fill=reorder(type, -n), y=cell_type, x=n)) +
geom_bar(position="stack", stat="identity", colour="black", size=0.3) +
scale_fill_manual(values = c(pal_aaas("default", alpha=0.7)(3))) +
coord_flip() +
theme_pubclean() +
guides(fill=guide_legend(title="HERV Type")) +
ylab("Downregulated HERVs by Type") +
xlab("Number of HERVs") +
theme(legend.position = c("right")) +
theme(aspect.ratio = 1)
dev.off()
########################### FAMILY LEVEL UP NO PB ##############################
upreg_hervs_no_pb_df <- do.call(rbind, lapply(upvars_agirre_hervs_no_pb, data.frame))
colnames(upreg_hervs_no_pb_df) <- c("herv")
upreg_hervs_no_pb_df$cell_type <- rownames(upreg_hervs_no_pb_df)
upreg_hervs_no_pb_df$cell_type <- gsub("\\..*","",upreg_hervs_no_pb_df$cell_type)
upreg_hervs_no_pb_df$family <- retro.annot$family[match(upreg_hervs_no_pb_df$herv,
retro.annot$locus)]
upreg_families_no_pb <-
upreg_hervs_no_pb_df %>% dplyr::count(family, cell_type, sort = TRUE)
upreg_families_no_pb<-upreg_families_no_pb[!(upreg_families_no_pb$cell_type=="DZvLZ"),]
pdf("plots/05f-gcb_agirre_upreg_families.pdf", height=8, width=6)
ggplot(upreg_families_no_pb, aes(fill=reorder(family, -n), y=cell_type, x=n)) +
geom_bar(position="fill", stat="identity", colour="black", size=0.3) +
scale_fill_manual(values = c(pal_futurama("planetexpress")(12),
pal_npg("nrc", alpha = 0.7)(10),
pal_jco("default", alpha=0.7)(10),
pal_nejm("default", alpha=0.7)(8),
pal_tron("legacy", alpha=0.7)(7),
pal_lancet("lanonc", alpha=0.7)(9),
pal_startrek("uniform", alpha=0.7)(7)),
breaks = unique(retro.annot$family),
labels = unique(retro.annot$family)) +
coord_flip() +
theme_pubclean() +
guides(fill=guide_legend(title="TE Family")) +
ylab("Upregulated HERVs by Family") +
xlab("Proportion of HERVs") +
theme(legend.position = c("right")) +
guides(fill = guide_legend(title = "HERV family", ncol = 2))
dev.off()
############################ FAMILY LEVEL DOWN NO PB ###########################
down_hervs_no_pb_df <- do.call(rbind, lapply(downvars_agirre_hervs_no_pb, data.frame))
colnames(down_hervs_no_pb_df) <- c("herv")
down_hervs_no_pb_df$cell_type <- rownames(down_hervs_no_pb_df)
down_hervs_no_pb_df$cell_type <- gsub("\\..*","",down_hervs_no_pb_df$cell_type)
down_hervs_no_pb_df$family <- retro.annot$family[match(down_hervs_no_pb_df$herv,
retro.annot$locus)]
downreg_families_no_pb <-
down_hervs_no_pb_df %>% dplyr::count(family, cell_type, sort = TRUE)
downreg_families_no_pb<-downreg_families_no_pb[!(downreg_families_no_pb$cell_type=="DZvLZ"),]
pdf("plots/05f-gcb_agirre_families.pdf", height=8, width=6)
ggplot(downreg_families_no_pb, aes(fill=reorder(family, -n), y=cell_type, x=n)) +
geom_bar(position="fill", stat="identity", colour="black", size=0.3) +
scale_fill_manual(values = c(pal_futurama("planetexpress")(12),
pal_npg("nrc", alpha = 0.7)(10),
pal_jco("default", alpha=0.7)(10),
pal_nejm("default", alpha=0.7)(8),
pal_tron("legacy", alpha=0.7)(7),
pal_lancet("lanonc", alpha=0.7)(9),
pal_startrek("uniform", alpha=0.7)(7)),
breaks = unique(retro.annot$family),
labels = unique(retro.annot$family)) +
coord_flip() +
theme_pubclean() +
guides(fill=guide_legend(title="TE Family")) +
ylab("Downregulated HERVs by Family") +
xlab("Proportion of HERVs") +
theme(legend.position = c("right")) +
guides(fill = guide_legend(title = "HERV family", ncol = 2))
dev.off()
########################## MERGED FAMILY UP/DOWN NO PB #########################
updown_family <- do.call(rbind, (list(Upregulated = upreg_families_no_pb,
Dowregulated = downreg_families_no_pb)))
updown_family$expression <- rownames(updown_family)
updown_family$expression <- gsub("\\..*","",updown_family$expression)
rownames(updown_family)<-NULL
pdf("plots/05f-gcb_agirre_updown_families.pdf", height=8, width=10)
ggplot(updown_family, aes(fill=reorder(family, -n), y=cell_type, x=n)) +
geom_bar(position="fill", stat="identity", colour="black", size=0.3) +
scale_fill_manual(values = c(pal_futurama("planetexpress")(12),
pal_npg("nrc", alpha = 0.7)(10),
pal_jco("default", alpha=0.7)(10),
pal_nejm("default", alpha=0.7)(8),
pal_tron("legacy", alpha=0.7)(7),
pal_lancet("lanonc", alpha=0.7)(9),
pal_startrek("uniform", alpha=0.7)(7)),
breaks = unique(retro.annot$family),
labels = unique(retro.annot$family)) +
coord_flip() +
theme_pubclean() +
guides(fill=guide_legend(title="TE Family")) +
ylab("Downregulated HERVs by Family") +
xlab("Proportion of HERVs") +
theme(legend.position = c("right")) +
guides(fill = guide_legend(title = "HERV family", ncol = 2)) +
facet_wrap(~ expression, ncol = 2)
dev.off()
############################ FAMILY LEVEL WITH PB ##############################
upreg_hervs_pb <- do.call(rbind, lapply(upvars_agirre_hervs, data.frame))
colnames(upreg_hervs_pb) <- c("herv")
upreg_hervs_pb$cell_type <- rownames(upreg_hervs_pb)
upreg_hervs_pb$cell_type <- gsub("\\..*","",upreg_hervs_pb$cell_type)
upreg_hervs_pb$family <- retro.annot$family[match(upreg_hervs_no_pb_df$herv,
retro.annot$locus)]
upreg_families_pb <-
upreg_hervs_pb %>% dplyr::count(family, cell_type, sort = TRUE)
upreg_families_pb<-upreg_families_pb[!(upreg_families_pb$cell_type=="DZvLZ"),]
down_hervs_pb <- do.call(rbind, lapply(downvars_agirre_hervs, data.frame))
colnames(down_hervs_pb) <- c("herv")
down_hervs_pb$cell_type <- rownames(down_hervs_pb)
down_hervs_pb$cell_type <- gsub("\\..*","",down_hervs_pb$cell_type)
down_hervs_pb$family <- retro.annot$family[match(down_hervs_pb$herv,
retro.annot$locus)]
downreg_families_pb <-
down_hervs_pb %>% dplyr::count(family, cell_type, sort = TRUE)
downreg_families_pb<-downreg_families_pb[!(downreg_families_pb$cell_type=="DZvLZ"),]
updown_family <- do.call(rbind, (list(Upregulated = upreg_families_pb,
Dowregulated = downreg_families_pb)))
updown_family$expression <- rownames(updown_family)
updown_family$expression <- gsub("\\..*","",updown_family$expression)
rownames(updown_family)<-NULL
pdf("plots/05f-gcb_agirre_updown_families_with_pb.pdf", height=8, width=10)
ggplot(updown_family, aes(fill=reorder(family, -n), y=cell_type, x=n)) +
geom_bar(position="fill", stat="identity", colour="black", size=0.3) +
scale_fill_manual(values = c(pal_futurama("planetexpress")(12),
pal_npg("nrc", alpha = 0.7)(10),
pal_jco("default", alpha=0.7)(10),
pal_nejm("default", alpha=0.7)(8),
pal_tron("legacy", alpha=0.7)(7),
pal_lancet("lanonc", alpha=0.7)(9),
pal_startrek("uniform", alpha=0.7)(7)),
breaks = unique(retro.annot$family),
labels = unique(retro.annot$family)) +
coord_flip() +
theme_pubclean() +
guides(fill=guide_legend(title="TE Family")) +
ylab("Downregulated HERVs by Family") +
xlab("Proportion of HERVs") +
theme(legend.position = c("right")) +
guides(fill = guide_legend(title = "HERV family", ncol = 2)) +
facet_wrap(~ expression, ncol = 2)
dev.off()
############################# VOLCANO DZ VS LZ #################################
pdf("plots/05f-gcb_agirre_volcano_DZ_v_all.pdf", height=8, width=8)
EnhancedVolcano(sig_herv$DZ,
lab = rownames(sig_herv$DZ),
x = 'log2FoldChange',
y = 'pvalue',
title = 'All vs DZ')
dev.off()
pdf("plots/05f-gcb_agirre_volcano_LZ_v_all.pdf", height=8, width=8)
EnhancedVolcano(sig_herv$LZ,
lab = rownames(sig_herv$LZ),
x = 'log2FoldChange',
y = 'pvalue',
title = 'All vs LZ')
dev.off()
pdf("plots/05f-gcb_agirre_volcano_NB_v_all.pdf", height=8, width=8)
EnhancedVolcano(sig_herv$NB,
lab = rownames(sig_herv$NB),
x = 'log2FoldChange',
y = 'pvalue',
title = 'All vs NB')
dev.off()
pdf("plots/05f-gcb_agirre_volcano_MB_v_all.pdf", height=8, width=8)
EnhancedVolcano(sig_herv$MB,
lab = rownames(sig_herv$MB),
x = 'log2FoldChange',
y = 'pvalue',
title = 'All vs MB')
dev.off()
pdf("plots/05f-gcb_agirre_volcano_PB_v_all.pdf", height=8, width=8)
EnhancedVolcano(sig_herv$PB,
lab = rownames(sig_herv$PB),
x = 'log2FoldChange',
y = 'pvalue',
title = 'All vs PB')
dev.off()
pdf("plots/05f-gcb_agirre_volcano_BMPC_v_all.pdf", height=8, width=8)
EnhancedVolcano(sig_herv$BMPC,
lab = rownames(sig_herv$BMPC),
x = 'log2FoldChange',
y = 'pvalue',
title = 'All vs BMPC')
dev.off()
pdf("plots/05f-gcb_agirre_volcano_DZ_v_LZ.pdf", height=8, width=8)
EnhancedVolcano(sig_herv$DZvLZ,
lab = rownames(sig_herv$DZvLZ),
x = 'log2FoldChange',
y = 'pvalue',