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05b-lymphoma_pca.R
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05b-lymphoma_pca.R
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################################################################################
################################################################################
################################################################################
################################################################################
################################ LYMPHOMA PCAs #################################
## Plan:
## DLBCL individual DESEQ2 and PCA
## FL individual DESEQ2 and PCA
## BL individual DESEQ2 and PCA
## All together DESEQ2 and PCA (Maybe not?)
#################################### SETUP #####################################
library(knitr)
library(tidyverse)
library(matrixStats)
library(data.table)
library(PCAtools)
library(DESeq2)
library(ggplot2)
library(ggsci)
library(edgeR)
library(ashr)
library(cowplot)
library(wesanderson)
library(sva)
################################### LOAD DATA ##################################
load("r_outputs/02-DLBCL_filt_counts.Rdata")
load("r_outputs/02-BL_filt_counts.Rdata")
load("r_outputs/02-FL_filt_counts.Rdata")
load("r_outputs/01-metadata.Rdata")
load("r_outputs/01-refs.Rdata")
load("r_outputs/02-all_lymphoma_filt_counts.Rdata")
################################# 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$n))
cat(sprintf('%d PCs needed to explain %d percent of variation\n',
varline.x, varline))
print(screeplot)
return(pca.obj)
}
############################## DLBCL DESEQ HERVs ###############################
### DESeq2 (HERVs Only)
DLBCL_metadata$COO_class <- DLBCL_metadata$COO_class %>%
replace(is.na(.), "Unclass")
DLBCL.countdat <- DLBCL.filt.herv
cat(sprintf('%d variables\n', nrow(DLBCL.countdat)))
stopifnot(all(colnames(DLBCL.countdat) == rownames(DLBCL_metadata)))
DLBCL.dds <- DESeq2::DESeqDataSetFromMatrix(countData = DLBCL.countdat,
colData = DLBCL_metadata,
design = ~ project + COO_class)
DLBCL.dds <- DESeq2::DESeq(DLBCL.dds, parallel=T)
DLBCL.tform <- DESeq2::varianceStabilizingTransformation(DLBCL.dds, blind=FALSE)
## PCA
DLBCL.herv.pca.obj <-
pca_standard(tform = DLBCL.tform,
metadata = DLBCL_metadata,
var = 0.9)
# 4 PCs for Elbow method
# 22 PCs for Horn method
# 22 PCs needed to explain 50 percent of variation
############################# DLBCL BIPLOTS HERVs ###############################
## Biplot with projects (only HERVs)
biplot(DLBCL.herv.pca.obj,
lab = NULL,
showLoadings = TRUE,
boxedLoadingsNames = TRUE,
fillBoxedLoadings = alpha("white", 3/4),
pointSize = 2,
encircle = FALSE,
sizeLoadingsNames = 4,
lengthLoadingsArrowsFactor = 1.5,
drawConnectors = TRUE,
colby = "project",
colkey = c("NCICCR-DLBCL" = "orange",
"TCGA-DLBC" = "lightblue"),
legendPosition = "right") +
theme_cowplot()
ggsave("plots/05b-dlbcl_hervs_biplot_pc1_pc2_project.pdf", height = 6, width = 8)
# Biplot with COO call (only HERVs)
biplot(DLBCL.herv.pca.obj,
lab = NULL,
showLoadings = TRUE,
boxedLoadingsNames = TRUE,
fillBoxedLoadings = alpha("white", 3/4),
pointSize = 2,
encircle = FALSE,
sizeLoadingsNames = 4,
lengthLoadingsArrowsFactor = 1.5,
drawConnectors = TRUE,
ellipse = TRUE,
colby = "COO_class",
shape = "project", shapekey = c("NCICCR-DLBCL" = 15, "TCGA-DLBC" = 8),
colkey = c("GCB" = "royalblue",
"ABC" = "red3",
"Unclass" = "lightblue",
"Missing" = "grey"),
legendPosition = "right") +
theme_cowplot()
ggsave("plots/05b-dlbcl_hervs_biplot_pc1_pc2_coo.pdf", height = 6, width = 8)
############################ DLBCL LOADINGS HERVs ##############################
rangeRetain <- 0.01
PCAtools::plotloadings(DLBCL.herv.pca.obj,
title=paste0("DLBCL HERV loadings"),
rangeRetain = rangeRetain,
caption = paste0('Top ', rangeRetain * 100, '% variables'),
subtitle = 'PC1-PC5',
shapeSizeRange = c(3,3),
labSize = 3.0,
shape = 24,
col = c('white', 'pink'),
drawConnectors = TRUE
)
ggsave("plots/05b-dlbcl_hervs_loadings_plot.pdf", height = 10, width = 10)
########################## DLBCL DESEQ HERVS & GENES ###########################
DLBCL.g.countDat <- DLBCL.filt.comb
cat(sprintf('%d variables\n', nrow(DLBCL.g.countDat)))
stopifnot(all(colnames(DLBCL.g.countDat) == rownames(DLBCL_metadata)))
DLBCL.g.dds <- DESeq2::DESeqDataSetFromMatrix(countData = DLBCL.g.countDat,
colData = DLBCL_metadata,
design = ~ project + COO_class)
DLBCL.g.dds <- DESeq2::DESeq(DLBCL.g.dds, parallel=T)
DLBCL.g.tform <- DESeq2::varianceStabilizingTransformation(DLBCL.g.dds,
blind=FALSE)
## scree plot
DLBCL.g.pca.obj <-
pca_standard(tform = DLBCL.g.tform,
metadata = DLBCL_metadata,
var = 0.9)
# 3 PCs for Elbow method
# 45 PCs for Horn method
# 21 PCs needed to explain 50 percent of variation
all(rownames(DLBCL.g.pca.obj$loadings) %in% rownames(gene_table))
rownames(DLBCL.g.pca.obj$loadings) <-
gene_table[rownames(DLBCL.g.pca.obj$loadings), 'display']
######################## DLBCL BIPLOTS HERVs & GENES ###########################
## Biplot with projects (HERVs and genes)
biplot(DLBCL.g.pca.obj,
lab = NULL,
showLoadings = TRUE,
boxedLoadingsNames = TRUE,
fillBoxedLoadings = alpha("white", 3/4),
pointSize = 2,
encircle = FALSE,
sizeLoadingsNames = 4,
lengthLoadingsArrowsFactor = 1.5,
drawConnectors = TRUE,
colby = "project",
colkey = c("NCICCR-DLBCL" = "orange",
"TCGA-DLBC" = "lightblue"),
legendPosition = "right") +
theme_cowplot()
ggsave("plots/05b-dlbcl_hervsgenes_biplot_pc1_pc2_project.pdf", height = 6, width = 8)
# Biplot with COO call (only HERVs)
biplot(DLBCL.g.pca.obj,
lab = NULL,
showLoadings = TRUE,
boxedLoadingsNames = TRUE,
fillBoxedLoadings = alpha("white", 3/4),
pointSize = 2,
encircle = FALSE,
sizeLoadingsNames = 4,
lengthLoadingsArrowsFactor = 1.5,
ellipse = TRUE,
drawConnectors = TRUE,
colby = "COO_class",
shape = "project", shapekey = c("NCICCR-DLBCL" = 15, "TCGA-DLBC" = 8),
colkey = c("GCB" = "royalblue",
"ABC" = "red3",
"Unclass" = "lightblue",
"Missing" = "grey"),
legendPosition = "right") +
theme_cowplot()
ggsave("plots/05b-dlbcl_hervsgenes_biplot_pc1_pc2_coo.pdf", height = 6, width = 8)
######################## DLBCL LOADINGS HERVs & GENES ##########################
rangeRetain <- 0.01
PCAtools::plotloadings(DLBCL.g.pca.obj,
title=paste0("DLBCL HERV & Gene loadings"),
rangeRetain = rangeRetain,
caption = paste0('Top ', rangeRetain * 100, '% variables'),
subtitle = 'PC1-PC5',
shapeSizeRange = c(3,3),
labSize = 3.0,
shape = 24,
col = c('white', 'pink'),
drawConnectors = TRUE
)
ggsave("plots/05b-dlbcl_hervsgenes_loadings_plot.pdf", height = 10, width = 10)
################################ BL DESEQ HERVs ################################
### DESeq2 (HERVs Only)
BL.countdat <- BL.filt.herv
cat(sprintf('%d variables\n', nrow(BL.countdat)))
stopifnot(all(colnames(BL.countdat) == rownames(BL_metadata)))
BL.dds <- DESeq2::DESeqDataSetFromMatrix(countData = BL.countdat,
colData = BL_metadata,
design = ~ clinical_variant +
ebv_status)
BL.dds <- DESeq2::DESeq(BL.dds, parallel=T)
BL.tform <- DESeq2::varianceStabilizingTransformation(BL.dds, blind=FALSE)
## PCA
BL.herv.pca.obj <-
pca_standard(tform = BL.tform,
metadata = BL_metadata,
var = 0.1)
# var = 0.1, 4, 19, 20
# var = 0.3, 4, 18, 19
# var = 0.5, 4, 18, 18
# var = 0.7, 4, 15, 16
# var = 0.8, 5, 15, 15
# Final var 0.7:
# 4 PCs for Elbow method
# 15 PCs for Horn method
# 16 PCs needed to explain 50 percent of variation
############################## BL BIPLOTS HERVs ################################
## Biplot with projects (only HERVs)
biplot(BL.herv.pca.obj,
lab = NULL,
showLoadings = FALSE,
boxedLoadingsNames = TRUE,
fillBoxedLoadings = alpha("white", 3/4),
pointSize = 3,
encircle = FALSE,
sizeLoadingsNames = 4,
lengthLoadingsArrowsFactor = 1.5,
drawConnectors = TRUE,
ellipse = FALSE,
colby = "clinical_variant",
colkey = c("Endemic BL" = wes_palette("Zissou1")[2],
"Sporadic BL" = wes_palette("Zissou1")[4]),
legendPosition = "right") +
theme_cowplot() +
theme(aspect.ratio = 1)
ggsave("plots/05b-BL_hervs_biplot_pc1_pc2_clinvariant.pdf", height = 6, width = 6)
# Biplot with COO call (only HERVs)
biplot(BL.herv.pca.obj,
lab = NULL,
showLoadings = FALSE,
boxedLoadingsNames = TRUE,
fillBoxedLoadings = alpha("white", 3/4),
pointSize = 3,
encircle = FALSE,
sizeLoadingsNames = 4,
lengthLoadingsArrowsFactor = 1.5,
drawConnectors = TRUE,
colby = "ebv_status",
ellipse = FALSE,
shape = "clinical_variant",
shapekey = c("Endemic BL" = 15, "Sporadic BL" = 8),
colkey = c("EBV-positive" = wes_palette("Darjeeling1")[2],
"EBV-negative" = wes_palette("Darjeeling1")[4]),
legendPosition = "right") +
theme_cowplot() +
theme(aspect.ratio = 1)
ggsave("plots/05b-BL_hervs_biplot_pc1_pc2_ebvclinvar.pdf", height = 6, width = 6)
biplot(BL.herv.pca.obj,
lab = NULL,
showLoadings = FALSE,
boxedLoadingsNames = TRUE,
fillBoxedLoadings = alpha("white", 3/4),
pointSize = 3,
encircle = FALSE,
sizeLoadingsNames = 4,
lengthLoadingsArrowsFactor = 1.5,
drawConnectors = TRUE,
colby = "gender",
ellipse = FALSE,
shape = "ebv_status",
legendPosition = "right") +
theme_cowplot() +
theme(aspect.ratio = 1)
ggsave("plots/05b-BL_hervs_biplot_pc1_pc2_gender.pdf", height = 6, width = 6)
eigencorplot(BL.herv.pca.obj,
metavars = c('ebv_status','clinical_variant','subgroup','gender',
'tissue_source_site','tumor_biopsy','MYC_SV_partner',
'subtype', 'Total_N_SSM', 'anatomic_site_classification'))
pairsplot(BL.herv.pca.obj)
############################## BL LOADINGS HERVs ###############################
rangeRetain <- 0.01
PCAtools::plotloadings(BL.herv.pca.obj,
title=paste0("BL HERV loadings"),
rangeRetain = rangeRetain,
caption = paste0('Top ', rangeRetain * 100, '% variables'),
subtitle = 'PC1-PC5',
shapeSizeRange = c(3,3),
labSize = 3.0,
shape = 24,
col = c('white', 'pink'),
drawConnectors = TRUE
)
ggsave("plots/05b-BL_hervs_loadings_plot.pdf", height = 10, width = 10)
############################# BL DESEQ GENES ONLY ##############################
BL.gonly.countdat <- BL.filt.tx
cat(sprintf('%d variables\n', nrow(BL.gonly.countdat)))
stopifnot(all(colnames(BL.gonly.countdat) == rownames(BL_metadata)))
BL.gonly.dds <- DESeq2::DESeqDataSetFromMatrix(countData = BL.gonly.countdat,
colData = BL_metadata,
design = ~ clinical_variant +
ebv_status)
BL.gonly.dds <- DESeq2::DESeq(BL.gonly.dds, parallel=T)
BL.gonly.tform <- DESeq2::varianceStabilizingTransformation(BL.gonly.dds, blind=FALSE)
## PCA
BL.gonly.pca.obj <-
pca_standard(tform = BL.gonly.tform,
metadata = BL_metadata,
var = 0.1)
# var 0.1, 6, 18, 12
# var 0.3, 6, 18, 12
# var 0.5 7, 18, 12
# var 0.7 8, 12, 12
all(rownames(BL.gonly.pca.obj$loadings) %in% rownames(gene_table))
rownames(BL.gonly.pca.obj$loadings) <-
gene_table[rownames(BL.gonly.pca.obj$loadings), 'display']
############################## BL BIPLOTS GENES ################################
## Biplot with projects (only genes)
biplot(BL.gonly.pca.obj,
lab = NULL,
showLoadings = FALSE,
boxedLoadingsNames = TRUE,
fillBoxedLoadings = alpha("white", 3/4),
pointSize = 3,
encircle = FALSE,
sizeLoadingsNames = 4,
lengthLoadingsArrowsFactor = 1.5,
drawConnectors = TRUE,
colby = "clinical_variant",
colkey = c("Endemic BL" = wes_palette("Zissou1")[2],
"Sporadic BL" = wes_palette("Zissou1")[4]),
legendPosition = "right") +
theme_cowplot() +
theme(aspect.ratio = 1)
ggsave("plots/05b-BL_genes_biplot_pc1_pc2_clinvariant.pdf", height = 6, width = 6)
# Biplot with EBV status + clinical variant (only genes)
biplot(BL.gonly.pca.obj,
lab = NULL,
showLoadings = FALSE,
boxedLoadingsNames = TRUE,
fillBoxedLoadings = alpha("white", 3/4),
pointSize = 3,
encircle = FALSE,
sizeLoadingsNames = 4,
lengthLoadingsArrowsFactor = 1.5,
drawConnectors = TRUE,
colby = "ebv_status",
shape = "clinical_variant",
shapekey = c("Endemic BL" = 15, "Sporadic BL" = 8),
colkey = c("EBV-positive" = wes_palette("Darjeeling1")[2],
"EBV-negative" = wes_palette("Darjeeling1")[4]),
legendPosition = "right") +
theme_cowplot() +
theme(aspect.ratio = 1)
ggsave("plots/05b-BL_genes_pc1_pc2_ebvclinvar.pdf", height = 6, width = 6)
# Gender
biplot(BL.gonly.pca.obj,
lab = NULL,
showLoadings = FALSE,
boxedLoadingsNames = TRUE,
fillBoxedLoadings = alpha("white", 3/4),
pointSize = 3,
encircle = FALSE,
sizeLoadingsNames = 4,
lengthLoadingsArrowsFactor = 1.5,
drawConnectors = TRUE,
colby = "gender",
ellipse = FALSE,
shape = "ebv_status",
legendPosition = "right") +
theme_cowplot() +
theme(aspect.ratio = 1)
ggsave("plots/05b-BL_genes_biplot_pc1_pc2_gender.pdf", height = 6, width = 6)
############################## BL LOADINGS GENES ###############################
rangeRetain <- 0.01
PCAtools::plotloadings(BL.gonly.pca.obj,
title=paste0("BL Genes loadings"),
rangeRetain = rangeRetain,
caption = paste0('Top ', rangeRetain * 100, '% variables'),
subtitle = 'PC1-PC5',
shapeSizeRange = c(3,3),
labSize = 3.0,
shape = 24,
col = c('white', 'pink'),
drawConnectors = TRUE
)
eigencorplot(BL.gonly.pca.obj,
metavars = c('ebv_status','clinical_variant','subgroup','gender',
'tissue_source_site','tumor_biopsy','MYC_SV_partner',
'subtype', 'Total_N_SSM', 'anatomic_site_classification'))
############################ BL DESEQ HERVs & GENES ############################
### DESeq2 (HERVs and Genes)
BL.g.countdat <- BL.filt.comb
cat(sprintf('%d variables\n', nrow(BL.g.countdat)))
stopifnot(all(colnames(BL.g.countdat) == rownames(BL_metadata)))
BL.g.dds <- DESeq2::DESeqDataSetFromMatrix(countData = BL.g.countdat,
colData = BL_metadata,
design = ~ clinical_variant +
ebv_status)
BL.g.dds <- DESeq2::DESeq(BL.g.dds, parallel=T)
BL.g.tform <- DESeq2::varianceStabilizingTransformation(BL.g.dds, blind=FALSE)
## PCA
BL.g.pca.obj <-
pca_standard(tform = BL.g.tform,
metadata = BL_metadata,
var = 0.3)
# var = 0.1, 5, 8, 14
# var = 0.3, 4, 19, 15
# var = 0.5, 5, 19, 14
# var - 0.7, 6, 18, 13
# 5 PCs for Elbow method
# 18 PCs for Horn method
# 4 PCs needed to explain 50 percent of variation
all(rownames(BL.g.pca.obj$loadings) %in% rownames(gene_table))
rownames(BL.g.pca.obj$loadings) <-
gene_table[rownames(BL.g.pca.obj$loadings), 'display']
########################## BL BIPLOTS HERVs + GENES ############################
## Biplot with projects (HERVS + GENES)
biplot(BL.g.pca.obj,
lab = NULL,
showLoadings = FALSE,
boxedLoadingsNames = TRUE,
fillBoxedLoadings = alpha("white", 3/4),
pointSize = 3,
encircle = FALSE,
sizeLoadingsNames = 4,
lengthLoadingsArrowsFactor = 1.5,
drawConnectors = TRUE,
colby = "clinical_variant",
colkey = c("Endemic BL" = wes_palette("Zissou1")[2],
"Sporadic BL" = wes_palette("Zissou1")[4]),
legendPosition = "right") +
theme_cowplot() +
theme(aspect.ratio = 1)
ggsave("plots/05b-BL_hervsgenes_biplot_pc1_pc2_clinvariant.pdf", height = 6, width = 8)
# Biplot with COO call (only HERVs)
biplot(BL.g.pca.obj,
lab = NULL,
showLoadings = FALSE,
boxedLoadingsNames = TRUE,
fillBoxedLoadings = alpha("white", 3/4),
pointSize = 3,
encircle = FALSE,
sizeLoadingsNames = 4,
lengthLoadingsArrowsFactor = 1.5,
drawConnectors = TRUE,
colby = "ebv_status",
shape = "clinical_variant",
shapekey = c("Endemic BL" = 15, "Sporadic BL" = 8),
colkey = c("EBV-positive" = wes_palette("Darjeeling1")[2],
"EBV-negative" = wes_palette("Darjeeling1")[4]),
legendPosition = "right") +
theme_cowplot() +
theme(aspect.ratio = 1)
ggsave("plots/05b-BL_hervsgenes_pc1_pc2_ebvclinvar.pdf", height = 6, width = 8)
biplot(BL.g.pca.obj,
lab = NULL,
showLoadings = FALSE,
boxedLoadingsNames = TRUE,
fillBoxedLoadings = alpha("white", 3/4),
pointSize = 3,
encircle = FALSE,
sizeLoadingsNames = 4,
lengthLoadingsArrowsFactor = 1.5,
drawConnectors = TRUE,
colby = "gender",
ellipse = FALSE,
shape = "ebv_status",
legendPosition = "right") +
theme_cowplot() +
theme(aspect.ratio = 1)
ggsave("plots/05b-BL_genes_biplot_pc1_pc2_gender.pdf", height = 6, width = 6)
########################## BL LOADINGS HERVs & GENES ###########################
rangeRetain <- 0.01
PCAtools::plotloadings(BL.g.pca.obj,
title=paste0("BL HERV & Gene loadings"),
rangeRetain = rangeRetain,
caption = paste0('Top ', rangeRetain * 100, '% variables'),
subtitle = 'PC1-PC5',
shapeSizeRange = c(3,3),
labSize = 3.0,
shape = 24,
col = c('white', 'pink'),
drawConnectors = TRUE
)
ggsave("plots/05b-BL_hervsgenes_loadings_plot.pdf", height = 10, width = 10)
######################## BL HERVS ONLY WITHOUT Y CHROM #########################
### DESeq2 (HERVs Only)
hervs.to.keep <- intersect(rownames(BL.filt.herv),
retro.annot$locus[retro.annot$chrom != "chrY"])
BL.filt.herv.no.y <- BL.filt.herv[hervs.to.keep,]
BL.countdat <- BL.filt.herv.no.y
cat(sprintf('%d variables\n', nrow(BL.countdat)))
stopifnot(all(colnames(BL.countdat) == rownames(BL_metadata)))
BL.dds.no.y <- DESeq2::DESeqDataSetFromMatrix(countData = BL.countdat,
colData = BL_metadata,
design = ~ clinical_variant +
ebv_status)
BL.dds.no.y <- DESeq2::DESeq(BL.dds.no.y, parallel=T)
BL.tform.no.y <- DESeq2::varianceStabilizingTransformation(BL.dds.no.y, blind=FALSE)
## PCA
BL.herv.no.y.pca.obj <-
pca_standard(tform = BL.tform.no.y,
metadata = BL_metadata,
var = 0.5)
# 0.1, 4, 20, 21
# 0.2, 4, 20, 21
# 0.3, 4, 20, 20
# 0.5, 4, 19, 19
# 0.7, 5, 17, 18
######################### BL HERVS ONLY NO Y PCA PLOTS #########################
biplot(BL.herv.no.y.pca.obj,
lab = NULL,
showLoadings = FALSE,
boxedLoadingsNames = TRUE,
fillBoxedLoadings = alpha("white", 3/4),
pointSize = 3,
encircle = FALSE,
sizeLoadingsNames = 4,
lengthLoadingsArrowsFactor = 1.5,
drawConnectors = TRUE,
ellipse = FALSE,
colby = "clinical_variant",
colkey = c("Endemic BL" = wes_palette("Zissou1")[2],
"Sporadic BL" = wes_palette("Zissou1")[4]),
legendPosition = "right") +
theme_cowplot() +
theme(aspect.ratio = 1)
ggsave("plots/05b-BL_hervs_no_y_biplot_pc1_pc2_clinvariant.pdf", height = 6, width = 6)
biplot(BL.herv.no.y.pca.obj,
lab = NULL,
showLoadings = FALSE,
boxedLoadingsNames = TRUE,
fillBoxedLoadings = alpha("white", 3/4),
pointSize = 3,
encircle = FALSE,
sizeLoadingsNames = 4,
lengthLoadingsArrowsFactor = 1.5,
drawConnectors = TRUE,
colby = "ebv_status",
ellipse = FALSE,
shape = "clinical_variant",
shapekey = c("Endemic BL" = 15, "Sporadic BL" = 8),
colkey = c("EBV-positive" = wes_palette("Darjeeling1")[2],
"EBV-negative" = wes_palette("Darjeeling1")[4]),
legendPosition = "right") +
theme_cowplot() +
theme(aspect.ratio = 1)
ggsave("plots/05b-BL_hervs_no_y_biplot_pc1_pc2_ebvclinvar.pdf", height = 6, width = 6)
biplot(BL.herv.no.y.pca.obj,
lab = NULL,
showLoadings = FALSE,
boxedLoadingsNames = TRUE,
fillBoxedLoadings = alpha("white", 3/4),
pointSize = 3,
encircle = FALSE,
sizeLoadingsNames = 4,
lengthLoadingsArrowsFactor = 1.5,
drawConnectors = TRUE,
colby = "gender",
ellipse = FALSE,
shape = "ebv_status",
legendPosition = "right") +
theme_cowplot() +
theme(aspect.ratio = 1)
ggsave("plots/05b-BL_hervs_no_y_biplot_pc1_pc2_gender.pdf", height = 6, width = 6)
###################### BL SEPARATED BY MALE AND FEMALE #########################
### DESeq2 (HERVs Only)
BL_metadata_y <- BL_metadata[BL_metadata$gender == "Male",]
BL_metadata_x <- BL_metadata[BL_metadata$gender == "Female",]
BL.filt.herv.y <- BL.filt.herv[rownames(BL_metadata_y)]
BL.filt.herv.x <- BL.filt.herv[rownames(BL_metadata_x)]
stopifnot(all(colnames(BL.filt.herv.y) == rownames(BL_metadata_y)))
stopifnot(all(colnames(BL.filt.herv.x) == rownames(BL_metadata_x)))
BL.dds.y <- DESeq2::DESeqDataSetFromMatrix(countData = BL.filt.herv.y,
colData = BL_metadata_y,
design = ~ clinical_variant +
ebv_status)
BL.dds.y <- DESeq2::DESeq(BL.dds.y, parallel=T)
BL.tform.y <- DESeq2::varianceStabilizingTransformation(BL.dds.y, blind=FALSE)
## PCA
BL.herv.y.pca.obj <-
pca_standard(tform = BL.tform.y,
metadata = BL_metadata_y,
var = 0.1)
# 0.1, 5, 13, 16
# 0.2, 5, 13, 16,
# 0.3, 5, 13, 15
# 0.5, 6, 13, 5
# 0.7, 6, 13, 14
BL.dds.x <- DESeq2::DESeqDataSetFromMatrix(countData = BL.filt.herv.x,
colData = BL_metadata_x,
design = ~ clinical_variant +
ebv_status)
BL.dds.x <- DESeq2::DESeq(BL.dds.x, parallel=T)
BL.tform.x <- DESeq2::varianceStabilizingTransformation(BL.dds.x, blind=FALSE)
## PCA
BL.herv.x.pca.obj <-
pca_standard(tform = BL.tform.x,
metadata = BL_metadata_x,
var = 0.1)
# 0.1, 2, 10, 12
# 0.2, 2, 12, 12
# 0.3, 2, 11, 11
# 0.5, 41, 10, 11
########################### BL HERVS ONLY Y/X BIPLOTS ##########################
biplot(BL.herv.y.pca.obj,
lab = NULL,
showLoadings = FALSE,
boxedLoadingsNames = TRUE,
fillBoxedLoadings = alpha("white", 3/4),
pointSize = 3,
encircle = FALSE,
sizeLoadingsNames = 4,
lengthLoadingsArrowsFactor = 1.5,
drawConnectors = TRUE,
colby = "ebv_status",
ellipse = FALSE,
shape = "clinical_variant",
shapekey = c("Endemic BL" = 15, "Sporadic BL" = 8),
colkey = c("EBV-positive" = wes_palette("Darjeeling1")[2],
"EBV-negative" = wes_palette("Darjeeling1")[4]),
legendPosition = "right") +
theme_cowplot() +
theme(aspect.ratio = 1)
eigencorplot(BL.herv.y.pca.obj,
metavars = c('ebv_status','clinical_variant','subgroup',
'tissue_source_site','tumor_biopsy','MYC_SV_partner',
'subtype', 'Total_N_SSM', 'anatomic_site_classification'))
biplot(BL.herv.x.pca.obj,
lab = NULL,
showLoadings = FALSE,
boxedLoadingsNames = TRUE,
fillBoxedLoadings = alpha("white", 3/4),
pointSize = 3,
encircle = FALSE,
sizeLoadingsNames = 4,
lengthLoadingsArrowsFactor = 1.5,
drawConnectors = TRUE,
colby = "ebv_status",
ellipse = FALSE,
shape = "clinical_variant",
shapekey = c("Endemic BL" = 15, "Sporadic BL" = 8),
colkey = c("EBV-positive" = wes_palette("Darjeeling1")[2],
"EBV-negative" = wes_palette("Darjeeling1")[4]),
legendPosition = "right") +
theme_cowplot() +
theme(aspect.ratio = 1)
eigencorplot(BL.herv.x.pca.obj,
metavars = c('ebv_status','clinical_variant','subgroup','gender',
'tissue_source_site','tumor_biopsy','MYC_SV_partner',
'subtype', 'Total_N_SSM', 'anatomic_site_classification'))
############################# BL HERVS ONLY CHR 19 #############################
### DESeq2 (HERVs Only)
hervs.to.keep <- intersect(rownames(BL.filt.herv),
retro.annot$locus[retro.annot$chrom == "chr19"])
BL.filt.herv.19 <- BL.filt.herv[hervs.to.keep,]
BL.countdat <- BL.filt.herv.19
cat(sprintf('%d variables\n', nrow(BL.countdat)))
stopifnot(all(colnames(BL.countdat) == rownames(BL_metadata)))
BL.dds.19 <- DESeq2::DESeqDataSetFromMatrix(countData = BL.countdat,
colData = BL_metadata,
design = ~ clinical_variant +
ebv_status)
BL.dds.19 <- DESeq2::DESeq(BL.dds.19, parallel=T)
BL.tform.19 <- DESeq2::varianceStabilizingTransformation(BL.dds.19, blind=FALSE)
## PCA
BL.herv.19.pca.obj <-
pca_standard(tform = BL.tform.19,
metadata = BL_metadata,
var = 0.1)
# 0.01, 10, 11, 9
# 0.1, 10, 11, 9
# 0.3, 10, 9, 8
############################ BL HERVS ONLY 19 BIPLOTS ##########################
biplot(BL.herv.19.pca.obj,
lab = NULL,
showLoadings = FALSE,
boxedLoadingsNames = TRUE,
fillBoxedLoadings = alpha("white", 3/4),
pointSize = 3,
encircle = FALSE,
sizeLoadingsNames = 4,
lengthLoadingsArrowsFactor = 1.5,
drawConnectors = TRUE,
colby = "ebv_status",
ellipse = FALSE,
shape = "clinical_variant",
shapekey = c("Endemic BL" = 15, "Sporadic BL" = 8),
colkey = c("EBV-positive" = wes_palette("Darjeeling1")[2],
"EBV-negative" = wes_palette("Darjeeling1")[4]),
legendPosition = "right") +
theme_cowplot() +
theme(aspect.ratio = 1)
eigencorplot(BL.herv.19.pca.obj,
metavars = c('ebv_status','clinical_variant','subgroup','gender',
'tissue_source_site','tumor_biopsy','MYC_SV_partner',
'subtype', 'Total_N_SSM', 'anatomic_site_classification'))
################################ FL DESEQ HERVs ################################
### DESeq2 (HERVs Only)
FL.countdat <- FL.filt.herv
cat(sprintf('%d variables\n', nrow(FL.countdat)))
stopifnot(all(colnames(FL.countdat) == rownames(FL_metadata)))
FL.dds <- DESeq2::DESeqDataSetFromMatrix(countData = FL.countdat,
colData = FL_metadata,
design = ~ who_diagnosis)
FL.dds <- DESeq2::DESeq(FL.dds, parallel=T)
FL.tform <- DESeq2::varianceStabilizingTransformation(FL.dds, blind=FALSE)
## PCA
FL.herv.pca.obj <-
pca_standard(tform = FL.tform,
metadata = FL_metadata,
var = 0.1)
# 2 PCs for Elbow method
# 4 PCs for Horn method
# 4 PCs needed to explain 50 percent of variation
############################## FL BIPLOTS HERVs ################################
## Biplot with projects (only HERVs)
biplot(FL.herv.pca.obj,
lab = NULL,
showLoadings = FALSE,
boxedLoadingsNames = TRUE,
fillBoxedLoadings = alpha("white", 3/4),
pointSize = 4,
encircle = FALSE,
sizeLoadingsNames =4,
lengthLoadingsArrowsFactor = 3,
drawConnectors = TRUE,
colby = "site_progression",
colkey = c(wes_palette("GrandBudapest1")[1:5]),
shape = "who_diagnosis",
shapekey = c("FOLLICULAR GRADE 1" = 15,
"FOLLICULAR GRADE 2" = 8,
"FOLLICULAR GRADE 3A" = 2),
legendPosition = "right") +
theme_cowplot()
ggsave("plots/05b-FL_hervs_biplot_pc1_pc2_who.pdf", height = 6, width = 8)
eigencorplot(FL.herv.pca.obj,
metavars = c('who_diagnosis','gender','stage','performance_status',
'LDH_ratio','extranodal_sites','IPI_score',
'tumor_size', 'primary_treatment'))
################################ FL DESEQ GENES ################################
### DESeq2 (HERVs and genes)
FL.g.countdat <- FL.filt.tx
cat(sprintf('%d variables\n', nrow(FL.g.countdat)))
stopifnot(all(colnames(FL.g.countdat) == rownames(FL_metadata)))
FL.g.dds <- DESeq2::DESeqDataSetFromMatrix(countData = FL.g.countdat,
colData = FL_metadata,
design = ~ who_diagnosis)
FL.g.dds <- DESeq2::DESeq(FL.g.dds, parallel=T)
FL.g.tform <- DESeq2::varianceStabilizingTransformation(FL.g.dds, blind=FALSE)
## PCA
FL.g.pca.obj <-
pca_standard(tform = FL.g.tform,
metadata = FL_metadata,
var = 0.1)
# 6 PCs for Elbow method
# 5 PCs for Horn method
# 4 PCs needed to explain 50 percent of variation
all(rownames(FL.g.pca.obj$loadings) %in% rownames(gene_table))
rownames(FL.g.pca.obj$loadings) <-
gene_table[rownames(FL.g.pca.obj$loadings), 'display']
########################## FL BIPLOTS HERVs & GENES ############################
## Biplot with projects (only HERVs)
biplot(FL.g.pca.obj,
lab = NULL,
showLoadings = FALSE,
boxedLoadingsNames = TRUE,
fillBoxedLoadings = alpha("white", 3/4),
pointSize = 4,
encircle = FALSE,
sizeLoadingsNames =4,
lengthLoadingsArrowsFactor = 3,
drawConnectors = TRUE,
colby = "stage",
colkey = c(wes_palette("GrandBudapest1")[1:5]),
shape = "who_diagnosis",
shapekey = c("FOLLICULAR GRADE 1" = 15,
"FOLLICULAR GRADE 2" = 8,
"FOLLICULAR GRADE 3A" = 2),
legendPosition = "right") +
theme_cowplot()
ggsave("plots/05b-FL_hervsgenes_biplot_pc1_pc2_who.pdf", height = 6, width = 8)
########################## ALL TOGETHER DESEQ HERVs ############################