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Copy pathDESEq2_script_formal_v13.R
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DESEq2_script_formal_v13.R
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#0 LOADNG IN THE DATA, FILTERING
remove(list=ls())
`%notin%` = Negate(`%in%`)
setwd(dir = "~/RNA_Seq")
###########################################
#1 FUNCTION
###########################################-----
#install.packages(c("wesanderson", "RColorBrewer", "ggsci"))
#Fisher test for two overlapping sets (of genes)
my_fisher_test = function(overlap, set1, set2, background) {
test_m = matrix(c(overlap, set1-overlap, set2-overlap, background-(set1+set2-overlap)), nrow = 2)
x = fisher.test(test_m, alternative = "greater")
return(x)
}
#Plot first 2 PCs (no centroids)
plotPCA12 <- function (object, intgroup1 = "condition", ntop = 2000000, returnData = FALSE, intgroup2 = "Geog", intgroup3 = "Ecotype", my_xlims = c(-200, 200), my_ylims = c(-200, 200), my_background_col = col_control, thicko = 8)
{
rv <- rowVars(assay(object))
select <- order(rv, decreasing = TRUE)[seq_len(min(ntop,
length(rv)))]
pca <- prcomp(t(assay(object)[select, ]))
percentVar <- pca$sdev^2/sum(pca$sdev^2)
if (!all(intgroup1 %in% names(colData(object)))) {
stop("the argument 'intgroup1' should specify columns of colData(dds)")
}
intgroup1.df <- as.data.frame(colData(object)[, intgroup1,
drop = FALSE])
group1 <- if (length(intgroup1) > 1) {
factor(apply(intgroup1.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup1]]
}
if (!all(intgroup2 %in% names(colData(object)))) {
stop("the argument 'intgroup2' should specify columns of colData(dds)")
}
intgroup2.df <- as.data.frame(colData(object)[, intgroup2,
drop = FALSE])
group2 <- if (length(intgroup2) > 1) {
factor(apply(intgroup2.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup2]]
}
if (!all(intgroup3 %in% names(colData(object)))) {
stop("the argument 'intgroup3' should specify columns of colData(dds)")
}
intgroup3.df <- as.data.frame(colData(object)[, intgroup3,
drop = FALSE])
group3 <- if (length(intgroup3) > 1) {
factor(apply(intgroup3.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup3]]
}
d <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], PC3 = pca$x[, 3], PC4 = pca$x[, 4],
group1 = group1, group2 = group2, group3 = group3, name = colnames(object))
intgrop3d <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], PC3 = pca$x[, 3], PC4 = pca$x[, 4], group1 = group1,
intgroup1.df, group2 = group2, intgroup2.df, name = colnames(object))
if (returnData) {
attr(d, "percentVar") <- percentVar[1,2]
return(d)
}
ggplot(data = d, aes_string(x = "PC1", y = "PC2")) + xlim(my_xlims[1], my_xlims[2]) + ylim(my_ylims[1], my_ylims[2])+
# ggplot(data = d, aes_string(x = "PC1", y = "PC2"))+
geom_point(size = 11, stroke=2, aes(shape = group1, col = group2, fill = group3)) + scale_shape_manual(values = c(21,24))+
xlab(paste0("PC1: ", round(percentVar[1] * 100), "% variance")) +
scale_colour_manual(values = c(col_PPBD, col_GRSA)) + ylab(paste0("PC2: ", round(percentVar[2]*100), "% variance")) + scale_fill_manual(values = alpha(c(col_coast, col_mine), 0.7)) +
coord_fixed() + theme(legend.position = "none")+
theme(panel.border = element_rect(colour = my_background_col, fill = NA, size = thicko), panel.background = element_blank(), aspect.ratio = 1, axis.title = element_text(size = 27), axis.text = element_text(size = 20)) #, panel.grid.major=element_line(colour="lightgrey"))
}
plotPCA12_revcol <- function (object, intgroup1 = "Geog", ntop = 2000000, returnData = FALSE, intgroup2 = "Ecotype", intgroup3 = "condition", my_xlims = c(-200, 200), my_ylims = c(-200, 200), my_background_col = col_control, thicko = 8)
{
rv <- rowVars(assay(object))
select <- order(rv, decreasing = TRUE)[seq_len(min(ntop,
length(rv)))]
pca <- prcomp(t(assay(object)[select, ]))
percentVar <- pca$sdev^2/sum(pca$sdev^2)
if (!all(intgroup1 %in% names(colData(object)))) {
stop("the argument 'intgroup1' should specify columns of colData(dds)")
}
intgroup1.df <- as.data.frame(colData(object)[, intgroup1,
drop = FALSE])
group1 <- if (length(intgroup1) > 1) {
factor(apply(intgroup1.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup1]]
}
if (!all(intgroup2 %in% names(colData(object)))) {
stop("the argument 'intgroup2' should specify columns of colData(dds)")
}
intgroup2.df <- as.data.frame(colData(object)[, intgroup2,
drop = FALSE])
group2 <- if (length(intgroup2) > 1) {
factor(apply(intgroup2.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup2]]
}
if (!all(intgroup3 %in% names(colData(object)))) {
stop("the argument 'intgroup3' should specify columns of colData(dds)")
}
intgroup3.df <- as.data.frame(colData(object)[, intgroup3,
drop = FALSE])
group3 <- if (length(intgroup3) > 1) {
factor(apply(intgroup3.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup3]]
}
d <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], PC3 = pca$x[, 3], PC4 = pca$x[, 4],
group1 = group1, group2 = group2, group3 = group3, name = colnames(object))
intgrop3d <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], PC3 = pca$x[, 3], PC4 = pca$x[, 4], group1 = group1,
intgroup1.df, group2 = group2, intgroup2.df, name = colnames(object))
if (returnData) {
attr(d, "percentVar") <- percentVar[1,2]
return(d)
}
ggplot(data = d, aes_string(x = "PC1", y = "PC2")) + xlim(my_xlims[1], my_xlims[2]) + ylim(my_ylims[1], my_ylims[2])+
# ggplot(data = d, aes_string(x = "PC1", y = "PC2"))+
geom_point(size = 8, stroke=2, aes(shape = group1, col = group3, fill = group2)) + scale_shape_manual(values = c(21,24)) + xlab(paste0("PC1: ", round(percentVar[1] *
100), "% variance")) +
scale_colour_manual(values = c(col_PPBD, col_GRSA)) + ylab(paste0("PC2: ", round(percentVar[2]*100), "% variance")) + scale_fill_manual(values = c(col_mine, col_coast)) +
coord_fixed() + theme(legend.position = "none")+
theme(panel.border = element_rect(colour = my_background_col, fill = NA, size = thicko), panel.background = element_blank(), aspect.ratio = 1, axis.title = element_text(size = 22), axis.text = element_text(size = 15)) #, panel.grid.major=element_line(colour="lightgrey"))
}
#Plot first 2 PCs (centroids)
plotPCA12_centroids <- function (object, intgroup1 = "Geog", ntop = 2000000, returnData = FALSE, intgroup2 = "Ecotype", intgroup3 = "condition", my_xlims = c(-200, 200), my_ylims = c(-200, 200), my_background_col = col_control, thicko = 4)
{
rv <- rowVars(assay(object))
select <- order(rv, decreasing = TRUE)[seq_len(min(ntop,
length(rv)))]
pca <- prcomp(t(assay(object)[select, ]))
pca_thing = pca
percentVar <- pca$sdev^2/sum(pca$sdev^2)
if (!all(intgroup1 %in% names(colData(object)))) {
stop("the argument 'intgroup1' should specify columns of colData(dds)")
}
intgroup1.df <- as.data.frame(colData(object)[, intgroup1,
drop = FALSE])
group1 <- if (length(intgroup1) > 1) {
factor(apply(intgroup1.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup1]]
}
if (!all(intgroup2 %in% names(colData(object)))) {
stop("the argument 'intgroup2' should specify columns of colData(dds)")
}
intgroup2.df <- as.data.frame(colData(object)[, intgroup2,
drop = FALSE])
group2 <- if (length(intgroup2) > 1) {
factor(apply(intgroup2.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup2]]
}
if (!all(intgroup3 %in% names(colData(object)))) {
stop("the argument 'intgroup3' should specify columns of colData(dds)")
}
intgroup3.df <- as.data.frame(colData(object)[, intgroup3,
drop = FALSE])
group3 <- if (length(intgroup3) > 1) {
factor(apply(intgroup3.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup3]]
}
d <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], PC3 = pca$x[, 3], PC4 = pca$x[, 4],
group1 = group1, group2 = group2, group3 = group3, name = colnames(object))
intgrop3d <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], PC3 = pca$x[, 3], PC4 = pca$x[, 4], group1 = group1,
intgroup1.df, group2 = group2, intgroup2.df, name = colnames(object))
#Need to add the centroids somehow.
if (returnData) {
attr(d, "percentVar") <- percentVar[3,4]
return(d)
}
return(pca_thing)
ggplot(data = d, aes_string(x = "PC1", y = "PC2")) + xlim(my_xlims[1], my_xlims[2]) + ylim(my_ylims[1], my_ylims[2])+
# ggplot(data = d, aes_string(x = "PC1", y = "PC2"))+
geom_point(size = 10, stroke=4, aes(shape = group1, col = group3, fill = group2)) + scale_shape_manual(values = c(21,24)) + xlab(paste0("PC1: ", round(percentVar[1] *
100), "% variance")) + scale_fill_manual(values = c(col_coast, col_mine)) +
scale_colour_manual(values = c(col_PPBD, col_GRSA)) + ylab(paste0("PC2: ", round(percentVar[2]*100), "% variance")) +
coord_fixed() + theme(legend.position = "none")+
theme(panel.border = element_rect(colour = my_background_col, fill = NA, size = thicko), panel.background = element_blank(), aspect.ratio = 1) #, panel.grid.major=element_line(colour="lightgrey"))
}
plotPCA34 <- function (object, intgroup1 = "Geog", ntop = 2000000, returnData = FALSE, intgroup2 = "Ecotype", intgroup3 = "condition", my_xlims = c(-200, 200), my_ylims = c(-200, 200), my_background_col = col_control, thicko = 8)
{
rv <- rowVars(assay(object))
select <- order(rv, decreasing = TRUE)[seq_len(min(ntop,
length(rv)))]
pca <- prcomp(t(assay(object)[select, ]))
percentVar <- pca$sdev^2/sum(pca$sdev^2)
if (!all(intgroup1 %in% names(colData(object)))) {
stop("the argument 'intgroup1' should specify columns of colData(dds)")
}
intgroup1.df <- as.data.frame(colData(object)[, intgroup1,
drop = FALSE])
group1 <- if (length(intgroup1) > 1) {
factor(apply(intgroup1.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup1]]
}
if (!all(intgroup2 %in% names(colData(object)))) {
stop("the argument 'intgroup2' should specify columns of colData(dds)")
}
intgroup2.df <- as.data.frame(colData(object)[, intgroup2,
drop = FALSE])
group2 <- if (length(intgroup2) > 1) {
factor(apply(intgroup2.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup2]]
}
if (!all(intgroup3 %in% names(colData(object)))) {
stop("the argument 'intgroup3' should specify columns of colData(dds)")
}
intgroup3.df <- as.data.frame(colData(object)[, intgroup3,
drop = FALSE])
group3 <- if (length(intgroup3) > 1) {
factor(apply(intgroup3.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup3]]
}
d <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], PC3 = pca$x[, 3], PC4 = pca$x[, 4],
group1 = group1, group2 = group2, group3 = group3, name = colnames(object))
intgrop3d <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], PC3 = pca$x[, 3], PC4 = pca$x[, 4], group1 = group1,
intgroup1.df, group2 = group2, intgroup2.df, name = colnames(object))
if (returnData) {
attr(d, "percentVar") <- percentVar[3,4]
return(d)
}
ggplot(data = d, aes_string(x = "PC3", y = "PC4")) + xlim(my_xlims[1], my_xlims[2]) + ylim(my_ylims[1], my_ylims[2])+
# ggplot(data = d, aes_string(x = "PC1", y = "PC2"))+
geom_point(size = 8, stroke=2, aes(shape = group1, col = group3, fill = group2)) + scale_shape_manual(values = c(21,24)) + xlab(paste0("PC3: ", round(percentVar[3] *
100), "% variance")) +
scale_colour_manual(values = c(col_PPBD, col_GRSA)) + ylab(paste0("PC4: ", round(percentVar[4]*100), "% variance")) + scale_fill_manual(values = alpha(c(col_coast, col_mine), 0.7)) +
coord_fixed() + theme(legend.position = "none")+
theme(panel.border = element_rect(colour = my_background_col, fill = NA, size = thicko), panel.background = element_blank(), aspect.ratio = 1, axis.title = element_text(size = 22), axis.text = element_text(size = 15)) #, panel.grid.major=element_line(colour="lightgrey"))
}
plotPCA56 <- function (object, intgroup1 = "Geog", ntop = 2000000, returnData = FALSE, intgroup2 = "Ecotype", intgroup3 = "condition", my_xlims = c(-200, 200), my_ylims = c(-200, 200), my_background_col = col_control, thicko = 8)
{
rv <- rowVars(assay(object))
select <- order(rv, decreasing = TRUE)[seq_len(min(ntop,
length(rv)))]
pca <- prcomp(t(assay(object)[select, ]))
percentVar <- pca$sdev^2/sum(pca$sdev^2)
if (!all(intgroup1 %in% names(colData(object)))) {
stop("the argument 'intgroup1' should specify columns of colData(dds)")
}
intgroup1.df <- as.data.frame(colData(object)[, intgroup1,
drop = FALSE])
group1 <- if (length(intgroup1) > 1) {
factor(apply(intgroup1.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup1]]
}
if (!all(intgroup2 %in% names(colData(object)))) {
stop("the argument 'intgroup2' should specify columns of colData(dds)")
}
intgroup2.df <- as.data.frame(colData(object)[, intgroup2,
drop = FALSE])
group2 <- if (length(intgroup2) > 1) {
factor(apply(intgroup2.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup2]]
}
if (!all(intgroup3 %in% names(colData(object)))) {
stop("the argument 'intgroup3' should specify columns of colData(dds)")
}
intgroup3.df <- as.data.frame(colData(object)[, intgroup3,
drop = FALSE])
group3 <- if (length(intgroup3) > 1) {
factor(apply(intgroup3.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup3]]
}
d <- data.frame(PC5 = pca$x[, 5], PC6 = pca$x[, 6],
group1 = group1, group2 = group2, group3 = group3, name = colnames(object))
intgrop3d <- data.frame(PC5 = pca$x[, 5], PC6 = pca$x[, 6], group1 = group1,
intgroup1.df, group2 = group2, intgroup2.df, name = colnames(object))
if (returnData) {
attr(d, "percentVar") <- percentVar[5,6]
return(d)
}
ggplot(data = d, aes_string(x = "PC5", y = "PC6")) + xlim(my_xlims[1], my_xlims[2]) + ylim(my_ylims[1], my_ylims[2])+
# ggplot(data = d, aes_string(x = "PC1", y = "PC2"))+
geom_point(size = 8, stroke=2, aes(shape = group1, col = group3, fill = group2)) + scale_shape_manual(values = c(21,24)) + xlab(paste0("PC5: ", round(percentVar[5] *
100), "% variance")) +
scale_colour_manual(values = c(col_PPBD, col_GRSA)) + ylab(paste0("PC6: ", round(percentVar[6]*100), "% variance")) + scale_fill_manual(values = alpha(c(col_coast, col_mine), 0.7)) +
coord_fixed() + theme(legend.position = "none")+
theme(panel.border = element_rect(colour = my_background_col, fill = NA, size = thicko), panel.background = element_blank(), aspect.ratio = 1, axis.title = element_text(size = 22), axis.text = element_text(size = 15)) #, panel.grid.major=element_line(colour="lightgrey"))
}
plotPCA34_revcol <- function (object, intgroup1 = "Geog", ntop = 2000000, returnData = FALSE, intgroup2 = "Ecotype", intgroup3 = "condition", my_xlims = c(-200, 200), my_ylims = c(-200, 200), my_background_col = col_control, thicko = 8)
{
rv <- rowVars(assay(object))
select <- order(rv, decreasing = TRUE)[seq_len(min(ntop,
length(rv)))]
pca <- prcomp(t(assay(object)[select, ]))
percentVar <- pca$sdev^2/sum(pca$sdev^2)
if (!all(intgroup1 %in% names(colData(object)))) {
stop("the argument 'intgroup1' should specify columns of colData(dds)")
}
intgroup1.df <- as.data.frame(colData(object)[, intgroup1,
drop = FALSE])
group1 <- if (length(intgroup1) > 1) {
factor(apply(intgroup1.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup1]]
}
if (!all(intgroup2 %in% names(colData(object)))) {
stop("the argument 'intgroup2' should specify columns of colData(dds)")
}
intgroup2.df <- as.data.frame(colData(object)[, intgroup2,
drop = FALSE])
group2 <- if (length(intgroup2) > 1) {
factor(apply(intgroup2.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup2]]
}
if (!all(intgroup3 %in% names(colData(object)))) {
stop("the argument 'intgroup3' should specify columns of colData(dds)")
}
intgroup3.df <- as.data.frame(colData(object)[, intgroup3,
drop = FALSE])
group3 <- if (length(intgroup3) > 1) {
factor(apply(intgroup3.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup3]]
}
d <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], PC3 = pca$x[, 3], PC4 = pca$x[, 4],
group1 = group1, group2 = group2, group3 = group3, name = colnames(object))
intgrop3d <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], PC3 = pca$x[, 3], PC4 = pca$x[, 4], group1 = group1,
intgroup1.df, group2 = group2, intgroup2.df, name = colnames(object))
if (returnData) {
attr(d, "percentVar") <- percentVar[3,4]
return(d)
}
ggplot(data = d, aes_string(x = "PC3", y = "PC4")) + xlim(my_xlims[1], my_xlims[2]) + ylim(my_ylims[1], my_ylims[2])+
# ggplot(data = d, aes_string(x = "PC1", y = "PC2"))+
geom_point(size = 8, stroke=2, aes(shape = group1, col = group3, fill = group2)) + scale_shape_manual(values = c(21,24)) + xlab(paste0("PC3: ", round(percentVar[3] *
100), "% variance")) +
scale_colour_manual(values = c(col_PPBD, col_GRSA)) + ylab(paste0("PC4: ", round(percentVar[4]*100), "% variance")) + scale_fill_manual(values = c(col_mine, col_coast)) +
coord_fixed() + theme(legend.position = "none")+
theme(panel.border = element_rect(colour = my_background_col, fill = NA, size = thicko), panel.background = element_blank(), aspect.ratio = 1, axis.title = element_text(size = 22), axis.text = element_text(size = 15)) #, panel.grid.major=element_line(colour="lightgrey"))
}
plotPCA14_revcol <- function (object, intgroup1 = "Geog", ntop = 2000000, returnData = FALSE, intgroup2 = "Ecotype", intgroup3 = "condition", my_xlims = c(-200, 200), my_ylims = c(-200, 200), my_background_col = col_control, thicko = 8)
{
rv <- rowVars(assay(object))
select <- order(rv, decreasing = TRUE)[seq_len(min(ntop,
length(rv)))]
pca <- prcomp(t(assay(object)[select, ]))
percentVar <- pca$sdev^2/sum(pca$sdev^2)
if (!all(intgroup1 %in% names(colData(object)))) {
stop("the argument 'intgroup1' should specify columns of colData(dds)")
}
intgroup1.df <- as.data.frame(colData(object)[, intgroup1,
drop = FALSE])
group1 <- if (length(intgroup1) > 1) {
factor(apply(intgroup1.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup1]]
}
if (!all(intgroup2 %in% names(colData(object)))) {
stop("the argument 'intgroup2' should specify columns of colData(dds)")
}
intgroup2.df <- as.data.frame(colData(object)[, intgroup2,
drop = FALSE])
group2 <- if (length(intgroup2) > 1) {
factor(apply(intgroup2.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup2]]
}
if (!all(intgroup3 %in% names(colData(object)))) {
stop("the argument 'intgroup3' should specify columns of colData(dds)")
}
intgroup3.df <- as.data.frame(colData(object)[, intgroup3,
drop = FALSE])
group3 <- if (length(intgroup3) > 1) {
factor(apply(intgroup3.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup3]]
}
d <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], PC3 = pca$x[, 3], PC4 = pca$x[, 4],
group1 = group1, group2 = group2, group3 = group3, name = colnames(object))
intgrop3d <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], PC3 = pca$x[, 3], PC4 = pca$x[, 4], group1 = group1,
intgroup1.df, group2 = group2, intgroup2.df, name = colnames(object))
if (returnData) {
attr(d, "percentVar") <- percentVar[1,4]
return(d)
}
ggplot(data = d, aes_string(x = "PC1", y = "PC4")) + xlim(my_xlims[1], my_xlims[2]) + ylim(my_ylims[1], my_ylims[2])+
# ggplot(data = d, aes_string(x = "PC1", y = "PC2"))+
geom_point(size = 8, stroke=2, aes(shape = group1, col = group3, fill = group2)) + scale_shape_manual(values = c(21,24)) + xlab(paste0("PC3: ", round(percentVar[1] *
100), "% variance")) +
scale_colour_manual(values = c(col_PPBD, col_GRSA)) + ylab(paste0("PC4: ", round(percentVar[4]*100), "% variance")) + scale_fill_manual(values = c(col_mine, col_coast)) +
coord_fixed() + theme(legend.position = "none")+
theme(panel.border = element_rect(colour = my_background_col, fill = NA, size = thicko), panel.background = element_blank(), aspect.ratio = 1, axis.title = element_text(size = 22), axis.text = element_text(size = 15)) #, panel.grid.major=element_line(colour="lightgrey"))
}
plotPCA13 <- function (object, intgroup1 = "Geog", ntop = 2000000, returnData = FALSE, intgroup2 = "Ecotype", intgroup3 = "condition", my_xlims = c(-200, 200), my_ylims = c(-200, 200), my_background_col = col_control, thicko = 8)
{
rv <- rowVars(assay(object))
select <- order(rv, decreasing = TRUE)[seq_len(min(ntop,
length(rv)))]
pca <- prcomp(t(assay(object)[select, ]))
percentVar <- pca$sdev^2/sum(pca$sdev^2)
if (!all(intgroup1 %in% names(colData(object)))) {
stop("the argument 'intgroup1' should specify columns of colData(dds)")
}
intgroup1.df <- as.data.frame(colData(object)[, intgroup1,
drop = FALSE])
group1 <- if (length(intgroup1) > 1) {
factor(apply(intgroup1.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup1]]
}
if (!all(intgroup2 %in% names(colData(object)))) {
stop("the argument 'intgroup2' should specify columns of colData(dds)")
}
intgroup2.df <- as.data.frame(colData(object)[, intgroup2,
drop = FALSE])
group2 <- if (length(intgroup2) > 1) {
factor(apply(intgroup2.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup2]]
}
if (!all(intgroup3 %in% names(colData(object)))) {
stop("the argument 'intgroup3' should specify columns of colData(dds)")
}
intgroup3.df <- as.data.frame(colData(object)[, intgroup3,
drop = FALSE])
group3 <- if (length(intgroup3) > 1) {
factor(apply(intgroup3.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup3]]
}
d <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], PC3 = pca$x[, 3], PC4 = pca$x[, 4],
group1 = group1, group2 = group2, group3 = group3, name = colnames(object))
intgrop3d <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], PC3 = pca$x[, 3], PC4 = pca$x[, 4], group1 = group1,
intgroup1.df, group2 = group2, intgroup2.df, name = colnames(object))
if (returnData) {
attr(d, "percentVar") <- percentVar[1,3]
return(d)
}
ggplot(data = d, aes_string(x = "PC1", y = "PC3")) + xlim(my_xlims[1], my_xlims[2]) + ylim(my_ylims[1], my_ylims[2])+
# ggplot(data = d, aes_string(x = "PC1", y = "PC2"))+
geom_point(size = 8, stroke=2, aes(shape = group1, col = group3, fill = group2)) + scale_shape_manual(values = c(21,24)) + xlab(paste0("PC1: ", round(percentVar[1] *
100), "% variance")) +
scale_colour_manual(values = c(col_PPBD, col_GRSA)) + ylab(paste0("PC3: ", round(percentVar[3]*100), "% variance")) + scale_fill_manual(values = c(col_coast, col_mine)) +
coord_fixed() + theme(legend.position = "none")+
theme(panel.border = element_rect(colour = my_background_col, fill = NA, size = thicko), panel.background = element_blank(), aspect.ratio = 1, axis.title = element_text(size = 22), axis.text = element_text(size = 15)) #, panel.grid.major=element_line(colour="lightgrey"))
}
plotPCA13_revcol <- function (object, intgroup1 = "Geog", ntop = 2000000, returnData = FALSE, intgroup2 = "Ecotype", intgroup3 = "condition", my_xlims = c(-200, 200), my_ylims = c(-200, 200), my_background_col = col_control, thicko = 8)
{
rv <- rowVars(assay(object))
select <- order(rv, decreasing = TRUE)[seq_len(min(ntop,
length(rv)))]
pca <- prcomp(t(assay(object)[select, ]))
percentVar <- pca$sdev^2/sum(pca$sdev^2)
if (!all(intgroup1 %in% names(colData(object)))) {
stop("the argument 'intgroup1' should specify columns of colData(dds)")
}
intgroup1.df <- as.data.frame(colData(object)[, intgroup1,
drop = FALSE])
group1 <- if (length(intgroup1) > 1) {
factor(apply(intgroup1.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup1]]
}
if (!all(intgroup2 %in% names(colData(object)))) {
stop("the argument 'intgroup2' should specify columns of colData(dds)")
}
intgroup2.df <- as.data.frame(colData(object)[, intgroup2,
drop = FALSE])
group2 <- if (length(intgroup2) > 1) {
factor(apply(intgroup2.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup2]]
}
if (!all(intgroup3 %in% names(colData(object)))) {
stop("the argument 'intgroup3' should specify columns of colData(dds)")
}
intgroup3.df <- as.data.frame(colData(object)[, intgroup3,
drop = FALSE])
group3 <- if (length(intgroup3) > 1) {
factor(apply(intgroup3.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup3]]
}
d <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], PC3 = pca$x[, 3], PC4 = pca$x[, 4],
group1 = group1, group2 = group2, group3 = group3, name = colnames(object))
intgrop3d <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], PC3 = pca$x[, 3], PC4 = pca$x[, 4], group1 = group1,
intgroup1.df, group2 = group2, intgroup2.df, name = colnames(object))
if (returnData) {
attr(d, "percentVar") <- percentVar[1,3]
return(d)
}
ggplot(data = d, aes_string(x = "PC1", y = "PC3")) + xlim(my_xlims[1], my_xlims[2]) + ylim(my_ylims[1], my_ylims[2])+
# ggplot(data = d, aes_string(x = "PC1", y = "PC2"))+
geom_point(size = 8, stroke=2, aes(shape = group1, col = group3, fill = group2)) + scale_shape_manual(values = c(21,24)) + xlab(paste0("PC1: ", round(percentVar[1] *
100), "% variance")) +
scale_colour_manual(values = c(col_PPBD, col_GRSA)) + ylab(paste0("PC3: ", round(percentVar[3]*100), "% variance")) + scale_fill_manual(values = c(col_mine, col_coast)) +
coord_fixed() + theme(legend.position = "none")+
theme(panel.border = element_rect(colour = my_background_col, fill = NA, size = thicko), panel.background = element_blank(), aspect.ratio = 1, axis.title = element_text(size = 22), axis.text = element_text(size = 15)) #, panel.grid.major=element_line(colour="lightgrey"))
}
plotPCA_3D <- function (object, intgroup1 = "Geog", ntop = 2000000, returnData = FALSE, intgroup2 = "Ecotype", intgroup3 = "condition", my_xlims = c(-200, 200), my_ylims = c(-200, 200), my_background_col = col_control, thicko = 8)
{
#TEMP
object=vst_all
#
rv <- rowVars(assay(object))
select <- order(rv, decreasing = TRUE)[seq_len(min(ntop,
length(rv)))]
pca <- prcomp(t(assay(object)[select, ]))
percentVar <- pca$sdev^2/sum(pca$sdev^2)
if (!all(intgroup1 %in% names(colData(object)))) {
stop("the argument 'intgroup1' should specify columns of colData(dds)")
}
intgroup1.df <- as.data.frame(colData(object)[, intgroup1,
drop = FALSE])
group1 <- if (length(intgroup1) > 1) {
factor(apply(intgroup1.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup1]]
}
if (!all(intgroup2 %in% names(colData(object)))) {
stop("the argument 'intgroup2' should specify columns of colData(dds)")
}
intgroup2.df <- as.data.frame(colData(object)[, intgroup2,
drop = FALSE])
group2 <- if (length(intgroup2) > 1) {
factor(apply(intgroup2.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup2]]
}
if (!all(intgroup3 %in% names(colData(object)))) {
stop("the argument 'intgroup3' should specify columns of colData(dds)")
}
intgroup3.df <- as.data.frame(colData(object)[, intgroup3,
drop = FALSE])
group3 <- if (length(intgroup3) > 1) {
factor(apply(intgroup3.df, 1, paste, collapse = " : "))
}else {
colData(object)[[intgroup3]]
}
get_colors <- function(groups, group.col = palette()){
groups <- as.factor(groups)
ngrps <- length(levels(groups))
if(ngrps > length(group.col))
group.col <- rep(group.col, ngrps)
color2 <- group.col[as.numeric(groups)]
names(color2) <- as.vector(groups)
return(color2)
}
intgroup3.df$Ecotype = as.factor(intgroup3.df$Ecotype)
intgroup3.df
get_colors(intgroup3.df$Ecotype)
d <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], PC3 = pca$x[, 3], PC4 = pca$x[, 4],
group1 = group1, group2 = group2, group3 = group3, name = colnames(object))
intgrop3d <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], PC3 = pca$x[, 3], PC4 = pca$x[, 4], group1 = group1,
intgroup1.df, group2 = group2, intgroup2.df, name = colnames(object))
if (returnData) {
attr(d, "percentVar") <- percentVar[1,3]
return(d)
}
plot3d(d[,1:3], size=10, type='p'
, xlim = c(-50,50), ylim=c(-50,50), zlim=c(-50,50), )
color_vector = get_colors(intgroup3.df$Ecotype)
color_vector
rgl.spheres(d[,1], d[,2], d[,3], color = color_vector)
#plot3d(d[,1:3], color = color_vector)
text3d(d[,1]+2, d[,2]+10, d[,3]+2,
texts=c(rownames(d)), cex= 0.7, pos=3)
# rgl.postscript("3DPCA-Merge.pdf", "pdf")
color_vector
vector_thing = color_vector
with(d, plot3d(d[,1],d[,2],d[,3], col = color_vector, type ='p'))
rglwidget()
}
heatmap_samples <-function(vst_all) {
sampleDists <- dist(t(assay(vst_all)))
library("RColorBrewer")
#install.packages("pheatmap")
library(pheatmap)
sampleDistMatrix <- as.matrix(sampleDists)
rownames(sampleDistMatrix) <- paste(vst_all$Pop_Cond)
colnames(sampleDistMatrix) <- NULL
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
pheatmap(sampleDistMatrix,
clustering_distance_rows=sampleDists,
clustering_distance_cols=sampleDists,
col=colors)
}
#permutation stuff: does plotting a/c vs. b/c give a spurious correlation?
a = rnorm(100, mean = 10, sd = 1)
b = rnorm(100, mean = 10, sd = 1)
c = rnorm(100, mean = 10, sd = 1)
woof1 = a/c
woof2 = b/c
plot(woof1, woof2, xlim = c(-3,3), ylim = c(-3,3))
summary(lm(woof2 ~ woof1)) #So there is a correlation...
###################################
#2 Fisher Table setup
###################################
Fisher_Table = data.frame("Comparison", "All_pvalue (0 = <2.2e-16)", "All_oddsratio", "Samedir_pvalue", "Samedir_oddsratio", stringsAsFactors = FALSE)
##################################
#3 Colours
##################################
library(wesanderson)
library(RColorBrewer)
#install.packages("ggsci")
library(ggsci)
library(RColorBrewer)
color_list = c("#75EAA9","#B6EA50", "#F5FF74")
ctrl_green = "#00CC33"
tmp = col2rgb(ctrl_green)
ctrl_green = rgb(tmp[1]/255, tmp[2]/255, tmp[3]/255, alpha = 0.3)
col_coast = "#87CEFA"
col_mine = "orange"
col_PPBD = "#046C9A"
col_GRSA = "#F21A00"
col_control = "antiquewhite"
tmp = col2rgb("lightgreen")
col_zinc = rgb(tmp[1]/255, tmp[2]/255, tmp[3]/255, alpha = 0.3)
col_zinc2 = rgb(tmp[1]/255, tmp[2]/255, tmp[3]/255, alpha =1)
zinc_col = rgb(tmp[1]/255, tmp[2]/255, tmp[3]/255)
ctrl_col = col_control
ctrl_col_temp = col2rgb(ctrl_col)
ctrl_col_temp2 = rgb(ctrl_col_temp[1]/255, ctrl_col_temp[2]/255, ctrl_col_temp[3]/255)
tmp = col2rgb("yellow1")
gold_plot = rgb(tmp[1]/255, tmp[2]/255, tmp[3]/255, alpha = 0.3)
gold_plot_light = rgb(tmp[1]/255, tmp[2]/255, tmp[3]/255, alpha = 0.1)
tmp = col2rgb("red")
red_plot = rgb(tmp[1]/255, tmp[2]/255, tmp[3]/255, alpha = 0.3)
purple_col = col2rgb("plum")
purple_plot = rgb(purple_col[1]/255, purple_col[2]/255, purple_col[3]/255, alpha = 0.3)
purple_col2 = rgb(purple_col[1]/255, purple_col[2]/255, purple_col[3]/255)
another_plot_purple = purple_col2
tmp = col2rgb(col_mine)
mine_plot = rgb(tmp[1]/255, tmp[2]/255, tmp[3]/255, alpha = 0.2)
tmp = col2rgb(col_coast)
coast_plot = rgb(tmp[1]/255, tmp[2]/255, tmp[3]/255, alpha = 0.2)
tmp = col2rgb(col_GRSA)
GRSA_plot = rgb(tmp[1]/255, tmp[2]/255, tmp[3]/255, alpha = 0.6)
tmp = col2rgb(col_PPBD)
PPBD_plot = rgb(tmp[1]/255, tmp[2]/255, tmp[3]/255, alpha = 0.6)
###########################################
#4 Load relevant libraries
###########################################
#install.packages(c("DESeq2", "tximport", "ggplot2", "reshape2", "VennDiagram", "gghalves", "ggnewscale", "cowplot"))
#if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#BiocManager::install("DESeq2")
#BiocManager::install("DESeq2")
#install.packages("tximport")
#BiocManager::install("tximport")
#BiocManager::install("rhdf5")
#install.packages("DESeq2")
library(DESeq2) #1.26.0
library(tximport) #1.14.2
library(ggplot2)
library(reshape2)
library(VennDiagram)
library(gghalves)
library(ggnewscale)
#library(rcompanion)
library(cowplot)
##########################################
#5 Setup and filtering
##########################################
samples = read.table("~/RNA_Seq/samples_file.txt_forslueth.tsv", sep = "\t", header = TRUE)
files = file.path("~/RNA_Seq/Formal_RNASeq/", samples$Sample, "abundance.h5")
samples
#Ok well backup this PC too
#Adjust samples table to add "Pop_Cond" variable
samples$Pop_Cond = paste(samples$Pop, samples$Cond, sep="")
samples$Ecotype = c("C", "C", "C", "C", "C", "C", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M","M", "C", "C", "C", "C", "C", "C")
samples$Geog = c("E", "E", "E", "E", "E", "E", "W", "W", "W", "W", "W", "W", "E", "E", "E", "E", "E", "E", "W", "W", "W", "W", "W", "W")
samples$Ecotype = as.factor(samples$Ecotype)
samples$Geog = as.factor(samples$Geog)
samples
#Read in Trinity gene-transcript map
tx2gene = read.csv("~/RNA_Seq/Trinity-clusters.txt", sep = "\t", header = FALSE)
tx2gene = tx2gene[c("V2", "V1")]
head(tx2gene)
#Read in counts - summarise to gene level
tx.kallisto = tximport(files, type = "kallisto", txOut = FALSE, tx2gene=tx2gene, ignoreTxVersion = TRUE)
head(tx.kallisto)
#Convert tx object to DESeq object (design matrix can change later)
ddTxi = DESeqDataSetFromTximport(tx.kallisto, colData = samples, design = ~ Cond + Pop)
remove(tx.kallisto)
dim(ddTxi)
ddTxi_backup = ddTxi
#Filtering 1 - filter contigs by sequence criteria
#Either a >50bp match to latifolia genome, and/or a >50bp match to an Embryophyte
#Load in data
#blast transcripts against SwissProt/UniProt10 database [version 290]
blast_info = read.csv("~/RNA_Seq/blastp.outfmt6.emb.longesti", header = FALSE, sep = "\t")
#Use blat to search against Silene uniflora genome (GCA_018983105.1_ASM1898310v1_genomic.fna)
blat_uniflora_info = read.csv("~/SR29_sorted_merged_outfile_best.psl.longesti", sep = "\t", header = FALSE)
#GC content caluclated for each contigs longest isoform
GC_content = read.csv("~/RNA_Seq/SU_RedSamp_k25mincov2.fasta.trans_filtered.GClongesti", header=FALSE, sep="\t")
emb_yes = blast_info[blast_info$V13 == "emb_yes",]
emb_no = blast_info[blast_info$V13 == "emb_no",]
dim(emb_yes)
dim(emb_no)
dim(blast_info) #73k out of...121k. So 50k don't have a match to anything.
dim(ddTxi_backup)
blat_uniflora_info$match_length = blat_uniflora_info$V1+blat_uniflora_info$V2
blat_uniflora_info$pc = blat_uniflora_info$V1/(blat_uniflora_info$V1+blat_uniflora_info$V2)
#i) Match length to uniflora genome must be >200 and %ID > 90%
blat_uniflora_good = blat_uniflora_info[blat_uniflora_info$match_length > 200 & blat_uniflora_info$pc > 0.9,]
blat_200plus_list = blat_uniflora_good$V10
length(blat_200plus_list)
#ii) Match length must be >200 and %ID > 70%
emb_200plus70pc = emb_yes[emb_yes$V4 > 200 & emb_yes$V3 > 70,]
#Filtering 1: only genes either matching i) or ii) are included, provisionally.
GC_content_Filter2 = GC_content[GC_content$V1 %in% blat_200plus_list | GC_content$V1 %in% emb_200plus70pc$V1,]
dim(GC_content_Filter2)
#Filtering 2: any of these genes whose top hit is to a non-embryophyte (regardless of length etc.) excluded
GC_content_Filter2 = GC_content_Filter2[GC_content_Filter2$V1 %notin% emb_no$V1,]
dim(GC_content_Filter2)
hist(GC_content_Filter2$V2, breaks = 100)
ddTxi = subset(ddTxi, rownames(ddTxi) %in% GC_content_Filter2$V1)
dim(ddTxi)
#Filtering 3: removes genes with <10 counts across all samples
keep = rowSums(counts(ddTxi)) >= 10
hist(rowSums(counts(ddTxi)), breaks = 5000, xlim = c(0,10000))
summary(rowSums(counts(ddTxi)))
head(rowSums(counts(ddTxi)))
ddTxi2 = ddTxi[keep,]
GC_filter3 = GC_content_Filter2[GC_content_Filter2$V1 %in% rownames(ddTxi2),]
dim(GC_filter3)
dim(ddTxi2) #So this is the final set of genes to be included.
#Old filtering criteria...
old_emb50plus = emb_yes[emb_yes$V4 > 50,]
old_blat_latifolia_info = read.csv("~/RNA_Seq/merged_outfiles.psl_forlongesti.longesti", sep = "\t", header = FALSE)
old_blat_latifolia_info$match_length = old_blat_latifolia_info$V1+old_blat_latifolia_info$V2
old_blat_match_50plus = old_blat_latifolia_info[old_blat_latifolia_info$match_length > 50,]
old_blat_50plus_list = old_blat_match_50plus$V10
head(old_blat_50plus_list)
old_GC_content_Filter2 = GC_content[GC_content$V1 %in% old_blat_50plus_list | GC_content$V1 %in% old_emb50plus$V1,]
dim(old_GC_content_Filter2)
par(mfrow = c(1,1))
dev.new(width = 550, height = 550)
par(mar=c(8,8,8,8))
hist(GC_content$V2, breaks = 80, xlim = c(20,80), main = "Unfiltered", xlab = "GC content (%)", cex.axis = 1.5, cex.lab = 2, cex.main = 2.5)
hist(old_GC_content_Filter2$V2, breaks = 80, xlim = c(20,80), main = "Previous Filtering", xlab = "GC content (%)", cex.axis = 1.5, cex.lab = 2, cex.main = 2.5)
hist(GC_filter3$V2, breaks = 80, xlim = c(20,80), main = "New filtering", xlab = "GC content (%)", cex.axis = 1.5, cex.lab = 2, cex.main = 2.5) #So this still has a load of crap
#############
# 5B Transcriptome quality metrics
#############
#Length/ExN50
trinity_quality = read.table("~/SU_RedSamp_k25mincov2.fasta.stats", header = T)
head(trinity_quality)
plot(trinity_quality$Ex, trinity_quality$ExN50)
trinity_quality2 = read.table("~/SU_RedSamp_k25mincov2.fasta.trans_filtered.sub.stats", header = T)
head(trinity_quality2)
plot(trinity_quality2$Ex, trinity_quality2$ExN50)
#Multimapping transcripts?
sr30 = read.csv("~/sorted_merged_outfile_best.psl.expr.filt.re.sorted.not.SR30", header = FALSE, sep = "\t")
sr30 = na.omit(sr30)
dim(sr30)
hist(sr30$V2, xlab = "Percentage match length on different scaffold", main = "Multimapping contigs")
head(sr30)
dim(sr30)
plot(sr30$V2, sr30$V3)
sum(sr30$V2 > 0.95)
write.csv(rownames(ddTxi3), "background_ddTxi3_100622")
#Adding some more factors
ddTxi2$Cond = factor(ddTxi$Cond, levels = c("C", "Z"))
ddTxi2$Pop_Cond = factor(ddTxi$Pop_Cond, levels = c("BDZ", "BDC", "GRZ", "GRC", "PPZ", "PPC", "SAZ", "SAC"))
#Background fit - just use Pop_Cond
#design(ddTxi2) <- ~Cond+Ecotype+Geog
design(ddTxi2) <- ~Pop_Cond
#Fit the DESeq2 model
dds = DESeq(ddTxi2)
#Examine the overall patterns of variation
par(mfrow = c(1,2))
boxplot(log2(counts(dds, normalized = FALSE)))
boxplot(log2(counts(dds, normalized = TRUE)))
####################################################
#6 Counts, PCAs/heatmaps in control/zinc
####################################################
vst_all = vst(dds)
dds_C = dds[,dds$Cond == "C"]
vst_C = vst(dds_C) #I think this way of doing it is fine: you use the variance smoothing of the relevant conditions.
#For a small number of genes, better to use the whole set of genes.
#Maybe run this once done?
vst_C = vst_all[,vst_all$Cond %in% "C"]
dim(vst_C)
dds_Z = dds[,dds$Cond == "Z"]
vst_Z = vst(dds_Z)
dds_S = dds[,dds$Ecotype %in% "C"]
vst_S = vst(dds_S)
dds_T = dds[,dds$Ecotype %in% "M"]
vst_T = vst(dds_T)
#Heatmaps...
pdf(file = "~/RNA_Seq/NEE_resubmission_030722/Figures/SP2.pdf", width = 6.42, height = 5.84)
vst_C_plot = vst_C
vst_C_plot
#Note: obviously this is wrong but makes plotting easier
vst_C_plot$Pop_Cond = levels(as.factor(c("S2i", "S2ii", "S2iii", "T1i", "T1ii", "T1iii", "T2i", "T2ii", "T2iii", "S1i", "S1ii", "S1iii")))
vst_C_plot$Pop_Cond
heatmap_samples(vst_C_plot)
dev.off()
heatmap_samples(vst_all)