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run.R
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library(ggplot2)
library(gridExtra)
library(DESeq2)
library(ComplexHeatmap)
library(viridis)
library(circlize)
library(RColorBrewer)
library(factoextra)
library(Seurat)
library(readxl)
library(ggpubr)
library(factoextra)
library(readxl)
library( DESeq2 )
library( genefilter )
library( statmod )
library(ggplot2)
library(Rtsne)
setwd("/home/hiri/Desktop/PhD_Projects/2020-09-21-Christian")
dir.create("output")
## Importing the count table
temp <- list.files("./temp", pattern = "out.txt")
col <- gsub("_S.*", "", gsub("Aligned.out.txt", "", temp))
time <- gsub("_.*","",col)
nCell <- gsub("_.*","",gsub(".*d_","",gsub(".*h_","",col)))
filenames <- list.files(path = "./temp", full.names = TRUE)
datalist <- lapply(filenames, function(x){read.table(file = x, header = TRUE, row.names = 1, sep = "\t", stringsAsFactors = FALSE)[,6]})
unique_table <- sapply(datalist, cbind)
colnames(unique_table) <- col
rownames(unique_table) <- rownames(read.table("./temp/0h_1_A3_1Aligned.out.txt", row.names = 1, header = TRUE))
write.csv(unique_table, file = "raw_counts.csv")
colData <- data.frame(col, time = time, nCell = nCell)
#############################################
#############################################
############################################
## Adding the number of detected genes and library size
colData$n.gene <- colSums(unique_table > 5)
colData$lib.size <- colSums(unique_table)
write.csv(colData, file="colData.csv")
colData50 <- colData[colData$nCell == 50,]
colData1 <- colData[colData$nCell == 1,]
unique_table1 <- unique_table[,colData1$col]
unique_table50 <- unique_table[,colData50$col]
colData1$time <- factor(colData1$time,levels = c("0h", "4h", "12h", "24h", "7d"))
p1 <- ggplot(data = colData1, aes(x = time, y = n.gene, color = time)) +
geom_jitter() +
geom_violin(alpha = 0) +
theme_classic() +
ylab("Number of detected genes") +
xlab("") +
theme(legend.position = "none")
p2 <- ggplot(data = colData1, aes(x = time, y = lib.size, color = time)) +
geom_jitter() +
geom_violin(alpha = 0) +
theme_classic() +
ylab("library size") +
xlab("") +
theme(legend.position = "none") +
scale_y_continuous(trans='log10')
p <- grid.arrange(p1, p2, ncol=1)
ggsave("./output/gene_lib_1.eps", Fig1c, width = 6, height = 5)
ggplot(data = colData1, aes(x = lib.size, y=n.gene, color = time)) +
geom_jitter() +
theme_classic() +
theme() +
ylab("Number of detected genes") +
xlab("Library Size") +
geom_hline(yintercept = 200) +
geom_hline(yintercept = 1000) +
geom_vline(xintercept = 200000) +
geom_vline(xintercept = 2000000)
ggsave("./output/gene_lib_cor_1.eps", width = 5, height = 5)
p1 <- ggplot(data = colData50, aes(x = time, y = n.gene, color = time)) +
geom_jitter() +
geom_violin(alpha = 0) +
theme_classic() +
ylab("Number of detected genes") +
xlab("") +
theme(legend.position = "none")
p2 <- ggplot(data = colData50, aes(x = time, y = lib.size, color = time)) +
geom_jitter() +
geom_violin(alpha = 0) +
theme_classic() +
ylab("library size") +
xlab("") +
theme(legend.position = "none") +
scale_y_continuous(trans='log10')
Fig1c <- grid.arrange(p1, p2, ncol=1)
ggsave("./output/gene_lib_50.png", Fig1c, width = 6, height = 5)
ggplot(data = colData50, aes(x = lib.size, y=n.gene, color = time)) +
geom_jitter() +
theme_classic() +
theme() +
ylab("Number of detected genes") +
xlab("Library Size")
ggsave("./output/gene_lib_cor_50.png", width = 5, height = 5)
########################################
################# Single ###############
########################################
colData_filt <- colData1[colData1$n.gene > 200 , ]
colData_filt <- colData_filt[colData_filt$n.gene < 1000 , ]
colData_filt <- colData_filt[colData_filt$lib.size > 200000 , ]
colData_filt <- colData_filt[colData_filt$lib.size < 2000000 , ]
colData_filt <- colData_filt[-c(7),]
colData_filt$col <- as.character(colData_filt$col)
colData_filt$time <- as.character(colData_filt$time)
colData_filt$nCell <- as.character(colData_filt$nCell)
rownames(colData_filt) <- colData_filt$col
outlier <- c("0h_1_B5_1", "0h_1_G2", "0h_1_C5_1")
colData_filt <- colData_filt[!rownames(colData_filt) %in% outlier, ]
colData_write <- colData_filt
rownames(colData_write) <- colData_write$col
outlier <- c("0h_1_B5_1", "0h_1_G2", "0h_1_C5_1")
colData_write <- colData_write[!rownames(colData_write) %in% outlier, ]
write.csv(colData_write, file = "colData_w.csv")
single_table <- unique_table1[c(1:57,157:11807), as.character(colData_filt$col)]
pos <- grep("mVAT",rownames(single_table))
pos <- c(pos,11190)
single_table <- single_table[-pos, ]
single_table <- single_table[rowSums(single_table) > 20, ]
### Normalizing data
sfSingle <- estimateSizeFactorsForMatrix(single_table)
nSingle <- t(t(single_table) / sfSingle)
##########################################################################
## Selecting the top variable genes and log transformation
topVarGenesSingle <- head(order(rowVars(nSingle), decreasing = TRUE),1000)
nSingle_fin <- log(nSingle[topVarGenesSingle,] + 1)
### Performing PCA (single cell)
res.pca <- prcomp(t(nSingle_fin), scale = FALSE)
#colData1$time <- factor(colData1$time,levels = c("0h", "4h", "12h", "24h", "7d"))
ggplot(data.frame(PC1 = res.pca$x[,"PC1"], PC2 = res.pca$x[,"PC2"], time = factor(colData_filt$time,levels = c("0h", "4h", "12h", "24h", "7d"))), aes(x = PC1, y = PC2, color = time)) +
geom_jitter(size = 2) +
theme_classic() +
theme() +
geom_hline(yintercept=0, linetype="dashed") +
geom_vline(xintercept=0, linetype="dashed")
ggsave("./output/pca.eps", width = 6.5, height = 5)
write.csv(data.frame(PC1 = res.pca$x[,"PC1"], PC2 = res.pca$x[,"PC2"], time = colData_filt$time), file = "coordinate.csv")
fviz_eig(res.pca, addlabels = TRUE, ylim = c(0, 50))
ggsave("./output/scree_plot.png", width = 5, height = 5)
df <- data.frame(PC1 = res.pca$x[,"PC1"], PC2 = res.pca$x[,"PC2"], time = colData_filt$time)
df <- df[!df$time == "7d",]
ggplot(df, aes(x = PC1, y = PC2, color = time)) +
geom_jitter(size = 2) +
theme_classic() +
theme() +
geom_hline(yintercept=0, linetype="dashed") +
geom_vline(xintercept=0, linetype="dashed")
ggsave("./output/pca_7d_removed.png", width = 5, height = 5)
write.csv(df, file = "coordinate_d7_removed.csv")
### outliers === 0h_1_B5_1 0h_1_G2 0h_1_C5_1
#s <- rownames(df)
outlier <- c("0h_1_B5_1", "0h_1_G2", "0h_1_C5_1")
df <- df[!rownames(df) %in% outlier, ]
ggplot(df, aes(x = PC1, y = PC2, color = time)) +
geom_jitter(size = 2) +
theme_classic() +
theme() +
geom_hline(yintercept=0, linetype="dashed") +
geom_vline(xintercept=0, linetype="dashed")
ggsave("./output/pca_7d_outlier_removed.png", width = 5, height = 5)
write.csv(df, file = "coordinate_d7_outlier_removed.csv")
#############################################################
#################### DE 7d vs 24h ###########################
#############################################################
colData_pair <- colData_filt[colData_filt$time == "7d" | colData_filt$time == "24h",]
rownames(colData_pair) <- colData_pair$col
colData_pair <- colData_pair[!rownames(colData_pair) %in% outlier, ]
tab <- single_table[,rownames(colData_pair)]
dds <- DESeqDataSetFromMatrix(countData = tab,
colData = colData_pair,
design= ~time)
dds <- DESeq(dds)
resultsNames(dds)
res_7d_vs_24h <- results(dds, name = "time_7d_vs_24h")
res_7d_vs_24h <- data.frame(res_7d_vs_24h)
res_7d_vs_24h <- res_7d_vs_24h[complete.cases(res_7d_vs_24h),]
write.csv(res_7d_vs_24h, file = "res_7d_vs_24h.csv")
res_7d_vs_24h_hm <- res_7d_vs_24h[res_7d_vs_24h$padj < 0.01,]
positive <- res_7d_vs_24h_hm[res_7d_vs_24h_hm$log2FoldChange > 2,]
negative <- res_7d_vs_24h_hm[res_7d_vs_24h_hm$log2FoldChange < -2,]
hm_gene <- c(rownames(positive), rownames(negative))
norm <- nSingle[hm_gene, rownames(colData_pair)]
scaled <- t(apply(norm, 1, scale))
colnames(scaled) <- colnames(norm)
group <- factor(colData_DE$time)
ha <- HeatmapAnnotation(df = data.frame(group), col = list(group = c("0h" = "#619cff", "12h" = "#00BA38", "24h" = "#F8766D", "4h" = "#6a3d9a")))
pdf("./output/heatmap_res_7d_vs_24h.pdf", width = 14, height = 10)
p <- Heatmap(scaled, cluster_columns = FALSE, cluster_rows = FALSE, col = colorRamp2(seq(-2,2,0.1), viridis(41)), row_names_gp = gpar(fontsize = 2), show_column_names = TRUE)
print(p)
dev.off()
#############################################################
#################### DE 7d vs 0h ###########################
#############################################################
colData_pair <- colData_filt[colData_filt$time == "7d" | colData_filt$time == "0h",]
rownames(colData_pair) <- colData_pair$col
colData_pair <- colData_pair[!rownames(colData_pair) %in% outlier, ]
tab <- single_table[,rownames(colData_pair)]
dds <- DESeqDataSetFromMatrix(countData = tab,
colData = colData_pair,
design= ~time)
dds <- DESeq(dds)
resultsNames(dds)
res_7d_vs_0h <- results(dds, name = "time_7d_vs_0h")
res_7d_vs_0h <- data.frame(res_7d_vs_0h)
res_7d_vs_0h <- res_7d_vs_0h[complete.cases(res_7d_vs_0h),]
write.csv(res_7d_vs_0h, file = "res_7d_vs_0h.csv")
res_7d_vs_0h_hm <- res_7d_vs_0h[res_7d_vs_0h$padj < 0.01,]
positive <- res_7d_vs_0h_hm[res_7d_vs_0h_hm$log2FoldChange > 2,]
negative <- res_7d_vs_0h_hm[res_7d_vs_0h_hm$log2FoldChange < -2,]
hm_gene <- c(rownames(positive), rownames(negative))
norm <- nSingle[hm_gene, rownames(colData_pair)]
scaled <- t(apply(norm, 1, scale))
colnames(scaled) <- colnames(norm)
group <- factor(colData_DE$time)
ha <- HeatmapAnnotation(df = data.frame(group), col = list(group = c("0h" = "#619cff", "12h" = "#00BA38", "0h" = "#F8766D", "4h" = "#6a3d9a")))
pdf("./output/heatmap_res_7d_vs_0h.pdf", width = 14, height = 10)
p <- Heatmap(scaled, cluster_columns = FALSE, cluster_rows = FALSE, col = colorRamp2(seq(-2,2,0.1), viridis(41)), row_names_gp = gpar(fontsize = 2), show_column_names = TRUE)
print(p)
dev.off()
#############################################################
#############################################################
############################################################
## performing DE analysis
colData_DE <- colData_filt#[!colData_filt$time == "7d",]
rownames(colData_DE) <- colData_DE$col
colData_DE <- colData_DE[!rownames(colData_DE) %in% outlier, ]
h0 <- factor(gsub("7d","rest",gsub("4h","rest",gsub("12h","rest",gsub("24h", "rest", gsub("0h", "aim", colData_DE$time))))))
h4 <- factor(gsub("7d","rest",gsub("4h","aim",gsub("12h","rest",gsub("24h", "rest", gsub("0h", "rest", colData_DE$time))))))
h12 <- factor(gsub("7d","rest",gsub("4h","rest",gsub("12h","aim",gsub("24h", "rest", gsub("0h", "rest", colData_DE$time))))))
h24 <- factor(gsub("7d","rest",gsub("4h","rest",gsub("12h","rest",gsub("24h", "aim", gsub("0h", "rest", colData_DE$time))))))
d7 <- factor(gsub("7d","aim",gsub("4h","rest",gsub("12h","rest",gsub("24h", "rest", gsub("0h", "rest", colData_DE$time))))))
colData_DE <- data.frame(colData_DE, h0, h4, h12, h24, d7)
# cts <- single_table[,as.character(colData_DE$col) ]
#cts <- cts[rowSums(cts) > 0, ]
#cts <- cts[rowSums(cts > 0) > 5, ]
# col_data <- data.frame(colData_DE, condition = factor(condition))
# rownames(col_data) <- col_data$col
# dds <- DESeqDataSetFromMatrix(countData = cts,
# colData = col_data,
# design= ~condition)
# resultsNames(dds)
#h4 <- as.character(colData_DE[colData_DE$time == "4h", ]$col)
#h0 <- as.character(colData_DE[colData_DE$time == "0h", ]$col)
#h12 <- as.character(colData_DE[colData_DE$time == "12h", ]$col)
#h24 <- as.character(colData_DE[colData_DE$time == "24h", ]$col)
#ht_order <- c(h0, h4, h12, h24)
single_table <- single_table[,rownames(colData_DE)]
#condition <- factor(c(rep("early", 71), rep("late", 49)))
condition <- factor(colData_DE$time)
library(scde)
names(condition) <- as.character(colnames(single_table))
sg <- condition
cd <- single_table
o.ifm <- scde.error.models(counts = cd, groups = sg, n.cores = 8, threshold.segmentation = TRUE, save.crossfit.plots = FALSE, save.model.plots = FALSE, verbose = 1)
o.prior <- scde.expression.prior(models = o.ifm, counts = cd, length.out = 400, show.plot = FALSE)
### 0h
# group <- factor(colData_DE$h0)
# names(group) <- rownames(o.ifm)
# ediff <- scde.expression.difference(o.ifm, cd, o.prior, groups = group, n.randomizations = 100, n.cores = 8, verbose = 1)
# de_0h <- ediff[order(ediff$Z, decreasing = TRUE), ]
# write.csv(de_0h, file = "de_0h.csv")
### 4h
# group <- factor(colData_DE$h4)
# names(group) <- rownames(o.ifm)
# ediff <- scde.expression.difference(o.ifm, cd, o.prior, groups = group, n.randomizations = 100, n.cores = 8, verbose = 1)
# de_4h <- ediff[order(ediff$Z, decreasing = TRUE), ]
# write.csv(de_4h, file = "de_4h.csv")
### 12h
# group <- factor(colData_DE$h12)
# names(group) <- rownames(o.ifm)
# ediff <- scde.expression.difference(o.ifm, cd, o.prior, groups = group, n.randomizations = 100, n.cores = 8, verbose = 1)
# de_12h <- ediff[order(ediff$Z, decreasing = TRUE), ]
# write.csv(de_12h, file = "de_12h.csv")
### 24h
# group <- factor(colData_DE$h24)
# names(group) <- rownames(o.ifm)
# ediff <- scde.expression.difference(o.ifm, cd, o.prior, groups = group, n.randomizations = 100, n.cores = 8, verbose = 1)
# de_24h <- ediff[order(ediff$Z, decreasing = TRUE), ]
# write.csv(de_24h, file = "de_24h.csv")
### 7d
#group <- factor(colData_DE$d7)
#names(group) <- rownames(o.ifm)
#ediff <- scde.expression.difference(o.ifm, cd, o.prior, groups = group, n.randomizations = 100, n.cores = 8, verbose = 1)
#de_d7 <- ediff[order(ediff$Z, decreasing = TRUE), ]
#write.csv(de_d7, file = "de_d7.csv")
# de_ht_0h <- de_0h[de_0h$cZ > 1.65, ]
# de_ht_4h <- de_4h[de_4h$cZ > 1.65, ]
# de_ht_12h <- de_12h[de_12h$cZ > 1.65, ]
# de_ht_24h <- de_24h[de_24h$cZ > 1.65, ]
#de_ht_d7 <- de_d7[de_d7$cZ > 1.96, ]
# de_sig <- rbind(de_ht_0h, de_ht_4h, de_ht_12h, de_ht_24h)
# de_sig <- rownames(de_sig)
# de_sig <- unique(de_sig)
#de_a <- de[de$cZ > 1.96, ]
#de_b <- de[de$cZ < -1.96, ]
#de_sig <- rbind(de_a, de_b)
#de_sig <- rownames(de_sig)
### creating the heatmap
h4 <- as.character(colData_DE[colData_DE$time == "4h", ]$col)
h0 <- as.character(colData_DE[colData_DE$time == "0h", ]$col)
h12 <- as.character(colData_DE[colData_DE$time == "12h", ]$col)
h24 <- as.character(colData_DE[colData_DE$time == "24h", ]$col)
d7 <- as.character(colData_DE[colData_DE$time == "7d", ]$col)
ht_order <- c(h0, h4, h12, h24, d7)
# norm <- nSingle[de_sig, ht_order]
# scaled <- t(apply(norm, 1, scale))
# colnames(scaled) <- colnames(norm)
#group <- factor(colData_DE$time)
#ha <- HeatmapAnnotation(df = data.frame(group), col = list(group = c("0h" = "#619cff", "12h" = "#00BA38", "24h" = "#F8766D", "4h" = "#6a3d9a")))
# pdf("./output/heatmap_comparision.pdf", width = 14, height = 7)
# p <- Heatmap(scaled, cluster_columns = FALSE, cluster_rows = FALSE, col = colorRamp2(seq(-2,2,0.1), viridis(41)), row_names_gp = gpar(fontsize = 4), show_column_names = TRUE)
# print(p)
# dev.off()
#norm <- nSingle[hm_gene, ht_order]
# scaled <- t(apply(norm, 1, scale))
# colnames(scaled) <- colnames(norm)
#group <- factor(colData_DE$time)
#ha <- HeatmapAnnotation(df = data.frame(group), col = list(group = c("0h" = "#619cff", "12h" = "#00BA38", "24h" = "#F8766D", "4h" = "#6a3d9a")))
# pdf("./output/heatmap_comparision.pdf", width = 14, height = 7)
# p <- Heatmap(scaled, cluster_columns = FALSE, cluster_rows = TRUE, col = colorRamp2(seq(-2,2,0.1), viridis(41)), row_names_gp = gpar(fontsize = 4), show_column_names = TRUE)
# print(p)
# dev.off()
##################################################################
######################## Performing DEseq2 #######################
##################################################################
condition <- colData_DE
##h0
dds <- DESeqDataSetFromMatrix(countData = single_table,
colData = condition,
design= ~h0)
dds <- DESeq(dds)
resultsNames(dds)
res_h0 <- results(dds, name = "h0_rest_vs_aim")
res_h0 <- data.frame(res_h0)
res_h0 <- res_h0[complete.cases(res_h0),]
write.csv(res_h0, file = "res_h0.csv")
#### finding genes that are low at the beginning but low at the end
### creating the violinplot
vln_norm <- nSingle[,rownames(colData_DE)]
de_gene <- res_h0[res_h0$padj < 0.01,]
pos <- de_gene[de_gene$log2FoldChange > 2,]
## violinplot
colData_DE$gene <- log10(vln_norm["Tb927.1.290",] + 1)
ggplot(colData_DE, aes(x = time, y = gene, color = time)) +
geom_violin() +
geom_jitter() +
theme_classic()
##h4
dds <- DESeqDataSetFromMatrix(countData = single_table,
colData = condition,
design= ~h4)
dds <- DESeq(dds)
resultsNames(dds)
res_h4 <- results(dds, name = "h4_rest_vs_aim")
res_h4 <- data.frame(res_h4)
res_h4 <- res_h4[complete.cases(res_h4),]
write.csv(res_h4, file = "res_h4.csv")
##h12
dds <- DESeqDataSetFromMatrix(countData = single_table,
colData = condition,
design= ~h12)
dds <- DESeq(dds)
resultsNames(dds)
res_h12 <- results(dds, name = "h12_rest_vs_aim")
res_h12 <- data.frame(res_h12)
res_h12 <- res_h12[complete.cases(res_h12),]
write.csv(res_h12, file = "res_h12.csv")
##h24
dds <- DESeqDataSetFromMatrix(countData = single_table,
colData = condition,
design= ~h24)
dds <- DESeq(dds)
resultsNames(dds)
res_h24 <- results(dds, name = "h24_rest_vs_aim")
res_h24 <- data.frame(res_h24)
res_h24 <- res_h24[complete.cases(res_h24),]
write.csv(res_h24, file = "res_h24.csv")
##d7
dds <- DESeqDataSetFromMatrix(countData = single_table,
colData = condition,
design= ~d7)
dds <- DESeq(dds)
resultsNames(dds)
res_d7 <- results(dds, name = "d7_rest_vs_aim")
res_d7 <- data.frame(res_d7)
res_d7 <- res_d7[complete.cases(res_d7),]
write.csv(res_d7, file = "res_d7.csv")
res_h0_hm <- res_h0[res_h0$padj < 0.01,]
res_h0_hm <- res_h0_hm[res_h0_hm$log2FoldChange < 2 | res_h0_hm$log2FoldChange > 2,]
res_h4_hm <- res_h4[res_h4$padj < 0.01,]
res_h4_hm <- res_h4_hm[res_h4_hm$log2FoldChange < 2 | res_h4_hm$log2FoldChange > 2,]
res_h12_hm <- res_h12[res_h12$padj < 0.01,]
res_h12_hm <- res_h12_hm[res_h12_hm$log2FoldChange < 2 | res_h12_hm$log2FoldChange > 2,]
res_h24_hm <- res_h24[res_h24$padj < 0.01,]
res_h24_hm <- res_h24_hm[res_h24_hm$log2FoldChange < 2 | res_h24_hm$log2FoldChange > 2,]
res_d7_hm <- res_d7[res_d7$padj < 0.01,]
res_d7_hm <- res_d7_hm[res_d7_hm$log2FoldChange < 2 | res_d7_hm$log2FoldChange > 2,]
gene_selected <- read.csv(file = "All RBPs for Heatmap.txt", header = FALSE)
hm_gene <- as.character(gene_selected$V1)
#hm_gene <- unique(c(rownames(res_h0_hm), rownames(res_h4_hm), rownames(res_h12_hm), rownames(res_h24_hm), rownames(res_d7_hm)))
norm <- nSingle[hm_gene, ht_order]
scaled <- t(apply(norm, 1, scale))
colnames(scaled) <- colnames(norm)
group <- factor(colData_DE$time)
ha <- HeatmapAnnotation(df = data.frame(group), col = list(group = c("0h" = "#619cff", "12h" = "#00BA38", "24h" = "#F8766D", "4h" = "#6a3d9a")))
pdf("./output/heatmap_comparision5.pdf", width = 14, height = 10)
p <- Heatmap(scaled, cluster_columns = TRUE, cluster_rows = TRUE, col = colorRamp2(seq(-2,2,0.1), viridis(41)), row_names_gp = gpar(fontsize = 2), show_column_names = TRUE)
print(p)
dev.off()
########################################################
########################################################
res_h0_hm <- res_h0[res_h0$padj < 0.01,]
res_h0_hm <- res_h0_hm[res_h0_hm$log2FoldChange < 2,]
res_h4_hm <- res_h4[res_h4$padj < 0.01,]
res_h4_hm <- res_h4_hm[res_h4_hm$log2FoldChange < 2,]
res_h12_hm <- res_h12[res_h12$padj < 0.01,]
res_h12_hm <- res_h12_hm[res_h12_hm$log2FoldChange < 2,]
res_h24_hm <- res_h24[res_h24$padj < 0.01,]
res_h24_hm <- res_h24_hm[res_h24_hm$log2FoldChange < 2,]
res_d7_hm <- res_d7[res_d7$padj < 0.01,]
res_d7_hm <- res_d7_hm[res_d7_hm$log2FoldChange < 2,]
hm_gene <- unique(c(rownames(res_h0_hm), rownames(res_h4_hm), rownames(res_h12_hm), rownames(res_h24_hm), rownames(res_d7_hm)))
norm <- nSingle[hm_gene, ht_order]
scaled <- t(apply(norm, 1, scale))
colnames(scaled) <- colnames(norm)
#group <- factor(colData_DE$time)
#ha <- HeatmapAnnotation(df = data.frame(group), col = list(group = c("0h" = "#619cff", "12h" = "#00BA38", "24h" = "#F8766D", "4h" = "#6a3d9a")))
pdf("./output/heatmap_comparision2.pdf", width = 14, height = 10)
p <- Heatmap(scaled, cluster_columns = FALSE, cluster_rows = FALSE, col = colorRamp2(seq(-2,2,0.1), viridis(41)), row_names_gp = gpar(fontsize = 2), show_column_names = TRUE)
print(p)
################ Performing DE analysis ... early vs late ###################
#colData_DE <- colData_DE[!colData_DE$time == "7d",]
#cd <- single_table[,as.character(colData_DE$col) ]
pair <- factor(gsub("4h","early",gsub("12h","late",gsub("24h", "late", gsub("0h", "early", colData_DE$time)))))
colData_DE <- cbind(colData_DE,pair)
group <- factor(colData_DE$pair)
names(group) <- rownames(o.ifm)
ediff <- scde.expression.difference(o.ifm, cd, o.prior, groups = group, n.randomizations = 100, n.cores = 8, verbose = 1)
de_early <- ediff[order(ediff$Z, decreasing = TRUE), ]
write.csv(de_early, file = "de_early_vs_late.csv")
de_ht_pos <- de_early[de_early$cZ > 1.96, ]
de_ht_neg <- de_early[de_early$cZ < -1.96, ]
de_sig <- rbind(de_ht_pos, de_ht_neg)
de_sig <- rownames(de_sig)
de_sig <- unique(de_sig)
h4 <- as.character(colData_DE[colData_DE$time == "4h", ]$col)
h0 <- as.character(colData_DE[colData_DE$time == "0h", ]$col)
h12 <- as.character(colData_DE[colData_DE$time == "12h", ]$col)
h24 <- as.character(colData_DE[colData_DE$time == "24h", ]$col)
d7 <- as.character(colData_DE[colData_DE$time == "7d", ]$col)
ht_order <- c(h0, h4, h12, h24)
norm <- nSingle[de_sig, ht_order]
scaled <- t(apply(norm, 1, scale))
colnames(scaled) <- colnames(norm)
#group <- factor(colData_DE$time)
#ha <- HeatmapAnnotation(df = data.frame(group), col = list(group = c("0h" = "#619cff", "12h" = "#00BA38", "24h" = "#F8766D", "4h" = "#6a3d9a")))
pdf("./output/heatmap_early_vs_late.pdf", width = 14, height = 7)
p <- Heatmap(scaled, cluster_columns = FALSE, cluster_rows = FALSE, col = colorRamp2(seq(-2,2,0.1), viridis(41)), row_names_gp = gpar(fontsize = 4), show_column_names = TRUE)
print(p)
dev.off()
############################################################################
#################### DE early vs late DESeq ################################
######################################################################
pair <- factor(gsub("4h","early",gsub("12h","late",gsub("24h", "late", gsub("0h", "early", colData_DE$time)))))
colData_DE <- cbind(colData_DE,pair)
condition <- colData_DE
dds <- DESeqDataSetFromMatrix(countData = single_table,
colData = condition,
design= ~pair)
dds <- DESeq(dds)
resultsNames(dds)
res_pair <- results(dds, name = "pair_late_vs_early")
res_pair <- data.frame(res_pair)
res_pair <- res_pair[complete.cases(res_pair),]
write.csv(res_pair, file = "res_early_vs_late.csv")
res_pair_hm <- res_pair[res_pair$padj < 0.01,]
pos <- res_pair_hm[res_pair_hm$log2FoldChange > 5,]
neg <- res_pair_hm[res_pair_hm$log2FoldChange < 5,]
hm_gene <- c(rownames(pos), rownames(neg))
norm <- nSingle[hm_gene, ht_order]
scaled <- t(apply(norm, 1, scale))
colnames(scaled) <- colnames(norm)
#group <- factor(colData_DE$time)
#ha <- HeatmapAnnotation(df = data.frame(group), col = list(group = c("0h" = "#619cff", "12h" = "#00BA38", "24h" = "#F8766D", "4h" = "#6a3d9a")))
pdf("./output/heatmap_early_vs_late_2.pdf", width = 14, height = 7)
p <- Heatmap(scaled, cluster_columns = FALSE, cluster_rows = FALSE, col = colorRamp2(seq(-2,2,0.1), viridis(41)), row_names_gp = gpar(fontsize = 2), show_column_names = TRUE)
print(p)
dev.off()
##########################################################################
############################# Pairwise comparision ########################
##############################################################################
### 7d vs 0h $ 7d vs 24h
#############################################################################
### PCA on 50 cells
colData_filt <- colData[colData$nCell == "50", ]
single_table <- unique_table50[c(1:57,157:11807), as.character(colData_filt$col)]
pos <- grep("mVAT",rownames(single_table))
pos <- c(pos,11190)
single_table <- single_table[-pos, ]
single_table <- single_table[rowSums(single_table) > 20, ]
### Normalizing data
sfSingle <- estimateSizeFactorsForMatrix(single_table)
nSingle <- t(t(single_table) / sfSingle)
##########################################################################
## Selecting the top variable genes and log transformation
topVarGenesSingle <- head(order(rowVars(nSingle), decreasing = TRUE),1000)
nSingle_fin <- log(nSingle[topVarGenesSingle,] + 1)
### Performing PCA (single cell)
res.pca <- prcomp(t(nSingle_fin), scale = FALSE)
ggplot(data.frame(PC1 = res.pca$x[,"PC1"], PC2 = res.pca$x[,"PC2"], time = colData_filt$time), aes(x = PC1, y = PC2, color = time)) +
geom_jitter(size = 2) +
theme_classic() +
theme() +
geom_hline(yintercept=0, linetype="dashed") +
geom_vline(xintercept=0, linetype="dashed")
ggsave("./output/pca50.png", width = 5, height = 5)
### running DE analysis
colData_DE <- colData_filt
condition <- factor(c("one", "one", "one", "one", "one", "one", "two", "two", "two", "two", "two", "two", "two", "two", "two", "two", "two"))
cts <- as.matrix(single_table[,as.character(colData_DE$col) ])
#cts <- cts[rowSums(cts) > 0, ]
#cts <- cts[rowSums(cts > 0) > 5, ]
col_data <- data.frame(colData_DE, condition = factor(condition))
rownames(col_data) <- col_data$col
dds <- DESeqDataSetFromMatrix(countData = cts,
colData = col_data,
design= ~condition)
resultsNames(dds)