-
Notifications
You must be signed in to change notification settings - Fork 0
/
DESeq2 Perrin VCM EGR2.R
256 lines (194 loc) · 9.16 KB
/
DESeq2 Perrin VCM EGR2.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
## ---- echo=FALSE----------------------------------------------------------------
colFmt = function(x,color){
outputFormat = knitr::opts_knit$get("rmarkdown.pandoc.to")
if(outputFormat == 'latex')
paste("\\textcolor{",color,"}{",x,"}",sep="")
else if(outputFormat == 'html')
paste("<font color='",color,"'>",x,"</font>",sep="")
else
x
}
## ----setup, include=FALSE-------------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)
options(width = 200)
## ----Libraries, echo=TRUE, message=FALSE, results=FALSE-------------------------
# BiocManager::install("DESeq2")
# BiocManager::install("tximport")
# BiocManager::install("tximportData")
# BiocManager::install("sparseMatrixStats")
# BiocManager::install("DEGreport")
# BiocManager::install("apeglm")
# BiocManager::install("biomaRt")
# BiocManager::install("GO.db")
# BiocManager::install("GOstats")
# BiocManager::install("compEpiTools")
# BiocManager::install("org.Hs.eg.db")
# BiocManager::install("pathview")
# BiocManager::install("vsn")
# Validate Bioconductor:
# BiocManager::valid()
library(ggrepel)
library(cowplot)
library(ggplotify)
library(gridExtra)
library(RColorBrewer)
library(pheatmap)
library(pathview)
library(KEGGREST)
library(org.Hs.eg.db)
library(compEpiTools)
library(GOplot)
library(GO.db)
library(GOstats)
library(biomaRt)
library(ggpubr)
library(ggplot2)
library(DESeq2)
library(tximport)
library(tximportData)
library(sparseMatrixStats)
library(DEGreport)
library(AnnotationDbi)
library(gprofiler2)
library(tidyverse)
## ---- message=FALSE, cache = TRUE-----------------------------------------------
salmon_merged.gene_counts <- readRDS("RNAseq VCM Perrin Colas/salmon.merged.gene_counts.rds")
head(salmon_merged.gene_counts) # only first four experiments shown
## ---- message=FALSE-------------------------------------------------------------
raw_counts <- assays(salmon_merged.gene_counts)$counts
head(raw_counts)[,1:6]
## ---- message=FALSE-------------------------------------------------------------
subset_cols <- names(raw_counts)[c(1:3, 4:6)]
subset_data <- raw_counts[,subset_cols]
head(subset_data)
## -------------------------------------------------------------------------------
subset_data <- round(subset_data)
head(subset_data)
## ---- message=FALSE-------------------------------------------------------------
ExpDesign <- data.frame(row.names=colnames(subset_data), condition=c("ctrl_VCM", "ctrl_VCM", "ctrl_VCM", "EGR2_VCM", "EGR2_VCM", "EGR2_VCM"), libType = c("paired-end","paired-end","paired-end","paired-end","paired-end","paired-end"))
ExpDesign
## ---- collapse = TRUE-----------------------------------------------------------
dds <- DESeq2::DESeqDataSetFromMatrix(countData = subset_data, colData = ExpDesign, design = ~ condition)
dds
## ---- collapse = TRUE-----------------------------------------------------------
dds <- DESeq2::DESeq(dds)
## ---- echo=FALSE, fig.cap="Figure 1. Gene counts before and after normalization."----
log.norm.counts <- log2(counts(dds, normalized=TRUE) + 1)
ggplot(data = data.frame(log2(counts(dds))[,1:2]), aes(x = CTRL_R1, y = CTRL_R2)) +
geom_point(cex = 0.1, color = "red") +
geom_abline(intercept = 0, slope = 1, color = "green", cex = 0.5) +
geom_point(data = data.frame(log.norm.counts[,1:2]), aes(x = CTRL_R1, y = CTRL_R2), color = "blue", cex = 0.1, alpha = 0.2)
## ---- fig.cap="Figure 2. Dispersion Estimates."---------------------------------
plotDispEsts(dds)
## ----PCA------------------------------------------------------------------------
vsd <- vst(dds, blind=FALSE)
pcaData <- plotPCA(vsd, intgroup=c("condition", "libType"), returnData=TRUE)
percentVar <- round(100 * attr(pcaData, "percentVar"))
plot1 <- ggplot(pcaData, aes(PC1, PC2, color=condition, shape=libType)) +
geom_point(size=3) +
xlab(paste0("PC1: ",percentVar[1],"% variance")) +
ylab(paste0("PC2: ",percentVar[2],"% variance")) +
coord_fixed()
## ----Heatmap Clustering, results='hold'-----------------------------------------
sampleDists <- dist(t(assay(vsd)))
sampleDistMatrix <- as.matrix(sampleDists)
rownames(sampleDistMatrix) <- paste(vsd$condition, vsd$type, sep="-")
colnames(sampleDistMatrix) <- NULL
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
plot2 <- pheatmap(sampleDistMatrix,
clustering_distance_rows=sampleDists,
clustering_distance_cols=sampleDists,
col=colors, silent = TRUE)
## ----PCA and Heatmap Figure, echo=FALSE, fig.cap="Figure 4. PCA and Heatmap Cluster"----
plot_grid(plot1, as.ggplot(plot2), align = "h", nrow = 1, rel_widths = c(1.5,1))
## -------------------------------------------------------------------------------
#### Run these lines if the Rdata file is not present
# ensembl <- useMart('ensembl', dataset = 'hsapiens_gene_ensembl')
# geneset_ensembl <- getBM(attributes = c('entrezgene_id', 'external_gene_name', 'ensembl_gene_id'), filters = 'ensembl_gene_id', values = row.names(res), mart = ensembl)
# saveRDS(geneset_ensembl, file = "geneset_ensembl.Rdata")
geneset_ensembl <- readRDS(file = "geneset_ensembl.Rdata")
## ---- collapse = TRUE-----------------------------------------------------------
res <- DESeq2::results(dds)
## ---- collapse=TRUE-------------------------------------------------------------
res1 = results(dds, contrast=c("condition","ctrl_VCM", "EGR2_VCM"))
## ---- collapse = TRUE-----------------------------------------------------------
ddsLRT <- DESeq(dds, test="LRT", reduced= ~ 1)
resLRT <- results(ddsLRT)
# Add Entrez/Symbol annotation to the results
counts_table <- as.data.frame(counts(dds))
counts_table$Symbol <- geneset_ensembl$external_gene_name[match(row.names(counts_table), geneset_ensembl$ensembl_gene_id)]
res$Symbol <- geneset_ensembl$external_gene_name[match(row.names(res), geneset_ensembl$ensembl_gene_id)]
res$entrez <- geneset_ensembl$entrezgene_id[match(row.names(res), geneset_ensembl$ensembl_gene_id)]
resLRT$Symbol <- geneset_ensembl$external_gene_name[match(row.names(resLRT), geneset_ensembl$ensembl_gene_id)]
resLRT$entrez <- geneset_ensembl$entrezgene_id[match(row.names(resLRT), geneset_ensembl$ensembl_gene_id)]
head(resLRT)
## ---- echo = TRUE, message=FALSE, cache = TRUE----------------------------------
res1LFC <- lfcShrink(dds, coef="condition_EGR2_VCM_vs_ctrl_VCM", type="apeglm")
dds$condition <- relevel(dds$condition, ref = "EGR2_VCM")
dds <- nbinomWaldTest(dds) # <- to updated conditions
## ---- include=FALSE-------------------------------------------------------------
ggma_plot_res <- function(x)
{
z <- x@elementMetadata[4,2]
heading_plot <- substr(z, gregexpr("value: condition", z)[[1]][1]+17, nchar(z))
ggmaplot(x, main = heading_plot,
fdr = 0.05, fc = 2, size = 1, alpha = 0.5,
palette = c("#B31B21", "#1465AC", "gray80"),
genenames = as.vector(x$name),
legend = "top", top = 0,
font.label = c("bold", 11),
font.legend = "bold",
font.main = "bold",
ggtheme = ggplot2::theme_minimal()) + theme(plot.title = element_text(size = rel(1.5), hjust = 0.5, face = "bold"), text = element_text(size=12))
}
volcano_plot <- function(x)
{
z <- x@elementMetadata[4,2]
heading_plot <- substr(z, gregexpr("value: condition", z)[[1]][1]+17, nchar(z))
x$symbol <- geneset_ensembl$external_gene_name[match(row.names(x), geneset_ensembl$ensembl_gene_id)]
res_tableOE_tb <- as_tibble(x, rownames = "gene") %>%
mutate(threshold_OE = padj < 0.05 & abs(log2FoldChange) >= 1) # 1.7-fold change
res_tableOE_tb <- res_tableOE_tb %>% arrange(padj) %>% mutate(genelabels = "")
res_tableOE_tb$genelabels[1:10] <- res_tableOE_tb$symbol[1:10]
ggplot(res_tableOE_tb, aes(x = log2FoldChange, y = -log10(padj))) +
scale_color_manual(values=c("#25B025", "#9D1856")) +
geom_point(aes(colour = threshold_OE), size = 0.5) +
geom_text_repel(aes(label = genelabels), point.size = NA) +
ggtitle(heading_plot) +
xlab("log2 fold change") +
ylab("-log10 adjusted p-value") +
theme_bw() +
theme(legend.position = "none",
plot.title = element_text(size = rel(1.5), hjust = 0.5, face = "bold"),
axis.title = element_text(size = rel(1.25)))
}
## ---- echo=FALSE, warning=FALSE, fig.asp = .62, figures-side1, fig.show="hold", out.width="50%", fig.cap="Figure 4A. Log2-fold changes before (left) and after log fold change shrinkage (right)."----
plot1 <- ggma_plot_res(res1)
plot2 <- volcano_plot(res1)
plot3 <- ggma_plot_res(res1LFC)
plot4 <- volcano_plot(res1LFC)
par(mfrow=c(1,4), mai = c(1, 0.1, 0.1, 0.1))
plot1
plot2
plot3
plot4
## ---- echo=FALSE, warning=FALSE, figures-side2, fig.show="hold", out.width="50%", fig.cap="Figure 4B. Log2-fold changes before (left) and after log fold change shrinkage (right)."----
plot1 <- ggma_plot_res(res2)
plot2 <- volcano_plot(res2)
plot3 <- ggma_plot_res(res2LFC)
plot4 <- volcano_plot(res2LFC)
par(mar = c(4, 4, 4, 4))
plot1
plot2
plot3
plot4
## ---- echo=FALSE, warning=FALSE, figures-side3, fig.show="hold", out.width="50%", fig.cap="Figure 4C. Log2-fold changes before (left) and after log fold change shrinkage (right)."----
plot1 <- ggma_plot_res(res3)
plot2 <- volcano_plot(res3)
plot3 <- ggma_plot_res(res3LFC)
plot4 <- volcano_plot(res3LFC)
par(mar = c(4, 4, 4, 4))
plot1
plot2
plot3
plot4