-
Notifications
You must be signed in to change notification settings - Fork 0
/
R_Pipeline_Choroid.R
200 lines (148 loc) · 6.18 KB
/
R_Pipeline_Choroid.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
library("BiocManager")
library("DESeq2")
library(reshape2)
library(ggplot2)
library("RColorBrewer") # Load a package giving more colors
library("pheatmap") # load a package for making heatmaps
#Read the data into Data frame
setwd("/storage/hpc/data/bourasm/CXCR5")
CXCR5<-read.table("C57_vs_CXCR5_Choroid_results.txt", header = TRUE, row.names = 1)
CXCR5Design <-data.frame(
row.names = colnames(CXCR5),
condition = c("untraited", "untraited", "untraited",
"traited", "traited", "traited"),
libType = c("paired-end","paired-end","paired-end",
"paired-end","paired-end","paired-end"))
pf=function (x,y){
pdf(x)
y
dev.off()
}
pf("results_Boxplot_CXCR5.pdf", boxplot(CXCR5) )
pf("results_Hist_CXCR5.pdf", hist(CXCR5[,1]) )# Plotting only the first sample (column 1)
pseudoCount = log2(CXCR5 + 1) # log-transform to make numbers on scale (+1 to avoid zeroes)
boxplot(pseudoCount)
pf("results_Boxplot_LogCount.pdf", boxplot(pseudoCount))
pf("results_Hist_LogCount.pdf", hist(pseudoCount[,1]))
pseudoCount = as.data.frame(pseudoCount)
df = melt(pseudoCount, variable.name = "Samples", value.name = "count") # reshape the matrix
df01 = data.frame(df, Condition = substr(df$Samples, 1, 6))
p<- ggplot(df01, aes(x = df$Samples, y = count, fill = Condition)) + geom_boxplot() + xlab("") +
ylab(expression(log[2](count + 1)))
pf("results_Boxplot_LogCount_02.pdf", print(p))
p<-ggplot(df01, aes(x = count, colour = Samples, fill = Samples)) + ylim(c(0, 0.17)) +
geom_density(alpha = 0.2, size = 1.25) + facet_wrap(~ Condition) +
theme(legend.position = "top") + xlab(expression(log[2](count + 1)))
pf("results_Boxplot_LogCount_03.pdf", print(p))
dds = DESeqDataSetFromMatrix(countData = CXCR5,
colData = CXCR5Design,
design = ~ condition) # we're testing for the different condidtions
dds
rld = rlogTransformation(dds)
#Need to create a more general function to plotting.
pdf("results_Boxplot_DESeqLog.pdf")
plot(log2( 1 + counts(dds)[ , 1:2] ),
pch=16, cex=0.3, main = "log2")
dev.off()
pdf("results_Boxplot_DESeqrld01.pdf")
plot(assay(rld)[ , 1:2], # The assay function returns the count values of rld in a matrix
pch=16, cex=0.3, main = "rlog")
dev.off()
distsRL <- dist(t(assay(rld))) # Calculate distances using transformed (and normalized) counts
mat <- as.matrix(distsRL) # convert to matrix
rownames(mat) <- colnames(mat) <- with(colData(dds), paste(condition)) # set rownames in the matrix
colnames(mat) = NULL # remove column names
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255) # set colors
pdf("pheatmap.pdf")
pheatmap(mat,
clustering_distance_rows=distsRL,
clustering_distance_cols=distsRL,
col=colors)
dev.off()
pdf("plotPCArld.pdf")
plotPCA(rld, intgroup=c("condition"))
dev.off()
pdf("plotpseudoCount_CX5_C2.pdf")
plot(pseudoCount[,2], pseudoCount[,5], pch = 20, xlab = "C57_C2", ylab = "CX5_C2") + # pch specifies the type of symbols. Filled dots in this case.
abline(0,1, col = "red") # make a diagonal line
dev.off()
x = pseudoCount[, 2] # extract fourth column
y = pseudoCount[, 5] # extract seventh column
M = x - y # M-values (differences between samples)
A = (x + y)/2 # A-values (averages)
df = data.frame(A, M)
p<-ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(aes(yintercept = 0), color = "blue3") + stat_smooth(se =
FALSE, method = "loess", color = "red3") +
xlab("Average gene count") +
ylab("Count difference") +
ggtitle("C57_C2 vs. CX5_C2")
pf("AverageGeneCount.pdf",p)
dds = estimateSizeFactors(dds)
sizeFactors(dds)
norm_counts = counts(dds, normalized = TRUE) # Extract the normalized counts
pseudoCount = log2(norm_counts + 1) # convert to log-scale for visualization
df = melt(pseudoCount) # transpose the matrix
df = data.frame(df, Condition = substr(df$Var2, 1, 4))
pdf("AverageGeneCountLog.pdf")
ggplot(df, aes(x = df$Var2, y = value, fill = Condition)) + geom_boxplot() + xlab("") +
ylab(expression(log[2](count + 1)))
dev.off()
dds = DESeq(dds)
res <- results(dds)
mcols(res, use.names=TRUE)
summary(res)
pdf("DESeq_dds.pdf")
plotMA(res, ylim=c(-7,7))
dev.off()
resShrink = lfcShrink(dds, coef=2)
pdf("DESeq_dds_ver02.pdf")
plotMA(resShrink, ylim=c(-5,5))
dev.off()
resSig <- subset(res, padj < 0.1)
resSig
head(resShrink[order(resShrink$log2FoldChange), ], 10)
attributes(resSig)[4]
View(head(resShrink[ order(resShrink$log2FoldChange), ], 10))
pdf("ENSMUSG00000107651.pdf")
plotCounts(dds, "ENSMUSG00000107651", "condition")
head(resShrink[ order(resShrink$log2FoldChange, decreasing=TRUE), ], 10)
pdf("ENSMUSG00000020866.pdf")
plotCounts(dds, "ENSMUSG00000020866", "condition")
head(res[order(res$padj),], 5) # order by padjusted and print the top 5
pdf("ENSMUSG00000059412.pdf")
plotCounts(dds, "ENSMUSG00000059412", "condition")
resLFC1 = results(dds, lfcThreshold = 1)
summary(resLFC1)
pdf("resLFC1.pdf")
plotMA(resLFC1, ylim=c(-7,7)) +
abline(h = 1, col = "blue") +
abline(h = -1, col = "blue")
resLFC1Srhunk = lfcShrink(dds, coef=2, res=resLFC1)
pdf("resLFC1Srhunk.pdf")
plotMA(resLFC1Srhunk, ylim=c(-5,5))+
abline(h = 1, col = "blue") +
abline(h = -1, col = "blue")
res.05 <- results(dds, alpha=.05)
table(res.05$padj < .05)
summary(res.05)
res.05Shrunk = lfcShrink(dds, coef = 2, res=res.05)
pdf("res.05Shrunk.pdf")
plotMA(res.05Shrunk, alpha = 0.05, ylim=c(-5,5)) +
abline(h = 1, col = "blue") +
abline(h = -1, col = "blue")
resUpReg = res[which(res$log2FoldChange < 0), ] # get the upregulated genes
head(resUpReg[order(resUpReg$padj),], 5) # order by padjusted and print the top 5
pdf("ENSMUSG00000072476.pdf")
plotCounts(dds, "ENSMUSG00000072476", "condition")
mat = assay(rld)[ head(order(res$padj),30), ] # select the top 30 genes with the lowest padj
mat = mat - rowMeans(mat) # Subtract the row means from each value
# Optional, but to make the plot nicer:
df = as.data.frame(colData(rld)[,c("condition")]) # Create a dataframe with a column of the conditions
colnames(df) = "condition" # Rename the column header
rownames(df) = colnames(mat) # add rownames
# and plot the actual heatmap
pdf("pheatmapDESeq_ver02.pdf")
pheatmap(mat, annotation_col=df)
dev.off()
assay(rld)["ENSMUSG00000072476",]