-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy path1_DEGs.R
executable file
·209 lines (177 loc) · 8.02 KB
/
1_DEGs.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
#####################################################################
#gene expression atlas of tomato under heat stress #
####################################################################
# required packages
load.lib<-c("dplyr", "openxlsx", "ggplot2", "BiocManager", "UpSetR", "openxlsx", "Cairo")
install.lib<-load.lib[!load.lib %in% installed.packages()]
for(lib in install.lib) install.packages(lib,dependencies=TRUE)
sapply(load.lib,require,character=TRUE)
BiocManager::install(c("edgeR", "ComplexHeatmap", "topGO"))
bioc_pckg = c("edgeR", "ComplexHeatmap", "topGO")
lapply(bioc_pckg, require, character.only = TRUE)
rm(list=ls())
set.seed(123)
master.table = read.csv("inputs/master-table.csv")
master.table = master.table %>%
filter(species == "Solanum lycopersicum")
combined.abundance = read.csv("inputs/combined_abundance.tsv", sep = "\t")
row.names(combined.abundance) = combined.abundance$target_id
sample.group = "e1-1"
sras.in.group = master.table[master.table$sample.group == sample.group, ]$sra_run_id
control.group = unique(master.table[master.table$sample.group == sample.group, ]$respective_control)
sras.in.control = master.table[master.table$sample.group == control.group, ]$sra_run_id
selected.abundances = combined.abundance[,c(paste0(sras.in.control, "_est_counts"),
paste0(sras.in.group, "_est_counts"))]
edger.group = factor(c(rep(1, length(sras.in.control)),
rep(2, length(sras.in.group))))
y <- DGEList(counts = selected.abundances, group = edger.group)
y <- calcNormFactors(y)
y <- estimateCommonDisp(y, verbose = TRUE)
y <- estimateTagwiseDisp(y)
et <- exactTest(y)
res <- topTags(et, n = Inf)
res <- as.data.frame(res)
res <- res %>%
mutate(gene_id = row.names(res))
res <- res[order(row.names(res)), ]
res <- filter(res, FDR < 0.01)
dfr <- res[, c(1, 3, 4, 5)]
dfr <- dfr %>%
filter(logFC < -1 | logFC > 1)
names(dfr)[names(dfr) == "logFC"] <- paste0(sample.group, "_logFC")
names(dfr)[names(dfr) == "PValue"] <- paste0(sample.group, "_PValue")
names(dfr)[names(dfr) == "FDR"] <- paste0(sample.group, "_FDR")
# Example: Loop through all sample groups (then we can do as above):
sample.group = unique(master.table$sample.group)
for (i in 3:length(sample.group)) {
sras.in.group = master.table[master.table$sample.group == sample.group[i], ]$sra_run_id
control.group = unique(master.table[master.table$sample.group == sample.group[i], ]$respective_control)
if (control.group == "") {
next
}
else {
sras.in.control = master.table[master.table$sample.group == control.group, ]$sra_run_id
col.in.combined.abundance = colnames(combined.abundance)
col.interest = c(paste0(sras.in.control, "_est_counts"),
paste0(sras.in.group, "_est_counts"))
if (length(intersect(col.in.combined.abundance, col.interest)) == 0) { #if some sra id are missing in combined table
next
}
else {
selected.abundances = combined.abundance[c(
paste0(sras.in.control, "_est_counts"),
paste0(sras.in.group, "_est_counts"))]
#this is for those have no replication
if (length(sras.in.group) == 1) {
edger.group = factor(c(
rep(1, length(sras.in.control)),
rep(2, length(sras.in.group))))
bcv <- 0.1
y <- DGEList(counts = selected.abundances, group = edger.group)
et <- exactTest(y, dispersion = bcv ^ 2)
res <- topTags(et, n = Inf)
res <- as.data.frame(res)
res <- res %>%
mutate(gene_id = row.names(res))
res <-
res[order(row.names(res)), ] #ordering the genes in row from 00 chromosome to higher
res <- filter(res, FDR < 0.01)
df <-
res[, c(1, 3, 4, 5)] # we need logFC, p, fdr value and gene_id column
df <- df %>%
filter(logFC < -1 | logFC > 1)
names(df)[names(df) == "logFC"] <- paste0(sample.group[i], "_logFC")
names(df)[names(df) == "PValue"] <- paste0(sample.group[i], "_PValue")
names(df)[names(df) == "FDR"] <- paste0(sample.group[i], "_FDR")
dfr = full_join(dfr, df, by = "gene_id") #different samples have different DEGs cbind doesnot work when row is different that is why full_join
print(sample.group[i])
}
#this is for those have replication
else {
edger.group = factor(c(
rep(1, length(sras.in.control)),
rep(2, length(sras.in.group))))
y <- DGEList(counts = selected.abundances, group = edger.group)
y <- calcNormFactors(y)
y <- estimateCommonDisp(y)
y <- estimateTagwiseDisp(y)
et <- exactTest(y)
res <- topTags(et, n = Inf)
res <- as.data.frame(res)
res <- res %>%
mutate(gene_id = row.names(res))
res <- res[order(row.names(res)), ] #ordering the genes in row from 00 chromosome to higher
res <- filter(res, FDR < 0.01)
df <-res[, c(1, 3, 4, 5)] # we need logFC, p, fdr value and gene_id column
df <- df %>%
filter(logFC < -1 | logFC > 1)
names(df)[names(df) == "logFC"] <- paste0(sample.group[i], "_logFC")
names(df)[names(df) == "PValue"] <- paste0(sample.group[i], "_PValue")
names(df)[names(df) == "FDR"] <- paste0(sample.group[i], "_FDR")
dfr = full_join(dfr, df, by = "gene_id") #different samples have different DEGs cbind doesnot work when row is different that is why full_join
print(sample.group[i])
}
}
}
}
dfr[is.na(dfr)] <- 0
row.names(dfr) = dfr$gene_id
dfr = dplyr::select(dfr,-c('gene_id'))
combined.logFC = dfr[, c(grep("_logFC", colnames(dfr)))] ##fold change values were extracted
colnames(combined.logFC) = gsub("_logFC", "", colnames(combined.logFC))
colnames(combined.logFC)
write.csv(combined.logFC, "inputs/combined.logFC.csv", row.names = T)
#### design of different factors e.g genotypes, tissues, duration and heat levels for annotation
col = "e1-1"
tissue = unique(master.table[master.table$sample.group == col, ]$tissue)
stress = unique(master.table[master.table$sample.group == col, ]$stress_type)
duration = unique(master.table[master.table$sample.group == col, ]$stress.duration)
temp = unique(master.table[master.table$sample.group == col, ]$temperature)
genotype = unique(master.table[master.table$sample.group == col, ]$genotype.name)
design.logFC = data.frame(tissue, stress, duration, temp, genotype)
col = colnames(combined.logFC)
for (i in 2:length(col)) {
tissue = unique(master.table[master.table$sample.group == col[i], ]$tissue)
stress = unique(master.table[master.table$sample.group == col[i], ]$stress_type)
duration = unique(master.table[master.table$sample.group == col[i], ]$stress.duration)
temp = unique(master.table[master.table$sample.group == col[i], ]$temperature)
genotype = unique(master.table[master.table$sample.group == col[i], ]$genotype.name)
if(genotype == "") {
genotype = unique(master.table[master.table$sample.group == col[i], ]$genotype.accession)
design.rest = data.frame(tissue, stress, duration, temp, genotype)
design.logFC = rbind(design.logFC, design.rest)
}
else {
design.rest = data.frame(tissue, stress, duration, temp, genotype)
design.logFC = rbind(design.logFC, design.rest)
print(col[i])
}
}
genotype = design.logFC$genotype
genotype = as.data.frame(genotype)
tissue = design.logFC$tissue
tissue = as.data.frame(tissue)
duration = design.logFC$duration
duration = as.data.frame(duration)
temp = design.logFC$temp
temp = as.data.frame(temp)
ann_gen = HeatmapAnnotation(
df = cbind(duration, temp),
show_legend = TRUE,
annotation_name_gp = gpar(fontsize = 7))
ann_tissue = HeatmapAnnotation(df = cbind(genotype, tissue),
annotation_name_gp = gpar(fontsize = 7))
DEGs_exp = as.matrix(combined.logFC)
pdf("outputs/DEGs.pdf",
width = 6.13,
height = 5.60)
Heatmap(
DEGs_exp,
name = "logFC",
top_annotation = ann_gen,
show_row_names = FALSE,
bottom_annotation = ann_tissue,
column_title_gp = gpar(fontsize = 7),
column_names_gp = gpar(fontsize = 5)
)
dev.off()