-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathKD-DESeq2-Analysis.R
executable file
·296 lines (244 loc) · 16.4 KB
/
KD-DESeq2-Analysis.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
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
## RNA-seq analysis uising DEseq2 of Ahr knockdown (KD) samples ##
library(DESeq2)
library(tibble)
library(tidyr)
library(dplyr)
library(readr)
setwd("G:/RNASeq-Ahr-Gzf1-KD/CountMat/")
## COunt data - Program:featureCounts v1.4.6-p3; Command:"featureCounts" "-a"
##"./../../../RNA-seq-rep1/mm10fromENSEMBL/Mus_musculus.GRCm38.79.gtf" "-o" "03022017-Ahr.Gzf1.ReadsCount.try2txt" "-F" "GTF" "-t" "exon" "-g"
##"gene_id" "-s" "2" "-Q" "1" "-T" "12" "--minReadOverlap" "1" "-M" "A-siAHR-D1-rep1.bam" "A-siAHR-D1-rep2.bam" "A-siAHR-D1-rep3.bam"
##"A-siCTRL-D1-rep1.bam" "A-siCTRL-D1-rep2.bam" "A-siCTRL-D1-rep3.bam" "A-siCTRL-D9-rep1.bam" "A-siCTRL-D9-rep2.bam" "A-siCTRL-D9-rep3.bam"
##"A-siGZF1-D9-rep1.bam" "A-siGZF1-D9-rep2.bam" "A-siGZF1-D9-rep3.bam" "O-siAHR-D1-rep1.bam" "O-siAHR-D1-rep2.bam" "O-siAHR-D1-rep3.bam"
##"O-siCTRL-D1-rep1.bam" "O-siCTRL-D1-rep2.bam" "O-siCTRL-D1-rep3.bam" "ST2-siAHR-D1-rep1.bam" "ST2-siAHR-D1-rep2.bam" "ST2-siAHR-D1-rep3.bam"
##"ST2-siCTRL-D1-rep1.bam" "ST2-siCTRL-D1-rep2.bam" "ST2-siCTRL-D1-rep3.bam" ##
library(readr)
data = read_delim("03022017-Ahr.Gzf1.ReadsCount.try2.DESEQ.txt", delim = "\t", progress = TRUE) ## each row is a gene and each column is a
#RNA library, values give the raw numbers of sequencing reads mapped to the genes in each library ##
data
dataCount = data
## Only keep the counts for the siAHR (and their matched siCTRL) samples ##
dataCount = as.data.frame(select(dataCount, starts_with("ST2-siAHR"), starts_with("ST2-siCTRL"), starts_with("A-siAHR"),
starts_with("A-siCTRL-D1"), starts_with("O-siAHR"), starts_with("O-siCTRL")))
rownames(dataCount) = data$Geneid
head(dataCount)
## Provide metadata ##
mData = data.frame(row.names = colnames(dataCount), Cell = factor(c(rep("Msc",6), rep("Adipo",6), rep("Osteo", 6))),
type = factor(c(rep("siAHR",3), rep("siCTRL",3), rep("siAHR",3), rep("siCTRL",3), rep("siAHR",3),
rep("siCTRL", 3))),
Time = factor(c(rep("Day1", 6), rep("Day1",6), rep("Day1", 6))))
mData
## DESeq matrix ##
dds = DESeq2::DESeqDataSetFromMatrix(dataCount, mData, design = ~type)
dds
# Make sure that CTRL is the 1st level in the condition factor (R chose them alphabetically otherwise)
dds$type = relevel(dds$type, "siCTRL")
dds$group = factor(paste0(dds$Cell, dds$type, dds$Time))
design(dds) = ~group
dds$group
dds
## Remove rows that have only 0 or 1 read ##
dds = dds[rowSums(counts(dds)) > 1,]
## Visually explore the dataset ##
rld = rlogTransformation(dds, blind = FALSE) ##genes with high counts, rlog =~ log2 transformation / genes with low counts, values are shrunkrn towards the
# gene's average across all samples. NOT FOR DIFFERENTIAL TESTING!!!
head(assay(rld))
# => Genes with low counts are high variable using log2 transformation (variance increases with the mean because of poisson distribution)
# while rlog transformation compresses differences for genes
## Run Deseq for ST2 ##
dds2 = DESeq(dds)
resultsNames(dds2) #Check the result name to apply fold change
## DEGs in ST2 siAhr vs ST2 siCTRL ##
res.ST2.D1 = results(dds2, contrast = c("group", "MscsiAHRDay1", "MscsiCTRLDay1"))
write_delim(as.data.frame(res.ST2.D1), "Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/Figures/Figure7/28022017-ST2-AhrKD-AllGenes.txt", delim = "\t", col_names = TRUE)
resSig.ST2.D1 = subset(res.ST2.D1, padj < 0.1)
resSig.ST2.D1.df = as.data.frame(resSig.ST2.D1[order(resSig.ST2.D1$log2FoldChange), ])
## Add the gene symbol to the data => easier to read
library("biomaRt")
listMarts(host = 'mar2015.archive.ensembl.org') ## TO HAVE THE SAME ANNOTATION THAN THE ONE USED FOR THE TIME COURSE OF DIFFERENTIATION ##
ensembl79 = useMart(host = 'mar2015.archive.ensembl.org', biomart = 'ENSEMBL_MART_ENSEMBL',
dataset = 'mmusculus_gene_ensembl')
filters = listFilters(ensembl79)
head(filters)
attributes = listAttributes(ensembl79)
head(attributes)
ensID.ST2 = rownames(resSig.ST2.D1.df)
qry.ST2 = getBM(attributes = c('external_gene_name', 'ensembl_gene_id'), filters = 'ensembl_gene_id',
values = ensID.ST2, mart = ensembl79)
head(qry.ST2)
resSig.ST2.D1.df = rownames_to_column(resSig.ST2.D1.df, var = "ensembl_gene_id")
res.ST2.D1.GS = full_join(resSig.ST2.D1.df, qry.ST2, by = "ensembl_gene_id") %>%
dplyr::select(ensembl_gene_id, external_gene_name, baseMean, log2FoldChange, lfcSE, stat, pvalue, padj) %>%
dplyr::rename(geneSymbol = external_gene_name)
head(res.ST2.D1.GS)
write.table(res.ST2.D1.GS, "Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/DEG/14042017-ST2-AHR-KD-D1-FDR0.1.txt", sep = "\t", col.names = TRUE, row.names = FALSE, quote = FALSE)
## DEGs in A siAhr vs A siCTRL ##
res.A.D1 = results(dds2, contrast = c("group", "AdiposiAHRDay1", "AdiposiCTRLDay1"))
write_delim(as.data.frame(res.A.D1), "Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/Figures/Figure7/28022017-Ad-AhrKD-AllGenes.txt", delim = "\t", col_names = TRUE)
resSig.A.D1 = subset(res.A.D1, padj < 0.1)
resSig.A.D1.df = as.data.frame(resSig.A.D1[order(resSig.A.D1$log2FoldChange), ])
head(resSig.A.D1.df)
## Add the gene symbol to the data => easier to read
ensID.A = rownames(resSig.A.D1.df)
qry.A = getBM(attributes = c('external_gene_name', 'ensembl_gene_id'), filters = 'ensembl_gene_id',
values = ensID.A, mart = ensembl79)
head(qry.A)
resSig.A.D1.df = rownames_to_column(resSig.A.D1.df, var = "ensembl_gene_id")
res.A.D1.GS = full_join(resSig.A.D1.df, qry.A, by = "ensembl_gene_id") %>%
dplyr::select(ensembl_gene_id, external_gene_name, baseMean, log2FoldChange, lfcSE, stat, pvalue, padj) %>%
dplyr::rename(geneSymbol = external_gene_name)
head(res.A.D1.GS)
write.table(res.A.D1.GS, "Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/DEG/14042017-A-AHR-KD-D1-p0.1.txt", sep = "\t", col.names = TRUE, row.names = FALSE, quote = FALSE)
## DEGs in O siAhr vs O siCTRL ##
res.O.D1 = results(dds2, contrast = c("group", "OsteosiAHRDay1", "OsteosiCTRLDay1"))
write_delim(as.data.frame(res.O.D1), "Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/Figures/Figure7/28022017-Ob-AhrKD-AllGenes.txt", delim = "\t", col_names = TRUE)
resSig.O.D1 = subset(res.O.D1, padj < 0.1)
resSig.O.D1.df = as.data.frame(resSig.O.D1[order(resSig.O.D1$log2FoldChange), ])
head(resSig.O.D1.df)
ensID.O = rownames(resSig.O.D1.df)
qry.O = getBM(attributes = c('external_gene_name', 'ensembl_gene_id'), filters = 'ensembl_gene_id',
values = ensID.O, mart = ensembl79)
head(qry.O)
resSig.O.D1.df = rownames_to_column(resSig.O.D1.df, var = "ensembl_gene_id")
res.O.D1.GS = full_join(resSig.O.D1.df, qry.O, by = "ensembl_gene_id") %>%
dplyr::select(ensembl_gene_id, external_gene_name, baseMean, log2FoldChange, lfcSE, stat, pvalue, padj) %>%
dplyr::rename(geneSymbol = external_gene_name)
head(res.O.D1.GS)
write.table(res.O.D1.GS, "Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/DEG/14042017-O-AHR-KD-D1-p0.1.txt", sep = "\t", col.names = TRUE, row.names = FALSE, quote = FALSE)
# Make a scatter plot on DEG between adipo diff for 15 days and Ahr KD in ST2##
head(res.ST2.D1) ## DEG in ST2 KD for Ahr
res.ST2.D1$ensembl_gene_id = rownames(res.ST2.D1)
res.ST2.D1.sc = as.data.frame(res.ST2.D1)
head(res.ST2.D1.sc)
load("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/DEG/14042017-DEG-AD1vsD0.rda") ## DEG in diff AD15 vs D0
ComGenes266 = read_delim("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/EnrichR/18042017-CommonGenesBetweenAhrKG_266.txt", delim = "\t", col_names = FALSE) %>%
transmute(ensembl_gene_id = X1, external_gene_name = X2)
resAd15vsD0$ensembl_gene_id = rownames(resAd15vsD0)
resAd15vsD0.sc = as.data.frame(resAd15vsD0)
head(resAd15vsD0.sc)
# Table combining fold change and FDR for DEG in both sets ##
matScST2.AhrKD = full_join(res.ST2.D1.sc, resAd15vsD0.sc, by = "ensembl_gene_id") %>%
dplyr::select(ensembl_gene_id, log2FoldChange.x, padj.x, log2FoldChange.y, padj.y) %>%
dplyr::rename(log2FCAhrKD.ST2 = log2FoldChange.x, padjAhrKD.ST2 = padj.x, log2FCAd15vsD0 = log2FoldChange.y,
padjAd15vsD0 = padj.y) %>%
mutate(log2FCAhrKD.ST2 = ifelse(is.na(log2FCAhrKD.ST2), "0", log2FCAhrKD.ST2),
log2FCAd15vsD0 = ifelse(is.na(log2FCAd15vsD0), "0", log2FCAd15vsD0),
padjAhrKD.ST2 = ifelse(is.na(padjAhrKD.ST2), "1", padjAhrKD.ST2),
padjAd15vsD0 = ifelse(is.na(padjAd15vsD0), "1", padjAd15vsD0)) %>%
mutate(Sig = ifelse(as.numeric(padjAhrKD.ST2) < 0.1 & as.numeric(padjAd15vsD0) < 0.1, "Sig in both (FDR < 0.1)",
ifelse(as.numeric(padjAhrKD.ST2) < 0.1 & as.numeric(padjAd15vsD0) > 0.1, "Sig in KD",
ifelse(as.numeric(padjAd15vsD0) < 0.1 & as.numeric(padjAhrKD.ST2) > 0.1, "Sig in diff", "No Sig"))))
head(matScST2.AhrKD)
# Take the gene symbol instead of the ensembl id
ensG = dplyr::select(matScST2.AhrKD, ensembl_gene_id)
qry.ensG = getBM(attributes = c('external_gene_name', 'ensembl_gene_id'), filters = 'ensembl_gene_id',
values = ensG, mart = ensembl79)
matScST2.AhrKD = full_join(matScST2.AhrKD, qry.ensG, by = "ensembl_gene_id")
head(matScST2.AhrKD)
## Scatterplot
library(ggplot2)
library(viridis)
library(ggrepel)
# Highlight the genes name that are significantly UP in the KD and DOWN in diff
scST2vsAD15 = ggplot(matScST2.AhrKD %>% filter(Sig != "No Sig") %>% filter(Sig != "Sig in diff") , aes(x = as.numeric(log2FCAhrKD.ST2), y = as.numeric(log2FCAd15vsD0),
color = Sig)) +
geom_point() +
# geom_hex(bins = 100) +
theme_classic() +
geom_hline(yintercept = 0, size = 1.0, color = "#A6A6A6", linetype = "dashed") +
geom_vline(xintercept = 0, size = 1.0,color = "#A6A6A6", linetype = "dashed") +
theme(axis.line = element_line(colour = "black", size = 1, linetype = "solid"),
axis.text.x = element_text(face = "bold", size = 20, colour = "black"),
axis.text.y = element_text(face = "bold", size = 20, colour = "black"),
axis.title.x = element_text(face = "bold", size = 20),
axis.title.y = element_text(face = "bold", size = 20),
legend.position = "none", legend.text = element_text(size = 20, face = "bold.italic")) +
scale_color_manual(values = c("red", "#E69F00")) +
# scale_color_manual(values = c("black", "red", "#CCCCCC", "#E69F00")) +
# viridis::scale_fill_viridis(option = "B") +
# scale_x_continuous(limits = c(-2, 2)) +
# scale_y_continuous(limits = c(-6, 6)) +
labs(x = "Fold change ST2 siAhr/ST2 siCtrl (log2)",
y = "Fold change adipocytes day 15/ST2 day 0 (log2)") +
geom_point(data = matScST2.AhrKD %>% filter(Sig == "Sig in both (FDR < 0.1)"), colour = "red") +
geom_point(data = matScST2.AhrKD %>% filter(Sig == "Sig in KD"), colour = "#E69F00") +
geom_text_repel(data = matScST2.AhrKD %>% filter(Sig == "Sig in both (FDR < 0.1)") %>%
filter(external_gene_name == "Notch3"), size = 10, fontface = "bold.italic", color = "black", aes(label = external_gene_name), nudge_x = 0.5, nudge_y = 5,
box.padding = unit(0.75, "lines")) +
geom_text_repel(data = matScST2.AhrKD %>% filter(Sig == "Sig in both (FDR < 0.1)") %>%
filter(external_gene_name == "Ahr"), size = 10, fontface = "bold.italic", color = "black", aes(label = external_gene_name), nudge_x = -0.5,
nudge_y = -0.5, box.padding = unit(0.75, "lines"))
# geom_point(data = matScST2.AhrKD %>% inner_join(ComGenes266, by = "external_gene_name") , color = "blue")
scST2vsAD15
pdf("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/Figures/Figure7/02032018-FlyPlot-AD15vsST2KD.pdf", width = 8, height = 8)
scST2vsAD15
dev.off()
# Make a scatter plot on DEG between osteo diff for 15 days and Ahr KD in ST2##
# head(res.ST2.D1) ## DEG in ST2 KD for Ahr
# res.ST2.D1$ensembl_gene_id = rownames(res.ST2.D1)
# res.ST2.D1.sc = as.data.frame(res.ST2.D1)
# head(res.ST2.D1.sc)
load("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/DEG/14042017-DEG-OD15vsD0.rda") ## DEG in diff OB15 vs D0
resOd15vsD0$ensembl_gene_id = rownames(resOd15vsD0)
resOd15vsD0.sc = as.data.frame(resOd15vsD0)
head(resOd15vsD0.sc)
# Table combining fold change and FDR for DEG in both sets ##
matScST2.AhrKD.Ob = full_join(res.ST2.D1.sc, resOd15vsD0.sc, by = "ensembl_gene_id") %>%
dplyr::select(ensembl_gene_id, log2FoldChange.x, padj.x, log2FoldChange.y, padj.y) %>%
dplyr::rename(log2FCAhrKD.ST2 = log2FoldChange.x, padjAhrKD.ST2 = padj.x, log2FCOd15vsD0 = log2FoldChange.y,
padjOd15vsD0 = padj.y) %>%
mutate(log2FCAhrKD.ST2 = ifelse(is.na(log2FCAhrKD.ST2), "0", log2FCAhrKD.ST2),
log2FCOd15vsD0 = ifelse(is.na(log2FCOd15vsD0), "0", log2FCOd15vsD0),
padjAhrKD.ST2 = ifelse(is.na(padjAhrKD.ST2), "1", padjAhrKD.ST2),
padjOd15vsD0 = ifelse(is.na(padjOd15vsD0), "1", padjOd15vsD0)) %>%
mutate(Sig = ifelse(as.numeric(padjAhrKD.ST2) < 0.1 & as.numeric(padjOd15vsD0) < 0.1, "Sig in both (FDR < 0.1)",
ifelse(as.numeric(padjAhrKD.ST2) < 0.1 & as.numeric(padjOd15vsD0) > 0.1, "Sig in KD",
ifelse(as.numeric(padjOd15vsD0) < 0.1 & as.numeric(padjAhrKD.ST2) > 0.1, "Sig in diff", "No Sig"))))
head(matScST2.AhrKD.Ob)
# Take the gene symbol instead of the ensembl id
ensG = dplyr::select(matScST2.AhrKD.Ob, ensembl_gene_id)
qry.ensG = getBM(attributes = c('external_gene_name', 'ensembl_gene_id'), filters = 'ensembl_gene_id',
values = ensG, mart = ensembl79)
matScST2.AhrKD.Ob = full_join(matScST2.AhrKD.Ob, qry.ensG, by = "ensembl_gene_id")
head(matScST2.AhrKD.Ob)
## Scatterplot
library(ggplot2)
library(viridis)
library(ggrepel)
# Highlight the genes name that are significantly UP in the KD and DOWN in diff
scST2vsOb15 = ggplot(matScST2.AhrKD.Ob %>% filter(Sig != "No Sig") %>% filter(Sig != "Sig in diff"), aes(x = as.numeric(log2FCAhrKD.ST2), y = as.numeric(log2FCOd15vsD0),
color = Sig)) +
geom_point() +
# geom_hex(bins = 100) +
theme_classic() +
geom_hline(yintercept = 0, size = 1.0, color = "#A6A6A6", linetype = "dashed") +
geom_vline(xintercept = 0, size = 1.0, color = "#A6A6A6", linetype = "dashed") +
theme(axis.line = element_line(colour = "black", size = 1, linetype = "solid"),
axis.text.x = element_text(face = "bold", size = 20, colour = "black"),
axis.text.y = element_text(face = "bold", size = 20, colour = "black"),
axis.title.x = element_text(face = "bold", size = 20),
axis.title.y = element_text(face = "bold", size = 20),
legend.position = "none", legend.text = element_text(size = 20, face = "bold.italic")) +
scale_color_manual(values = c("red", "#E69F00")) +
# viridis::scale_fill_viridis(option = "B") +
# scale_x_continuous(limits = c(-2, 2)) +
# scale_y_continuous(limits = c(-6, 6)) +
labs(x = "Fold change ST2 siAhr/ST2 siCtrl (log2)",
y = "Fold change osteoblasts day 15/ST2 day 0 (log2)") +
geom_point(data = matScST2.AhrKD.Ob %>% filter(Sig == "Sig in both (FDR < 0.1)"), colour = "red") +
geom_point(data = matScST2.AhrKD.Ob %>% filter(Sig == "Sig in KD"), colour = "#E69F00") +
geom_text_repel(data = matScST2.AhrKD.Ob %>% filter(Sig == "Sig in both (FDR < 0.1)") %>%
filter(external_gene_name == "Notch3"), size = 10, fontface = "bold.italic", color = "black",
aes(label = external_gene_name), nudge_x = 0.5, nudge_y = 3.0, box.padding = unit(0.75, "lines")) +
geom_text_repel(data = matScST2.AhrKD.Ob %>% filter(Sig == "Sig in both (FDR < 0.1)") %>%
filter(external_gene_name == "Ahr"), size = 10, fontface = "bold.italic", color = "black",
aes(label = external_gene_name), nudge_x = -0.5, nudge_y = -0.5, box.padding = unit(0.75, "lines"))
# geom_point(data = matScST2.AhrKD.Ob %>% inner_join(ComGenes266, by = "external_gene_name") , color = "blue")
# geom_label_repel(data = matScST2.AhrKD %>% filter(Sig == "Sig in both (FDR < 0.1)") %>%
# filter(as.numeric(log2FCAhrKD.ST2) >= 0.27) %>%
# filter(as.numeric(log2FCAd1vsD0) <= -1),
# aes(label = external_gene_name))
scST2vsOb15
pdf("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/Figures/Figure7/02032018-FlyPlot-OB15vsST2KD.pdf", width = 8, height = 8)
scST2vsOb15
dev.off()