-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathF4_repliseq_TPM.R
155 lines (125 loc) · 5.97 KB
/
F4_repliseq_TPM.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
#!/usr/bin/Rscript
library(tidyverse)
library(hexbin)
data <- read.table("~/Desktop/Decato-PMD-revision-analysis/repliseq_summary",header=TRUE)
# Too many bins to effectively visualize this relationship.
ggplot(data, aes(x=Methylation_Level, y=Repliseq_Signal)) +
stat_binhex() +
scale_fill_viridis_c() +
facet_wrap(~Sample, ncol=2,scales="free") +
stat_smooth(method=lm) +
theme_bw() +
theme(legend.position = "right", text = element_text(size=14), axis.text = element_text(size = 10),
axis.text.x = element_text(angle = 45, hjust = 1), strip.background = element_blank(),
strip.placement = "outside")
data <- data %>%
mutate(Bin = ifelse(Methylation_Level<0.33, "Low", ifelse(Methylation_Level<0.67, "Med","High"))) %>%
mutate(MethylationPercent = Methylation_Level*100)
data$Bin <- factor(data$Bin, levels=c("High","Med","Low"))
ggplot(data, aes(x=Sample, y=Repliseq_Signal,fill=Bin)) +
geom_boxplot() +
theme_bw() +
theme(legend.position = "right", text = element_text(size=14), axis.text = element_text(size = 10),
axis.text.x = element_text(angle = 45, hjust = 1), strip.background = element_blank(),
strip.placement = "outside")
# R^2=0.2757, p<2e-16, 1% reduction in methylation corresponds to 0.5199 loss in repliseq signal
imr90 <- data %>% filter(Sample == "Lister-ESC-2009_Human_IMR90")
summary(lm(imr90$Repliseq_Signal~imr90$MethylationPercent))
# R^2=0.3314, p<2e-16, 1% reduction in methylation corresponds to 0.3815 loss in repliseq signal
mcf7 <- data %>% filter(Sample == "Menafra-2014_Human_MCF7")
summary(lm(mcf7$Repliseq_Signal~mcf7$MethylationPercent))
# R^2=0.2612, p<2e-16, 1% reduction in methylation corresponds to 0.4263 loss in repliseq signal
gm12878 <- data %>% filter(Sample == "Schlesinger-2013_Human_GM12878")
summary(lm(gm12878$Repliseq_Signal~gm12878$MethylationPercent))
# R^2=0.3018, p<2e-16, 1% reduction in methylation corresponds to 0.4197 loss in repliseq signal
hepg2<- data %>% filter(Sample == "Ziller-2013-Human_HepG2")
summary(lm(hepg2$Repliseq_Signal~hepg2$MethylationPercent))
######
# Escapee status and Repli-seq signal
######
data$Region <- factor(data$Region, levels=c("inPMD","escapeePMD","outPMD"))
ggplot(data, aes(x=Sample, y=Repliseq_Signal, fill=Region)) +
geom_violin(draw_quantiles = c(0.5)) +
theme_bw() +
theme(legend.position = "right", text = element_text(size=14), axis.text = element_text(size = 10),
axis.text.x = element_text(angle = 45, hjust = 1), strip.background = element_blank(),
strip.placement = "outside")
imr90_escapee_inside <- data %>%
filter(Sample == "Lister-ESC-2009_Human_IMR90") %>%
filter(Region == "inPMD" | Region == "escapeePMD")
wilcox.test(imr90_escapee_inside$Repliseq_Signal~imr90_escapee_inside$Region)
# p < 2.2e-16
imr90_escapee_outside <- data %>%
filter(Sample == "Lister-ESC-2009_Human_IMR90") %>%
filter(Region == "outPMD" | Region == "escapeePMD")
wilcox.test(imr90_escapee_outside$Repliseq_Signal~imr90_escapee_outside$Region)
wilcox.test(imr90_escapee_outside$Repliseq_Signal~imr90_escapee_outside$Region)
# p < 2.2e-16
mcf7_escapee_inside <- data %>%
filter(Sample == "Menafra-2014_Human_MCF7") %>%
filter(Region == "inPMD" | Region == "escapeePMD")
wilcox.test(mcf7_escapee_inside$Repliseq_Signal~mcf7_escapee_inside$Region)
# p < 2.2e-16
mcf7_escapee_outside <- data %>%
filter(Sample == "Menafra-2014_Human_MCF7") %>%
filter(Region == "outPMD" | Region == "escapeePMD")
wilcox.test(mcf7_escapee_outside$Repliseq_Signal~mcf7_escapee_outside$Region)
wilcox.test(mcf7_escapee_outside$Repliseq_Signal~mcf7_escapee_outside$Region)
# p < 2.2e-16
#gm12878_escapee_inside <- data %>%
#gm12878_escapee_outside <- data %>%
#hepg2_escapee_inside <- data %>%
#hepg2_escapee_outside <- data %>%
###################################################################################
#### TPM figures
###################################################################################
tpm <- read.table("~/Desktop/Decato-PMD-revision-analysis/TPM_info", header=TRUE)
tpm$Region <- factor(tpm$Region, levels=c("inPMDs","escapees","outPMDs"))
# SA: Healthy small airway, BE: Healthy bronchial epithelium
tpm <- tpm %>%
separate(Sample, sep = "_", into = c("PMD_Source","tpm2","RNA","Barcode","Replicate"), extra = "warn") %>%
mutate(CancerHealthy = ifelse(RNA == "SA" | RNA == "BE", "Healthy","Cancer"))
ggplot(tpm, aes(x=PMD_Source, y=log(TPM),fill=CancerHealthy)) +
geom_boxplot(outlier.shape=NA) +
coord_cartesian(ylim=c(-5.5,8))+
facet_wrap(~Region, ncol=6) +
theme_bw() +
theme(legend.position = "bottom", text = element_text(size=14), axis.text = element_text(size = 10),
axis.text.x = element_text(angle = 45, hjust = 1), strip.background = element_blank(),
strip.placement = "outside")
escapees_outPMDs <- tpm %>%
filter(Region == "escapees" | Region == "outPMDs")
wilcox.test(escapees_outPMDs$TPM~escapees_outPMDs$Region, paired=FALSE) # p<0.1027, n.s.
escapees_inPMDs <- tpm %>%
filter(Region == "escapees" | Region == "inPMDs")
wilcox.test(escapees_inPMDs$TPM~escapees_inPMDs$Region) # p<2.2e-16
tpm %>%
filter(Region == "inPMDs") %>%
group_by(PMD_Source) %>%
do(w = wilcox.test(TPM~CancerHealthy, alternative = "less", data=., paired=FALSE)) %>%
summarise(PMD_Source, Wilcox = w$p.value)
# PMD_Source Wilcox
# 1650Lung 3.21e-88
# 441NSCLC 3.09e-84
# Calu1Lung 2.66e-37
# M3Lung 2.25e-81
tpm %>%
filter(Region == "escapees") %>%
group_by(PMD_Source) %>%
do(w = wilcox.test(TPM~CancerHealthy, alternative = "less", data=., paired=FALSE)) %>%
summarise(PMD_Source, Wilcox = w$p.value)
# PMD_Source Wilcox
# 1650Lung 0.176
# 441NSCLC 0.494
# Calu1Lung 0.0000863
# M3Lung 0.704
tpm %>%
filter(Region == "outPMDs") %>%
group_by(PMD_Source) %>%
do(w = wilcox.test(TPM~CancerHealthy, alternative = "less", data=., paired=FALSE)) %>%
summarise(PMD_Source, Wilcox = w$p.value)
# PMD_Source Wilcox
# 1650Lung 1.04e-70
# 441NSCLC 2.54e-19
# Calu1Lung 6.32e- 9
# M3Lung 7.46e- 1