generated from jhudsl/OTTR_Template
-
Notifications
You must be signed in to change notification settings - Fork 1
/
05-annotation_activity.Rmd
181 lines (141 loc) · 6.14 KB
/
05-annotation_activity.Rmd
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
# Annotation: Hands-On Exercise
Now it's your turn to try!
## The Code
Here's some R code that hasn't been annotated particularly well.
```
library(qqman)
library(beepr)
# The list of datasets
datasets=c("fivehmc.glmmtmb.all.cing","fivehmc.glmmtmb.all.pari","fivemc.glmmtmb.cingulate","fivemc.glmmtmb.parietal")
mod=c("5hmc","5hmc","5mc","5mc") # For labeling purposes
tissues=rep(c("Cingulate","Parietal"),2) # For labeling purposes
if(dir.exists(paste0(home,"/ManhattanPlots"))==FALSE){ # For storing the graphs
dir.create(paste0(home,"/ManhattanPlots"))
}
tissues.f=c("Cingulate","Cingulate","Parietal","Parietal")
stage=c("limbic","neocortical","limbic","neocortical")
fdr.p=data.frame(t(c(rep(0,4))))
colnames(fdr.p)=c("FC","HolP","tissues","stage")
fdr.p=fdr.p[-1,]
for(ii in 1:length(fdr.files)){
xx=read.table(paste0(home,"/",fdr.files[ii]),row.names=1,skip=1)
xx=cbind(xx,rep(tissues.f[ii],nrow(xx)),rep(stage[ii],nrow(xx)))
colnames(xx)=c("FC","HolP","tissues","stage")
fdr.p=rbind(fdr.p,xx)
}
for(ii in 1:length(datasets)){
data=eval(parse(text=datasets[ii]))
probes=rownames(data)
# Match the probe names
if(ii<3){
yy=fdr.p[which(fdr.p[,3]==tissues[ii]),]
yy=yy[match(probes,rownames(yy)),]
yy=yy[which(!is.na(yy[,1])),]
yy.l=yy[which(yy[,4]=="limbic"),]
yy.n=yy[which(yy[,4]=="neocortical"),]
probes.l=rownames(yy.l)
}else{
probes.l=rownames(data)
}
xx.l=match(probes.l,EPIC.manifest@ranges@NAMES)
chrs.l=EPIC.manifest@elementMetadata@listData$chrmA[xx.l]
#manhattan function requires chromosomes to be noted as numeric vector with X, Y, and MT chrs being 23:25 respectively.
chrs.l=gsub("chr","",chrs.l)
chrs.l=gsub("X",23,chrs.l)
chrs.l=gsub("Y",24,chrs.l)
chrs.l=gsub("MT",25,chrs.l)
if(ii<3){
probes.n=rownames(yy.n)
xx.n=match(probes.n,EPIC.manifest@ranges@NAMES)
chrs.n=EPIC.manifest@elementMetadata@listData$chrmA[xx.n]
#manhattan function requires chromosomes to be noted as numeric vector with X, Y, and MT chrs being 23:25 respectively.
chrs.n=gsub("chr","",chrs.n)
chrs.n=gsub("X",23,chrs.n)
chrs.n=gsub("Y",24,chrs.n)
chrs.n=gsub("MT",25,chrs.n)
# Make a dataframe
manh.data.l=data.frame(probes.l,
as.numeric(chrs.l),
EPIC.manifest@ranges@start[xx.l],
-log10(yy.l[,2]),
stringsAsFactors = FALSE)
manh.data.n=data.frame(probes.n,
as.numeric(chrs.n),
EPIC.manifest@ranges@start[xx.n],
-log10(yy.n[,2]),
stringsAsFactors = FALSE)
colnames(manh.data.l)=colnames(manh.data.n)=c("CPG","CHR","BP","P")
}else{
manh.data.l=data.frame(probes.l,
as.numeric(chrs.l),
EPIC.manifest@ranges@start[xx.l],
-log10(data$LimbicVSNoneFDR),
-log10(data$NeocorticalVSNoneFDR),
stringsAsFactors = FALSE)
colnames(manh.data.l)=c("CPG","CHR","BP","P","NP")
manh.data.l=manh.data.l[-which(manh.data.l$NP==Inf),]
xx=unique(which(is.nan(manh.data.l$NP)),which(is.nan(manh.data.l$P)))
if(length(xx)>0){
manh.data.l=manh.data.l[-xx,]
}
}
# Label them as such.
manh.data.l=manh.data.l[-which(manh.data.l$P==Inf),]
manh.data.l$CPG=as.character(manh.data.l$CPG) # CPG's need to be a character vector
manh.data.l$CHR=as.numeric(manh.data.l$CHR) # Chromsomal locations need to be numeric
manh.data.l$BP= as.numeric(manh.data.l$BP)
manh.data.l=manh.data.l[which(!is.na(manh.data.l$CHR)),]
if(ii<3){
manh.data.n=manh.data.n[-which(manh.data.n$P==Inf),]
manh.data.n$CPG=as.character(manh.data.n$CPG) # CPG's need to be a character vector
manh.data.n$CHR=as.numeric(manh.data.n$CHR) # Chromsomal locations need to be numeric
manh.data.n$BP= as.numeric(manh.data.n$BP)
manh.data.n=manh.data.n[which(!is.na(manh.data.n$CHR)),]
}
# Manhattan Plot for Limbic data
jpeg(paste0(home,"/ManhattanPlots/",datasets[ii],"MidManhattan2.jpeg"))
if(ii<3){
sig=manh.data.l$CPG[which(manh.data.l$P>3)]
manhattan(manh.data.l,chr="CHR",bp="BP",p="P",snp="CPG",logp=F,ylim=c(0,round(max(manh.data.l$P))+10),highlight=sig,chrlabs=c(1:22, "X", "Y"),suggestiveline=FALSE,genomewideline=TRUE,main=paste( mod[ii],tissues[ii],"Mid Stage Disease"))
}else{
sig=manh.data.l$CPG[which(manh.data.l$P>3)]
manhattan(manh.data.l,chr="CHR",bp="BP",p="P",snp="CPG",ylim=c(0,round(max(manh.data.l$P[!is.na(manh.data.l$P)]))),logp=F,highlight=sig,chrlabs=c(1:22, "X", "Y"),suggestiveline=FALSE,genomewideline=TRUE,main=paste( mod[ii],tissues[ii],"Mid Stage Disease"))
}
# highlight probes
abline(h=-log10(.001),col="red")
dev.off()
# Manhattan Plot for Neocortical data
jpeg(paste0(home,"/ManhattanPlots/",datasets[ii],"LateManhattan2.jpeg"))
if(ii<3){
sig=manh.data.n$CPG[which(manh.data.n$P>3)]
manhattan(manh.data.n,chr="CHR",bp="BP",p="P",snp="CPG",ylim=c(0,round(max(manh.data.l$P))+10),logp=F,highlight=sig,chrlabs=c(1:22, "X", "Y"),suggestiveline=FALSE,genomewideline=TRUE,main=paste(mod[ii],tissues[ii],"Late Stage Disease"))
}else{
sig=manh.data.l$CPG[which(manh.data.l$NP>3)]
manhattan(manh.data.l,chr="CHR",bp="BP",p="NP",snp="CPG",ylim=c(0,round(max(manh.data.l$NP))),logp=F,highlight=sig,chrlabs=c(1:22, "X", "Y"),suggestiveline=FALSE,genomewideline=TRUE,main=paste(mod[ii],tissues[ii],"Late Stage Disease"))
}
abline(h=-log10(.001),col="red")
dev.off()
}
# Just a nifty way to signal that your graphs are finished being made.
beep(sound=2)
```
## Questions
1. Create a README file for this code. Make sure that it includes general purpose of the project, instructions on how to re-run the project, any software required by the project, both input and output file descriptions, and descriptions of any additional tools included in the project.
2. How can the annotation for this section be improved?
```
# Make a dataframe
manh.data.l=data.frame(probes.l,
as.numeric(chrs.l),
EPIC.manifest@ranges@start[xx.l],
-log10(yy.l[,2]),
stringsAsFactors = FALSE)
manh.data.n=data.frame(probes.n,
as.numeric(chrs.n),
EPIC.manifest@ranges@start[xx.n],
-log10(yy.n[,2]),
stringsAsFactors = FALSE)
colnames(manh.data.l)=colnames(manh.data.n)=c("CPG","CHR","BP","P")
```
```{r}
devtools::session_info()
```