forked from jminnier/STARTapp
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathfun-analysisres.R
195 lines (150 loc) · 7.88 KB
/
fun-analysisres.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
## ==================================================================================== ##
# START Shiny App for analysis and visualization of transcriptome data.
# Copyright (C) 2016 Jessica Minnier
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# You may contact the author of this code, Jessica Minnier, at <[email protected]>
## ==================================================================================== ##
## ==================================================================================== ##
## Volcano Plot
## ==================================================================================== ##
# change rna_volcanoplot to have an input function that depends on type of variables and then a plotting function
# need to be more careful about what p-value (adjusted or raw) is used for colors
rna_volcanoplot <- function(data_results, geneids=NULL,
test_sel=NULL,absFCcut=0,fdrcut=0.05) {
print(dim(data_results))
#group1 = group_sel[1]; group2 = group_sel[2]
#res = data_results%>%filter(test==paste0(group1,"/",group2))
if(test_sel%in%data_results$test) {
res = data_results%>%filter(test==test_sel)
}else{res = data_results}
usepadj=TRUE
pvalname = "adj-pval"
if(is.null(res$adj.P.Val)) {
res$adj.P.Val = res$P.Value
usepadj = FALSE
pvalname = "pval"
}
res$color="None"
res$color[which(res$adj.P.Val<fdrcut)] = paste0(pvalname,"<",fdrcut, " & abs(logfc)<",absFCcut)
res$color[which(abs(res$logFC)>absFCcut)] = paste0(pvalname,">",fdrcut, " & abs(logfc)<",absFCcut)
res$color[which((abs(res$logFC)>absFCcut)*(res$adj.P.Val<.05)==1)] = paste0(pvalname,"<",fdrcut," & abs(logfc)>",absFCcut)
res$color = factor(res$color,levels = c("None",paste0("adj-pval<",fdrcut, " & abs(logfc)<",absFCcut),
paste0(pvalname,">",fdrcut, " & abs(logfc)<",absFCcut),
paste0(pvalname,"<",fdrcut," & abs(logfc)>",absFCcut)
))
p <- ggplot(res,aes(x=logFC,y=-log10(P.Value),color=color,text=unique_id))+geom_point()+
scale_color_manual(values=c("black","red","orange","green"),name="Significance")
p <- p + theme_base() + theme(plot.margin = unit(c(2,2,2,2), "cm"))
g <- plotly_build(p)
#Match order of text to proper gene order
newtext = paste("Gene ID:",res$unique_id,"<br>",
"Comparison",res$test,"<br>",
"logFC",signif(res$logFC,3),"<br>",
"P.Value",signif(res$P.Value,3),"<br>",
"adj.P.Val",signif(res$adj.P.Val,3))
for(ii in 1:length(g$data)) {
tmpid = do.call(rbind,strsplit(g$data[[ii]]$text,"<br>"))[,4]
g$data[[ii]]$text <- newtext[match(tmpid,res$unique_id)]
}
g
}
# switched from ggvis to plotly, this function is not currently used
rna_volcanoplot_ggvis <- function(data_results, geneids=NULL,
test_sel=NULL,absFCcut=0,fdrcut=0.05) {
print(dim(data_results))
res = data_results%>%filter(test==test_sel)
usepadj=TRUE
if(is.null(res$adj.P.Val)) {
res$adj.P.Val = res$P.Value
usepadj = FALSE
}
res$color="None"
res$color[which(res$adj.P.Val<fdrcut)] = paste0("adj-pval<",fdrcut)
res$color[which(abs(res$logFC)>absFCcut)] = paste0("abs(logfc)>",absFCcut)
res$color[which((abs(res$logFC)>absFCcut)*(res$adj.P.Val<.05)==1)] = paste0("adj-pval<",fdrcut," & abs(logfc)>",absFCcut)
res$color = factor(res$color,levels = c("None",paste0("adj-pval<",fdrcut),paste0("abs(logfc)>",absFCcut),paste0("adj-pval<",fdrcut," & abs(logfc)>",absFCcut)))
res$id = 1:nrow(res)
all_values <- function(x){
if(is.null(x)) return(NULL)
row <- res[res$id==x$id,]
if(usepadj) {
show <- c("unique_id","test","logFC","P.Value","adj.P.Val")
showname <- c("Gene ID","Comparison","logFC","raw p-value","BH FDR adjusted p-value")
}else{
show <- c("unique_id","test","logFC","P.Value")
showname <- c("Gene ID","Comparison","logFC","p-value")
}
tmpout = paste0(showname,": ",format(row[,show],digits=3),collapse="<br />")
tmpout
# paste0(tmpout,"<br/>",paste0("proteomics MGI Name: ",tmpgenename,collapse="<br/>"),
# "<br/>",paste0("proteomics Accession: ",tmpaccession,collapse="<br/>"))
}
res%>%ggvis(~logFC,~ -log10(P.Value),fill=~color,key := ~id)%>%
layer_points()%>%add_axis("x",title="Log2(FC)")%>%
add_axis("x",orient = "top",title=paste0("Comparison: ",unique(res$test)),
ticks=0)%>%
add_axis("y",title="-log10(p-value)")%>%
add_tooltip(all_values, "hover")%>%add_legend("fill",title="Significance")
}
## ==================================================================================== ##
## Scatter plot of log2 fold changes
## ==================================================================================== ##
rna_scatterplot <- function(data_long, geneids=NULL, group_sel=NULL,
valuename="log2cpm") {
group1 = group_sel[1]; group2 = group_sel[2]
data_long$value = data_long[,valuename]
pp = data_long%>%filter(group%in%group_sel)
pp_sum = pp%>%group_by(unique_id,group)%>%summarise("Ave_value"=mean(value))
pp_wide = pp_sum%>%spread(key = group,Ave_value)
pp_wide$id = 1:nrow(pp_wide)
colnames(pp_wide)[c(match(group1,colnames(pp_wide)),match(group2,colnames(pp_wide)))] = c("g1","g2")
pp_wide = pp_wide%>%mutate(diff = g1-g2,color=1*(g1>=g2))
# pp_wide = pp_wide%>%filter(value>=valuecut[1],value<=valuecut[2])
# all_values <- function(x){
# if(is.null(x)) return(NULL)
# row <- pp_wide[pp_wide$id==x$id,]
# show <- c("unique_id","g1","g2","diff")
# showname <- c("Gene ID",
# paste0(group1,"_Ave",valuename),paste0(group2,"_Ave",valuename),
# "difference")
# tmpout = paste0(showname,": ",format(row[,show],digits=3),collapse="<br />")
# tmpout
# }
# pp_wide%>%ggvis(~g1,~g2,fill=~factor(color),key := ~id)%>%
# layer_points()%>%add_axis("x",title=paste0(group1,"_Ave",valuename))%>%
# add_axis("x",orient = "top",title=paste0("Number of genes: ",nrow(pp_wide)),
# ticks=0)%>%
# add_axis("y",title=paste0(group2,"_Ave",valuename))%>%
# add_tooltip(all_values, "hover")%>%hide_legend("fill")
# switch to ggplotly since ggvis was slow
p <- ggplot(pp_wide,aes(x=g1,y=g2,
color=factor(color),text=unique_id))+geom_point()
p <- p + xlab(paste0(group1,"_Ave",valuename)) + ylab(paste0(group2,"_Ave",valuename))+
scale_color_manual(values=c("darkred","darkorange"))
p <- p + theme_base() + #ggtitle(paste0("Number of genes: ",nrow(pp_wide))) +
theme(legend.position="none",plot.margin = unit(c(2,2,2,2), "cm"))
g <- plotly_build(p)
#Match order of text to proper gene order
newtext = paste("Gene ID:",pp_wide$unique_id,"<br>",
paste0(group1,"_Ave",valuename,":"),round(pp_wide$g1,3),"<br>",
paste0(group2,"_Ave",valuename,":"),round(pp_wide$g2,3),"<br>",
"Difference:",round(pp_wide$diff,3))
tmpid = do.call(rbind,strsplit(g$data[[1]]$text,"<br>"))[,4]
g$data[[1]]$text <- newtext[match(tmpid,pp_wide$unique_id)]
tmpid = do.call(rbind,strsplit(g$data[[2]]$text,"<br>"))[,4]
g$data[[2]]$text <- newtext[match(tmpid,pp_wide$unique_id)]
g
}