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fun-analysisres.R
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## ==================================================================================== ##
# 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
# add option to label a set of genes (top 5, or name them)
rna_volcanoplot <- function(data_results, geneids=NULL,
test_sel=NULL,absFCcut=0,fdrcut=0.05) {
validate(need(mean(is.na(data_results$P.Value))<1,message = "All p-values are NA.
Check to make sure you have replicates or >1 groups for statistical analysis."))
validate(need(test_sel%in%data_results$test,message = "Incompatable test selection. Check group names of file."))
#res = data_results%>%filter(test==paste0(group1,"/",group2))
#if(test_sel%in%data_results$test) {
res = data_results%>%filter(test==test_sel)
#}else{res = data_results}
res = res%>%filter(!is.na(res$P.Value))
validate(need(mean(is.na(res$P.Value))<1,message = "All p-values for this test are NA.
Check to make sure you have replicates or >1 groups for statistical analysis."))
usepadj=TRUE
pvalname = "adj-pval"
if(is.null(res$adj.P.Val)) {
res$adj.P.Val = res$P.Value
usepadj = FALSE
pvalname = "pval"
}
res$color="None"
res$color[which((abs(res$logFC)>absFCcut)*(res$P.Value<fdrcut)==1)] = paste0("pval","<",fdrcut," & abs(logfc)>",absFCcut)
res$color[which((abs(res$logFC)<absFCcut)*(res$P.Value<fdrcut)==1)] = paste0("pval","<",fdrcut, " & abs(logfc)<",absFCcut)
res$color[which((abs(res$logFC)>absFCcut)*(res$adj.P.Val<fdrcut)==1)] = paste0(pvalname,"<",fdrcut," & abs(logfc)>",absFCcut)
res$color[which((abs(res$logFC)<absFCcut)*(res$adj.P.Val<fdrcut)==1)] = paste0(pvalname,"<",fdrcut, " & abs(logfc)<",absFCcut)
#res$color[which((abs(res$logFC)>absFCcut)*(res$adj.P.Val>fdrcut)==1)] = paste0(pvalname,">",fdrcut, " & abs(logfc)>",absFCcut)
res$color = factor(res$color,levels = unique(c("None",
paste0("pval","<",fdrcut, " & abs(logfc)<",absFCcut), # only exists if pval != pvalname
paste0(pvalname,"<",fdrcut," & abs(logfc)<",absFCcut),
paste0("pval","<",fdrcut, " & abs(logfc)>",absFCcut), # only exists if pval != pvalname
paste0(pvalname,"<",fdrcut, " & abs(logfc)>",absFCcut)
)))
p <- ggplot(res,aes(x=logFC,y=-log10(P.Value),color=color,text=unique_id))+geom_point()
if(length(levels(res$color))>3) {
p <- p + scale_color_manual(values=c("grey40","grey60","green3","grey70","red2"),
limits=levels(res$color),
name="Significance")
}else{
p <- p + scale_color_manual(values=c("grey40","green3","red2"),
limits=levels(res$color),
name="Significance")
}
p <- p + theme_base() + theme(plot.margin = unit(c(2,2,2,2), "cm"))
g <- plotly_build(p)
#Match order of text to proper gene order
newtext = paste("Gene ID:",res$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
}