/usr/share/perl5/Genome/Model/Tools/Music/ClinicalCorrelation.pm.R is in libgenome-model-tools-music-perl 0.04-1.
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method = as.character(commandArgs()[4]);
if (method == "glm") {
### This program is for Generalized Linear Model (GLM) analysis
### Y = X + covar1 + covar2 ......
### Usually (not limited),
### Y is clinical trait (quantitative or binary)
### X is variant/gene/mutation etc.
### covar means covariate
### by Qunyuan Zhang (qunyuan@wustl.edu), 02/16/2012 updated
### input options
model.file = as.character(commandArgs()[5]);
y.file = as.character(commandArgs()[6]);
x.file = as.character(commandArgs()[7]);
out.file = as.character(commandArgs()[8]);
x.names="*";
### to run it on command line, type
# R --no-save < glm.R model.file y.file x.file * out.csv
# or (if you want to define x variables)
# R --no-save < glm.R model.csv y.file x.file x1|x2|x3 out.csv
# or (if x.file has been merged into y.file)
# R --no-save < glm.R model.csv y.file * x1|x2|x3 out.csv
# or (if you have defined x variable in model.file)
# R --no-save < glm.R model.csv y.file * * out.csv
### about model.file
# column "type": Q=quantitative trait, B=binary trait
# column "y": trait name
# colum "x": variant/gene name; if x=NA or blank, it will determined by x.file and x.names
# column "cvar": covariate(s)
# tab delimited
### about y.file
# trait data file, column 1 must be sample id
# tab delimited
### about x.file
# usually mutation/variant/geene data file
# the first column must be sample id (the same as in y.file, ordered or not)
# tab delimited
# x.file="*" if x.file already merged into y.file
### about x.names
# x.names="*" will use all column names in x.file as x variable names
# or you can define it in the format x.names="gene1|gene2|gene3"
# self-defined x.names have to be found in column names of y.file and/or x.file
#################### myglm fuction ##############
myglm=function(z,trait,variant,covar=NA,ytype) {
if (nchar(covar)==0 | is.na(covar) | is.null(covar)) {
model=formula(paste(trait,"~",variant))
} else {
model=formula(paste(trait,"~",variant,"+",covar))
}
if (ytype=="B") fit=glm(formula=model,data=z,family=binomial(link = "logit"))
if (ytype=="Q") fit=glm(formula=model,data=z,family=gaussian(link = "identity"))
fit
}
#################################################
### data input #####
read.table(model.file,colClasses="character",na.strings = c("","NA"),sep="\t",header=T)->md
read.table(y.file,na.strings = c("","NA"),sep="\t",header=T)->y
if (x.names!="*") x.names=strsplit(x.names,split="[|]")[[1]]
if (x.file!="*")
{
read.table(x.file,na.strings = c("","NA"),sep="\t",header=T)->x
xid=colnames(x)[1]
xs=colnames(x)[-1]
if (x.names!="*") {x=x[,c(xid,x.names)];xs=colnames(x)[-1]}
x.names=xs
yid=colnames(y)[1]
ysid = ! (colnames(y) %in% xs)
y=y[,ysid]
if (sum(ysid)==1) {y=data.frame(id=y);colnames(y)[1]=yid}
#y=merge(y,x,by.x = xid, by.y = yid)
y=merge(x,y,by.x = xid, by.y = yid)
}
######### analysis ##########
tt=NULL
for (i in c(1:nrow(md)))
{
ytype=md[i,1];yi=md[i,2];xs=md[i,3];covi=md[i,4];memo=md[i,5]
if (!is.na(xs) & nchar(xs)>0) xs=strsplit(xs,split="[|]")[[1]]
if (is.na(xs)[1]|nchar(xs)[1]==0) xs=x.names
if (length(covi)==0) covi=NA
for (xi in xs)
{
print(yi); print(xi); print(covi); print("******")
if (ytype=="Q") try(anova(myglm(y,yi,xi,covi,ytype),test="F"))->fit
if (ytype=="B") try(anova(myglm(y,yi,xi,covi,ytype),test="Chisq"))->fit
if (class(fit)[1]!="try-error")
{
fit=as.matrix(fit)
if (xi %in% rownames(fit)) tt=rbind(tt, cbind(yi,ytype,xi,as.data.frame(t(fit[xi,])),covi,memo))
}
}
}
#"yi","ytype","xi","Df","Deviance","Resid. Df","Resid. Dev","F","Pr(>F)","covi","memo"
if (ytype=="Q") colnames(tt) = c("y","y_type","x","degrees_freedom","deviance","residual_degrees_freedom","residual_deviance","F_statistic","p-value","covariants","memo");
if (ytype=="B") colnames(tt) = c("y","y_type","x","degrees_freedom","deviance","residual_degrees_freedom","residual_deviance","p-value","covariants","memo");
write.table(tt,file=out.file,quote=F,sep="\t",row.names=F);
} else {
# else, we process numerical or categorical clinical data correlation
clinical_data = as.character(commandArgs()[5]);
mutation_matrix = as.character(commandArgs()[6]);
output_file = as.character(commandArgs()[7]);
# FUNCTION finds the correlation between two variables
cor2=function(ty,tx,method)
{
id=intersect(!is.na(ty),!is.na(tx));
ty=ty[id];
tx=tx[id];
if(method=="cor")
{
tst=cor.test(tx,ty);
s=tst$est;
p=tst$p.value;
}
if(method=="wilcox") #x must be (0,1) mutation data
{
tst=wilcox.test(x=ty[tx==0],y=ty[tx>=1])
s=tst$stat
p=tst$p.value
}
if(method=="chisq")
{
tst=chisq.test(tx,ty);
s=tst$stat;
p=tst$p.value;
}
if(method=="fisher")
{
tst=fisher.test(tx,ty)
s=tst$p.value
p=tst$p.value
}
if(method=="anova")
{
tst=summary(aov(ty~tx,as.data.frame(cbind(tx,ty))))
s=tst[[1]]$F[1]
p=tst[[1]]$Pr[1]
}
tt=c(p,s);
tt;
}
# END cor2
# FUNCTION runs correlation test on matrixes of data
cor2test =function(y,x=NULL,method="cor",cutoff=1,sep="\t",outf=NULL)
{
if (!is.null(x))
{
if (length(x)==1) {read.table(x,header=T,sep=sep)->x;}
if (length(y)==1) {read.table(y,header=T,sep=sep)->y;}
colnames(y)[1]="id";
colnames(x)[1]="id";
tt=character(0);
for (vi in colnames(x)[-1])
{
for (vj in colnames(y)[-1])
{
tx=x[,c("id",vi)];
tx=tx[!is.na(tx[,vi]),];
tx=tx[!duplicated(tx[,"id"]),];
ty=y[,c("id",vj)];
ty=ty[!is.na(ty[,vj]),];
ty=ty[!duplicated(ty[,"id"]),];
xy=merge(tx,ty,by.x="id",by.y="id");
tx=xy[,2];
ty=xy[,3];
n=length(xy[,"id"]);
rst=try(cor2(ty,tx,method));
if (class(rst)=="try-error") {p=NA;s=NA;} else {p=rst[1];s=rst[2];}
t=c(vi,vj,method,n,s,p)
tt=rbind(tt,t);
} #end vj
} #end vi
rownames(tt)=NULL;
colnames(tt)=c("x","y","method","n","s","p");
tt=as.data.frame(tt);
tt[,"s"]=as.character(tt[,"s"]);
tt[,"s"]=as.numeric(tt[,"s"]);
tt[,"p"]=as.character(tt[,"p"]);
tt[,"p"]=as.numeric(tt[,"p"]);
fdr=p.adjust(tt[,"p"],method="fdr");
bon=p.adjust(tt[,"p"],method="bon");
tt=cbind(tt,fdr,bon);
tt=tt[order(tt[,"p"]),];
}
if (is.null(x))
{
if (length(y)==1) {read.table(y,header=T,sep=sep)->y;}
x=y;
nxy=ncol(y)-1;
colnames(y)[1]="id";
colnames(x)[1]="id"
tt=character(0);
for (i in c(1:(nxy-1)))
{
for (j in c((i+1):nxy))
{
vi=colnames(x)[-1][i];
vj=colnames(y)[-1][j];
tx=x[,c("id",vi)];
tx=tx[!is.na(tx[,vi]),];
tx=tx[!duplicated(tx[,"id"]),]
ty=y[,c("id",vj)];
ty=ty[!is.na(ty[,vj]),];
ty=ty[!duplicated(ty[,"id"]),];
xy=merge(tx,ty,by.x="id",by.y="id");
tx=xy[,2];
ty=xy[,3];
n=length(xy[,"id"]);
rst=try(cor2(ty,tx,method));
if (class(rst)=="try-error") {p=NA;s=NA;} else {p=rst[1];s=rst[2];}
t=c(vi,vj,method,n,s,p);
tt=rbind(tt,t);
} #end vj
} #end vi
rownames(tt)=NULL;
colnames(tt)=c("x","y","method","n","s","p");
tt=as.data.frame(tt);
tt[,"s"]=as.character(tt[,"s"]);
tt[,"s"]=as.numeric(tt[,"s"]);
tt[,"p"]=as.character(tt[,"p"]);
tt[,"p"]=as.numeric(tt[,"p"]);
fdr=p.adjust(tt[,"p"],method="fdr");
bon=p.adjust(tt[,"p"],method="bon");
tt=cbind(tt,fdr,bon);
tt=tt[order(tt[,"p"]),];
}
if (!is.null(outf))
{
colnames(tt)=c("x","y","method","n","s","p","fdr","bon");
#The amount of precision that R prints with is somehow machine dependent (or the R version?)
tt[,"s"] = sapply(tt[,"s"], sprintf, fmt="%.4E");
tt[,"p"] = sapply(tt[,"p"], sprintf, fmt="%.4E");
tt[,"fdr"] = sapply(tt[,"fdr"], sprintf, fmt="%.2E");
tt[,"bon"] = sapply(tt[,"bon"], sprintf, fmt="%.2E");
#The ordering should be done after reformatting the precision (duh)
tt=tt[order(tt[,"x"]),];
tt=tt[order(tt[,"p"]),];
write.table(tt,file=outf,quote=FALSE,row.names=FALSE,sep=",");
}
invisible(tt);
}
#END cor2test
#run correlation test using function
cor2test(y = clinical_data, x = mutation_matrix, method = method, outf = output_file);
}
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