Mercurial > repos > xuebing > sharplabtool
diff tools/stats/plot_from_lda.xml @ 0:9071e359b9a3
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| author | xuebing |
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| date | Fri, 09 Mar 2012 19:37:19 -0500 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/stats/plot_from_lda.xml Fri Mar 09 19:37:19 2012 -0500 @@ -0,0 +1,258 @@ +<tool id="plot_for_lda_output1" name="Draw ROC plot" version="1.0.1"> + <description>on "Perform LDA" output</description> + <command interpreter="sh">r_wrapper.sh $script_file</command> + + <inputs> + <param format="txt" name="input" type="data" label="Source file"> </param> + <param name="my_title" size="30" type="text" value="My Figure" label="Title of your plot" help="See syntax below"> </param> + <param name="X_axis" size="30" type="text" value="Text for X axis" label="Legend of X axis in your plot" help="See syntax below"> </param> + <param name="Y_axis" size="30" type="text" value="Text for Y axis" label="Legend of Y axis in your plot" help="See syntax below"> </param> + </inputs> + <outputs> + <data format="pdf" name="pdf_output" /> + </outputs> + + <tests> + <test> + <param name="input" value="lda_analy_output.txt"/> + <param name="my_title" value="Test Plot1"/> + <param name="X_axis" value="Test Plot2"/> + <param name="Y_axis" value="Test Plot3"/> + <output name="pdf_output" file="plot_for_lda_output.pdf"/> + </test> + </tests> + + <configfiles> + <configfile name="script_file"> + + rm(list = objects() ) + + ############# FORMAT X DATA ######################### + format<-function(data) { + ind=NULL + for(i in 1 : ncol(data)){ + if (is.na(data[nrow(data),i])) { + ind<-c(ind,i) + } + } + #print(is.null(ind)) + if (!is.null(ind)) { + data<-data[,-c(ind)] + } + + data + } + + ########GET RESPONSES ############################### + get_resp<- function(data) { + resp1<-as.vector(data[,ncol(data)]) + resp=numeric(length(resp1)) + for (i in 1:length(resp1)) { + if (resp1[i]=="Control ") { + resp[i] = 0 + } + if (resp1[i]=="XLMR ") { + resp[i] = 1 + } + } + return(resp) + } + + ######## CHARS TO NUMBERS ########################### + f_to_numbers<- function(F) { + ind<-NULL + G<-matrix(0,nrow(F), ncol(F)) + for (i in 1:nrow(F)) { + for (j in 1:ncol(F)) { + G[i,j]<-as.integer(F[i,j]) + } + } + return(G) + } + + ###################NORMALIZING######################### + norm <- function(M, a=NULL, b=NULL) { + C<-NULL + ind<-NULL + + for (i in 1: ncol(M)) { + if (sd(M[,i])!=0) { + M[,i]<-(M[,i]-mean(M[,i]))/sd(M[,i]) + } + # else {print(mean(M[,i]))} + } + return(M) + } + + ##### LDA DIRECTIONS ################################# + lda_dec <- function(data, k){ + priors=numeric(k) + grandmean<-numeric(ncol(data)-1) + means=matrix(0,k,ncol(data)-1) + B = matrix(0, ncol(data)-1, ncol(data)-1) + N=nrow(data) + for (i in 1:k){ + priors[i]=sum(data[,1]==i)/N + grp=subset(data,data\$group==i) + means[i,]=mean(grp[,2:ncol(data)]) + #print(means[i,]) + #print(priors[i]) + #print(priors[i]*means[i,]) + grandmean = priors[i]*means[i,] + grandmean + } + + for (i in 1:k) { + B= B + priors[i]*((means[i,]-grandmean)%*%t(means[i,]-grandmean)) + } + + W = var(data[,2:ncol(data)]) + svdW = svd(W) + inv_sqrtW =solve(svdW\$v %*% diag(sqrt(svdW\$d)) %*% t(svdW\$v)) + B_star= t(inv_sqrtW)%*%B%*%inv_sqrtW + B_star_decomp = svd(B_star) + directions = inv_sqrtW%*%B_star_decomp\$v + return( list(directions, B_star_decomp\$d) ) + } + + ################ NAIVE BAYES FOR 1D SIR OR LDA ############## + naive_bayes_classifier <- function(resp, tr_data, test_data, k=2, tau) { + tr_data=data.frame(resp=resp, dir=tr_data) + means=numeric(k) + #print(k) + cl=numeric(k) + predclass=numeric(length(test_data)) + for (i in 1:k) { + grp = subset(tr_data, resp==i) + means[i] = mean(grp\$dir) + #print(i, means[i]) + } + cutoff = tau*means[1]+(1-tau)*means[2] + #print(tau) + #print(means) + #print(cutoff) + if (cutoff>means[1]) { + cl[1]=1 + cl[2]=2 + } + else { + cl[1]=2 + cl[2]=1 + } + + for (i in 1:length(test_data)) { + + if (test_data[i] <= cutoff) { + predclass[i] = cl[1] + } + else { + predclass[i] = cl[2] + } + } + #print(means) + #print(mean(means)) + #X11() + #plot(test_data,pch=predclass, col=resp) + predclass + } + + ################# EXTENDED ERROR RATES ################# + ext_error_rate <- function(predclass, actualclass,msg=c("you forgot the message"), pr=1) { + er=sum(predclass != actualclass)/length(predclass) + + matr<-data.frame(predclass=predclass,actualclass=actualclass) + escapes = subset(matr, actualclass==1) + subjects = subset(matr, actualclass==2) + er_esc=sum(escapes\$predclass != escapes\$actualclass)/length(escapes\$predclass) + er_subj=sum(subjects\$predclass != subjects\$actualclass)/length(subjects\$predclass) + + if (pr==1) { + # print(paste(c(msg, 'overall : ', (1-er)*100, "%."),collapse=" ")) + # print(paste(c(msg, 'within escapes : ', (1-er_esc)*100, "%."),collapse=" ")) + # print(paste(c(msg, 'within subjects: ', (1-er_subj)*100, "%."),collapse=" ")) + } + return(c((1-er)*100, (1-er_esc)*100, (1-er_subj)*100)) + } + + ## Main Function ## + + files_alias<-c("${my_title}") + tau=seq(0,1,by=0.005) + nfiles=1 + f = c("${input}") + + rez_ext<-list() + for (i in 1:nfiles) { + rez_ext[[i]]<-dget(paste(f[i], sep="",collapse="")) + } + + tau<-tau[1:(length(tau)-1)] + for (i in 1:nfiles) { + rez_ext[[i]]<-rez_ext[[i]][,1:(length(tau)-1)] + } + + ######## OPTIMAIL TAU ########################### + + #rez_ext + + rate<-c("Optimal tau","Tr total", "Tr Y", "Tr X") + + m_tr<-numeric(nfiles) + m_xp22<-numeric(nfiles) + m_x<-numeric(nfiles) + + for (i in 1:nfiles) { + r<-rez_ext[[i]] + #tr + # rate<-rbind(rate, c(files_alias[i]," "," "," ") ) + mm<-which((r[3,])==max(r[3,])) + + m_tr[i]<-mm[1] + rate<-rbind(rate,c(tau[m_tr[i]],r[,m_tr[i]])) + } + print(rate) + + pdf(file= paste("${pdf_output}")) + + plot(rez_ext[[i]][2,]~rez_ext[[i]][3,], xlim=c(0,100), ylim=c(0,100), xlab="${X_axis} [1-FP(False Positive)]", ylab="${Y_axis} [1-FP(False Positive)]", type="l", lty=1, col="blue", xaxt='n', yaxt='n') + for (i in 1:nfiles) { + lines(rez_ext[[i]][2,]~rez_ext[[i]][3,], xlab="${X_axis} [1-FP(False Positive)]", ylab="${Y_axis} [1-FP(False Positive)]", type="l", lty=1, col=i) + # pt=c(r,) + points(x=rez_ext[[i]][3,m_tr[i]],y=rez_ext[[i]][2,m_tr[i]], pch=16, col=i) + } + + + title(main="${my_title}", adj=0, cex.main=1.1) + axis(2, at=c(0,20,40,60,80,100), labels=c('0','20','40','60','80','100%')) + axis(1, at=c(0,20,40,60,80,100), labels=c('0','20','40','60','80','100%')) + + #leg=c("10 kb","50 kb","100 kb") + #legend("bottomleft",legend=leg , col=c(1,2,3), lty=c(1,1,1)) + + #dev.off() + + </configfile> + </configfiles> + + + <help> +.. class:: infomark + +**What it does** + +This tool generates a Receiver Operating Characteristic (ROC) plot that shows LDA classification success rates for different values of the tuning parameter tau as Figure 3 in Carrel et al., 2006 (PMID: 17009873). + +*Carrel L, Park C, Tyekucheva S, Dunn J, Chiaromonte F, et al. (2006) Genomic Environment Predicts Expression Patterns on the Human Inactive X Chromosome. PLoS Genet 2(9): e151. doi:10.1371/journal.pgen.0020151* + +----- + +.. class:: warningmark + +**Note** + +- Output from "Perform LDA" tool is used as input file for this tool. + +</help> + + + +</tool>