Browse code

Adds DASiR, metagenomeSeq to the repos and manifest.

git-svn-id: file:///home/git/hedgehog.fhcrc.org/bioconductor/trunk/madman/Rpacks/metagenomeSeq@74804 bc3139a8-67e5-0310-9ffc-ced21a209358

Marc Carlson authored on 25/03/2013 18:40:33
Showing86 changed files

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+Package: metagenomeSeq
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+Title: Statistical analysis for sparse high-throughput sequencing
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+Version: 0.99.0
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+Date: 2012-07-23
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+Author: Joseph Nathaniel Paulson, Hector Corrada-Bravo
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+Maintainer: Joseph Paulson <jpaulson@umiacs.umd.edu>
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+Description: metaR is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metaR is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations.
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+License: Artistic-2.0
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+Depends: R(>= 2.10.0), Biobase, limma, matrixStats, methods,
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+        RColorBrewer, gplots
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+Suggests: annotate
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+biocViews: Bioinformatics, DifferentialExpression, Metagenomics,
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+        Visualization
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+Collate: 'zigControl.R' 'cumNorm.R' 'plotOTU.R' 'fitZig.R'
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+        'doCountMStep.R' 'doZeroMStep.R' 'doEStep.R' 'getZ.R' 'getPi.R'
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+        'getCountDensity.R' 'getNegativeLogLikelihoods.R'
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+        'isItStillActive.R' 'getEpsilon.R' 'load_meta.R'
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+        'load_phenoData.R' 'exportMat.R' 'exportStats.R'
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+        'cumNormStat.R' 'plotGenus.R' 'aggregateM.R' 'cumNormMat.R'
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+        'load_metaQ.R' 'allClasses.R' 'MRtable.R' 'MRcoefs.R'
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+        'plotMRheatmap.R' 'plotCorr.R' 'MRfisher.R' 'MRfulltable.R'
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+URL: http://cbcb.umd.edu/software/metaR
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+import(Biobase)
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+exportClasses( "MRexperiment" )
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+
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+exportMethods(
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+"["
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+)
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+
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+export(
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+aggregateM,
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+cumNorm,
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+cumNormMat,
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+cumNormStat,
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+exportMat,
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+exportStats,
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+fitZig,
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+load_meta,
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+load_metaQ,
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+load_phenoData,
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+MRcoefs,
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+MRfisher,
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+MRfulltable,
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+MRtable,
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+plotCorr,
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+plotGenus,
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+plotMRheatmap,
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+plotOTU,
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+zigControl,
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+newMRexperiment,
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+MRcounts,
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+posterior.probs,
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+normFactors,
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+libSize,
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+expSummary
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+)
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+version 1.0.0: (2013-00-00)
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+
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+-- release!
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+MRcoefs<-function(obj,by=2,coef=NULL,number=10,taxa=obj$taxa,adjust.method="fdr",group=0,eff=0,output=NULL){
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+    tb = obj$fit$coefficients
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+    tx = as.character(taxa);
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+    
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+    for (nm in unique(tx)) {
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+        ii=which(tx==nm)
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+        tx[ii]=paste(tx[ii],seq_along(ii),sep=":")
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+    }
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+    if(is.null(coef)){coef = 1:ncol(tb);}
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+
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+    p=obj$eb$p[,by];
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+    padj = p.adjust(p,method=adjust.method);
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+
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+    if(group==0){
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+        srt = order(abs(tb[,by]),decreasing=TRUE)
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+    } else if(group==1){
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+        srt = order((tb[,by]),decreasing=TRUE)
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+    } else if(group==2){
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+        srt = order((tb[,by]),decreasing=FALSE)
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+    }
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+    valid = which(rowSums(1-obj$z)>=quantile(rowSums(1-obj$z),p=eff,na.rm=TRUE))
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+    srt = srt[which(srt%in%valid)][1:number]
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+    
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+    mat = cbind(tb[,coef],p)
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+    mat = cbind(mat,padj)
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+    rownames(mat) = tx;
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+    mat = mat[srt,]
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+    
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+    nm = c(colnames(tb)[coef],"pValue","adjPvalue")
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+    colnames(mat) = nm
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+
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+
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+    if(!is.null(output)){
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+        nm = c("Taxa",nm)
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+        mat2 = cbind(rownames(mat),mat)
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+        mat2 = rbind(nm,mat2)
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+        write(t(mat2),ncolumns=ncol(mat2),file=output,sep="\t")
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+    } else{
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+        return(as.data.frame(mat))
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+    }
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+}
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+MRfisher<-function(obj,cl,mat=FALSE){
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+    if(mat==FALSE){
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+        x = MRcounts(obj)>0;
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+    } else {
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+        x = obj>0;
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+    }
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+    nrows= nrow(x);
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+	if(is.null(rownames(x))){rownames(x)=1:nrows}
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+    
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+    res = sapply(1:nrows,function(i){
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+        tbl = table(1-x[i,],cl)
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+        if(sum(dim(tbl))!=4){
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+            tbl = array(0,dim=c(2,2));
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+            tbl[1,1] = sum(x[i,cl==unique(cl)[1]])
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+            tbl[1,2] = sum(x[i,cl==unique(cl)[2]])
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+            tbl[2,1] = sum(cl==unique(cl)[1])-tbl[1,1]
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+            tbl[2,2] = sum(cl==unique(cl)[2])-tbl[1,2]
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+        }
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+        ft <- fisher.test(tbl, workspace = 8e6, alternative = "two.sided", conf.int = T)
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+        list(p=ft$p.value,o=ft$estimate,cl=ft$conf.int[1],cu=ft$conf.int[2])
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+    })
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+    
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+    dat = data.frame(as.matrix(t(res)))
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+    rownames(dat) = rownames(x)
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+    colnames(dat) = c("pvalues","oddsratio","lower","upper")
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+    return(dat)
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+}
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+MRfulltable<-function(obj,by=2,coef=NULL,number=10,taxa=obj$taxa,adjust.method="fdr",group=0,eff=0,output=NULL){
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+    
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+    tb = obj$fit$coefficients
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+    tx = as.character(taxa);
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+    
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+    for (nm in unique(tx)) {
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+        ii=which(tx==nm)
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+        tx[ii]=paste(tx[ii],seq_along(ii),sep=":")
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+    }
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+
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+    if(is.null(coef)){coef = 1:ncol(tb);}
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+
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+    p=obj$eb$p[,by];
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+    padj = p.adjust(p,method=adjust.method);
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+    
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+    groups = factor(obj$fit$design[,by])
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+    cnts = obj$counts;
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+    yy = cnts>0;
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+    
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+    pa = matrix(unlist(MRfisher(obj$counts,groups,mat=TRUE)),ncol=4)
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+    
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+    np0 = rowSums(yy[,groups==unique(groups)[1]]);
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+    np1 = rowSums(yy[,groups==unique(groups)[2]]);
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+
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+    nc0 = rowSums(cnts[,groups==unique(groups)[1]]);
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+    nc1 = rowSums(cnts[,groups==unique(groups)[2]]);
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+
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+    if(group==0){
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+        srt = order(abs(tb[,by]),decreasing=TRUE)
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+    } else if(group==1){
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+        srt = order((tb[,by]),decreasing=TRUE)
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+    } else if(group==2){
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+        srt = order((tb[,by]),decreasing=FALSE)
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+    }
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+    valid = which(rowSums(1-obj$z)>=quantile(rowSums(1-obj$z),p=eff,na.rm=TRUE))
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+    srt = srt[which(srt%in%valid)][1:number]
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+
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+    mat = cbind(np0,np1)
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+    mat = cbind(mat,nc0)
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+    mat = cbind(mat,nc1)
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+    mat = cbind(mat,pa)
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+    mat = cbind(mat,tb[,coef])
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+    mat = cbind(mat,p)
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+    mat = cbind(mat,padj)
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+    rownames(mat) = tx;
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+    mat = mat[srt,]
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+
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+    nm = c(paste("+samples in group",unique(groups)[1]),paste("+samples in group",unique(groups)[2]),
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+    paste("counts in group",unique(groups)[1]),paste("counts in group",unique(groups)[2]),c("fisher.p","oddsRatio","lower","upper"),
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+    colnames(tb)[coef],"pValue","adjPvalue")
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+    colnames(mat) = nm
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+
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+    if(!is.null(output)){
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+        nm = c("Taxa",nm)
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+        mat2 = cbind(rownames(mat),mat)
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+        mat2 = rbind(nm,mat2)
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+        write(t(mat2),ncolumns=ncol(mat2),file=output,sep="\t")
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+    } else{
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+        return(as.data.frame(mat))
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+    }
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+}
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+MRtable<-function(obj,by=2,coef=NULL,number=10,taxa=obj$taxa,adjust.method="fdr",group=0,output=NULL){
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+    tb = obj$fit$coefficients
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+    tx = as.character(taxa);
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+    
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+    for (nm in unique(tx)) {
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+        ii=which(tx==nm)
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+        tx[ii]=paste(tx[ii],seq_along(ii),sep=":")
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+    }
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+
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+    if(is.null(coef)){coef = 1:ncol(tb);}
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+
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+    p=obj$eb$p[,by];
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+    padj = p.adjust(p,method=adjust.method);
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+    
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+    groups = factor(obj$fit$design[,by])
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+    cnts = obj$counts;
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+    yy = cnts>0;
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+    
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+    np0 = rowSums(yy[,groups==unique(groups)[1]]);
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+    np1 = rowSums(yy[,groups==unique(groups)[2]]);
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+
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+    nc0 = rowSums(cnts[,groups==unique(groups)[1]]);
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+    nc1 = rowSums(cnts[,groups==unique(groups)[2]]);
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+
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+    if(group==0){
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+        srt = order(abs(tb[,by]),decreasing=TRUE)[1:number]
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+    } else if(group==1){
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+        srt = order((tb[,by]),decreasing=TRUE)[1:number]
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+    } else if(group==2){
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+        srt = order((tb[,by]),decreasing=FALSE)[1:number]
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+    }
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+
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+    mat = cbind(np0,np1)
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+    mat = cbind(mat,nc0)
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+    mat = cbind(mat,nc1)
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+    mat = cbind(mat,tb[,coef])
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+    mat = cbind(mat,p)
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+    mat = cbind(mat,padj)
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+    rownames(mat) = tx;
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+    mat = mat[srt,]
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+
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+    nm = c(paste("+samples in group",unique(groups)[1]),paste("+samples in group",unique(groups)[2]),
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+    paste("counts in group",unique(groups)[1]),paste("counts in group",unique(groups)[2]),
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+    colnames(tb)[coef],"pValue","adjPvalue")
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+    colnames(mat) = nm
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+
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+    if(!is.null(output)){
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+        nm = c("Taxa",nm)
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+        mat2 = cbind(rownames(mat),mat)
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+        mat2 = rbind(nm,mat2)
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+        write(t(mat2),ncolumns=ncol(mat2),file=output,sep="\t")
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+    } else{
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+        return(as.data.frame(mat))
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+    }
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+}
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+#' Aggregates the counts to a particular classification.
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+#'
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+#' This function takes an eSet object of data at a particular level with feature information allowing
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+#' for aggregation of counts to a particular level. This method assumes taxa begin at the highest level and continue to the current level.
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+#'
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+#' @param obj An eSet object of count data.
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+#' @param lvl The level to go up (numeric, 1,2,3).
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+#' @param split The way character strings in taxa in the obj are split.
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+#' @return Updated eSet object with counts aggregated to the various taxanomic levels.
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+#'
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+#' @name aggregateM
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+aggregateM <-
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+function(obj,taxa,lvl,split=";"){
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+
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+	tmp<-strsplit(taxa,split=split);
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+	nrows = length(taxa);
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+    cnts  = MRcounts(obj)
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+	
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+	maxName=max(sapply(tmp,length))
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+	
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+	featureMat<-array("NA",dim=c(nrows,maxName));
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+	
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+	for(i in 1:nrows){
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+		for(j in 1:length(tmp[[i]])){
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+			featureMat[i,j]=tmp[[i]][j]
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+			}
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+	}
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+	
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+	tt = sapply(1:nrows,function(i){
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+		   t = featureMat[i,1]
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+		   for(j in 2:lvl){
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+				t = paste(t,featureMat[i,j],sep=";")
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+		   }
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+		   t})
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+	
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+	newTaxa<-unique(tt);
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+    newCountMat<-array(0,dim=c(length(newTaxa),ncol(cnts)));
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+
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+    colnames(newCountMat)<-sampleNames(obj)
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+	rownames(newCountMat)<-newTaxa;	
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+	
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+	for(i in 1:length(newTaxa)){
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+		if(length(which(tt==newTaxa[i]))<2){
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+			newCountMat[rownames(newCountMat)==newTaxa[i],]=as.matrix(cnts[which(tt==newTaxa[i]),],nr=1)
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+		}else{
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+			newCountMat[rownames(newCountMat)==newTaxa[i],]=colSums(cnts[which(tt==newTaxa[i]),])
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+		}
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+	}
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+	
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+	taxa<-newTaxa;
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+	assayData(obj)<-newCountMat;
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+	validObject(obj)
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+
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+	return(obj)
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+}
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+setClass("MRexperiment", contains=c("eSet"), representation=representation(expSummary = "environment"),prototype = prototype( new( "VersionedBiobase",versions = c(classVersion("eSet"),MRexperiment = "1.0.0" ))))
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+            
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+setMethod("[", "MRexperiment", function (x, i, j, ..., drop = FALSE) {
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+        obj= callNextMethod()
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+        if(!missing(j)){
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+            obj@expSummary = new("environment",expSummary=as(expSummary(x)[j,1:2,...,drop=drop],"AnnotatedDataFrame"),cumNormStat=x@expSummary$cumNormStat)
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+            for(i in 1:length(pData(obj))){
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+                pData(obj)[,i] = factor(pData(obj)[,i])
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+            }
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+        }
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+        obj
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+})
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+
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+newMRexperiment <- function(counts, phenoData=NULL, featureData=NULL,libSize=NULL, normFactors=NULL) {
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+    counts= as.matrix(counts)
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+
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+    if( is.null( featureData ) )
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+      featureData <- annotatedDataFrameFrom(counts, byrow=TRUE)
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+    if( is.null( phenoData ) )
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+      phenoData   <- annotatedDataFrameFrom(counts, byrow=FALSE)
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+    if( is.null( libSize ) )
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+      libSize <- as.matrix(colSums(counts))
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+      rownames(libSize) = colnames(counts)
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+    if( is.null( normFactors ) ){
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+      normFactors <- as.matrix(rep( NA_real_, length(libSize) ))
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+      rownames(normFactors) = rownames(libSize)
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+    }
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+
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+    obj <-new("MRexperiment", assayData = assayDataNew("environment",counts=counts),phenoData = phenoData,featureData = featureData ,expSummary = new("environment",expSummary=annotatedDataFrameFrom(counts,byrow=FALSE),cumNormStat=NULL))
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+    obj@expSummary$expSummary$libSize = libSize;
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+    obj@expSummary$expSummary$normFactors=normFactors;
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+        
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+    validObject( obj )
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+    obj
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+}
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+
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+
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+setValidity( "MRexperiment", function( object ) {
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+    if( is.null(assayData(object)$counts))
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+        return( "There are no counts!" )
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+#    if( ncol(MRcounts(object)) != length(normFactors(object)))
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+#        return( "Experiment summary got hacked!" )
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+#    if( ncol(MRcounts(object)) != length(libSize(object)))
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+#        return( "Experiment summary got hacked!" )
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+    TRUE
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+} )
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+
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+MRcounts <- function( obj ,norm=FALSE) {
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+   stopifnot( is( obj, "MRexperiment" ) )
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+   if(!norm){
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+    return(assayData(obj)[["counts"]])
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+   }
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+   if(any(is.na(normFactors(obj)))){
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+    return("Calculate the normalization factors first!")
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+   } else{
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+    sweep(assayData(obj)[["counts"]],2,as.vector(unlist(normFactors(obj)))/1000,"/")
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+   }
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+}
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+
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+posterior.probs <- function( obj ) {
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+   stopifnot( is( obj, "MRexperiment" ) )
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+   assayData(obj)[["z"]]
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+}   
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+
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+normFactors <- function( obj ) {
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+   stopifnot( is( obj, "MRexperiment" ) )
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+   nf <- pData(obj@expSummary$expSummary)[["normFactors"]]
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+   nf
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+}   
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+
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+libSize<-function(obj){
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+   stopifnot( is( obj, "MRexperiment" ) )
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+   ls <- pData(obj@expSummary$expSummary)[["libSize"]]
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+   ls
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+}
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+
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+expSummary<-function(obj){
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+  stopifnot( is( obj, "MRexperiment" ) )
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+  pData(obj@expSummary$expSummary)
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+}
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+#' Cumulative sum scaling factors.
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+#'
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+#' Calculates each column's quantile and calculates the sum up to and including that quantile.
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+#'
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+#' @param jobj An eSet object.
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+#' @param p The pth quantile.
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+#' @return Vector of the sum up to and including a sample's pth quantile
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+#'
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+#' @name cumNorm
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+#' @seealso \code{\link{fitZig}}
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+cumNorm <-
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+function(obj,p=cumNormStat(obj)){
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+	x = MRcounts(obj)
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+	xx=x
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+	xx[x==0] <- NA
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+		
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+	qs=matrixStats::colQuantiles(xx,p=p,na.rm=TRUE)
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+		
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+	newMat<-sapply(1:ncol(xx), function(i) {
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+		   xx=(x[,i]-.Machine$double.eps)
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+		   sum(xx[xx<=qs[i]])
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+		   })
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+	names(newMat)<- colnames(x)
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+    pData(obj@expSummary$expSummary)$normFactors = as.data.frame(newMat)
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+    validObject(obj)
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+}
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+#' Cumulative sum scaling factors.
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+#'
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+#' Calculates each column's quantile and calculates the sum up to and including that quantile.
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+#'
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+#' @param jobj An eSet object.
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+#' @param p The pth quantile.
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+#' @return jobj An updated eSet object with normalized counts
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+#'
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+#' @name cumNormMat
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+#' @seealso \code{\link{fitZig}} \code{\link{cumNorm}}
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+cumNormMat <-
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+function(obj,p= cumNormStat(obj)){
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+####################################################################################
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+#   Calculates each column's quantile
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+#    and calculated the sum up to and
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+#    including that quantile.
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+####################################################################################
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+    x=MRcounts(obj)
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+    xx=x
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+	xx[x==0] <- NA
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+	
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+	qs=matrixStats::colQuantiles(xx,p=p,na.rm=TRUE)
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+	
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+	newMat<-sapply(1:ncol(xx), function(i) {
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+				   xx=(x[,i]-.Machine$double.eps)
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+				   sum(xx[xx<=qs[i]])
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+				   })
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+	x<-sweep(x,2,newMat/1000,"/")
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+	return(x)
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+}
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+#' Cumulative normalization statistic.
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+#'
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+#' @param obj An eSet object.
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+#' @param pFlag Whether or not to plot the reference.
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+#' @param rel Relative difference of rel percent.
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+#' @return P-value for which to cumulative normalize.
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+#'
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+#' @name cumNormStat
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+#' @seealso \code{\link{fitZig}} \code{\link{cumNorm}}
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+cumNormStat <-
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+function(obj,pFlag = FALSE,rel=.1,qFlag = TRUE, ...){
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+    
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+	mat = MRcounts(obj);
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+	smat = sapply(1:ncol(mat),function(i){sort(mat[,i],decreasing=FALSE)})
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+	ref  = rowMeans(smat);
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+	
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+	yy = mat;
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+	yy[yy==0]=NA;
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+	
20
+	ncols = ncol(mat);
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+	refS = sort(ref);
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+    
23
+    k = which(refS>0)[1]
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+    lo = (length(refS)-k+1)
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+
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+    if(qFlag == TRUE){
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+        diffr = sapply(1:ncols,function(i){
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+            refS[k:length(refS)] - quantile(yy[,i],p=seq(0,1,length.out=lo),na.rm=TRUE)
29
+        })
30
+    }
31
+    if(qFlag == FALSE){
32
+        diffr = sapply(1:ncols,function(i){
33
+            refS[k:length(refS)] - approx(yy[,i],n=lo)$y
34
+        })
35
+    }
36
+	diffr2 = matrixStats::rowMedians(abs(diffr))
37
+	if(pFlag ==TRUE){
38
+        plot(abs(diff(diffr2[diffr2>0]))/diffr2[diffr2>0][-1],type="h",ylab="Relative difference for reference",xaxt="n",...)
39
+		abline(h=rel)
40
+        axis(1,at=seq(0,length(diffr2),length.out=5),labels = seq(0,1,length.out=5))
41
+	}
42
+    x = which(abs(diff(diffr2))/diffr2[-1]>rel)[1] / length(diffr2)
43
+    obj@expSummary$cumNormStat = x;
44
+	return(x)
45
+}
0 46
\ No newline at end of file
1 47
new file mode 100644
... ...
@@ -0,0 +1,56 @@
1
+#' Compute the Maximization step calculation for features still active.
2
+#'
3
+#' Maximization step is solved by weighted least squares. The function also computes counts residuals.
4
+#'
5
+#' Maximum-likelihood estimates are approximated using the EM algorithm where we treat mixture membership $\deta_{ij}$ = 1 if $y_{ij}$
6
+#' is generated from the zero point mass as latent indicator variables. The density is defined as $f_zig(y_{ij} = \pi_j(S_j) \cdot f_{0}(y_{ij}) 
7
+#' +(1-\pi_j (S_j))\cdot f_{count}(y_{ij};\mu_i,\sigma_i^2)$.
8
+#' The log-likelihood in this extended model is
9
+#' $(1−\delta_{ij}) \log f_{count}(y;\mu_i,\sigma_i^2 )+\delta_{ij} \log \pi_j(s_j)+(1−\delta_{ij})\log (1−\pi_j (sj))$.
10
+#' The responsibilities are defined as $z_{ij} = pr(\delta_{ij}=1 | data)$.
11
+#'
12
+#' @param z Matrix (m x n) of estimate responsibilities (probabilities that a count comes from a spike distribution at 0).
13
+#' @param y Matrix (m x n) of count observations.
14
+#' @param mmCount Model matrix for the count distribution.
15
+#' @param stillActive Boolean vector of size M, indicating whether a feature converged or not.
16
+#' @param fit2 Previous fit of the count model.
17
+#' @return Update matrix (m x n) of estimate responsibilities (probabilities that a count comes from a spike distribution at 0).
18
+#'
19
+#' @name doCountMStep
20
+#' @seealso \code{\link{fitZig}}
21
+doCountMStep <-
22
+function(z, y, mmCount, stillActive,fit2=NULL){
23
+
24
+	if (is.null(fit2)){
25
+	fit=limma::lmFit(y[stillActive,],mmCount,weights = (1-z[stillActive,]))
26
+	countCoef = fit$coefficients
27
+	countMu=tcrossprod(countCoef, mmCount)
28
+	residuals=sweep((y[stillActive,,drop=FALSE]-countMu),1,fit$sigma,"/")
29
+
30
+	dat = list(fit = fit, residuals = residuals)
31
+	return(dat)
32
+	} else {
33
+
34
+	residuals = fit2$residuals
35
+	fit2 = fit2$fit
36
+
37
+	fit=limma::lmFit(y[stillActive,,drop=FALSE],mmCount,weights = (1-z[stillActive,,drop=FALSE]))
38
+
39
+	
40
+	fit2$coefficients[stillActive,] = fit$coefficients
41
+	fit2$stdev.unscaled[stillActive,]=fit$stdev.unscaled
42
+	fit2$sigma[stillActive] = fit$sigma
43
+	fit2$Amean[stillActive] = fit$Amean
44
+	fit2$df[stillActive]    = fit$df
45
+	fit2$df.residual[stillActive]    = fit$df.residual
46
+
47
+	countCoef = fit$coefficients
48
+	countMu=tcrossprod(countCoef, mmCount)
49
+	r=sweep((y[stillActive,,drop=FALSE]-countMu),1,fit$sigma,"/")
50
+	residuals[stillActive,]=r
51
+
52
+	dat = list(fit = fit2, residuals=residuals)
53
+
54
+	return(dat)
55
+	}
56
+}
0 57
new file mode 100644
... ...
@@ -0,0 +1,30 @@
1
+#' Compute the Expectation step.
2
+#'
3
+#' Estimates the responsibilities $z_{ij} = \frac{\pi_j \cdot I_{0}(y_{ij}}{\pi_j \cdot I_{0}(y_{ij} + (1-\pi_j) \cdot f_{count}(y_{ij}}
4
+#'
5
+#' Maximum-likelihood estimates are approximated using the EM algorithm where we treat mixture membership $\deta_{ij}$ = 1 if $y_{ij}$
6
+#' is generated from the zero point mass as latent indicator variables. The density is defined as $f_zig(y_{ij} = \pi_j(S_j) \cdot f_{0}(y_{ij}) 
7
+#' +(1-\pi_j (S_j))\cdot f_{count}(y_{ij};\mu_i,\sigma_i^2)$.
8
+#' The log-likelihood in this extended model is
9
+#' $(1−\delta_{ij}) \log f_{count}(y;\mu_i,\sigma_i^2 )+\delta_{ij} \log \pi_j(s_j)+(1−\delta_{ij})\log (1−\pi_j (sj))$.
10
+#' The responsibilities are defined as $z_{ij} = pr(\delta_{ij}=1 | data)$.
11
+#'
12
+#' @param countResiduals Residuals from the count model.
13
+#' @param zeroResiduals Residuals from the zero model.
14
+#' @param zeroIndices Index (matrix m x n) of counts that are zero/non-zero.
15
+#' @return Updated matrix (m x n) of estimate responsibilities (probabilities that a count comes from a spike distribution at 0).
16
+#'
17
+#' @name doEStep
18
+#' @seealso \code{\link{fitZig}}
19
+doEStep <-
20
+function(countResiduals,  zeroResiduals, zeroIndices)
21
+{
22
+	pi_prop=getPi(zeroResiduals)
23
+	w1=sweep(zeroIndices, 2, pi_prop, FUN="*")
24
+
25
+	countDensity=getCountDensity(countResiduals)
26
+	w2=sweep(countDensity, 2, 1-pi_prop, FUN="*")
27
+	z=w1/(w1+w2)
28
+	z[!zeroIndices]=0
29
+	z
30
+}
0 31
new file mode 100644
... ...
@@ -0,0 +1,39 @@
1
+#' Compute the zero Maximization step.
2
+#'
3
+#' Performs Maximization step calculation for the mixture components. Uses least squares to fit the parameters of the mean of the logistic distribution.
4
+#' $$
5
+#' pi_j = \sum_i^M \frac{1}{M}z_{ij}
6
+#' $$
7
+#' Maximum-likelihood estimates are approximated using the EM algorithm where we treat mixture membership $\deta_{ij}$ = 1 if $y_{ij}$
8
+#' is generated from the zero point mass as latent indicator variables. The density is defined as $f_zig(y_{ij} = \pi_j(S_j) \cdot f_{0}(y_{ij}) 
9
+#' +(1-\pi_j (S_j))\cdot f_{count}(y_{ij};\mu_i,\sigma_i^2)$.
10
+#' The log-likelihood in this extended model is
11
+#' $(1−\delta_{ij}) \log f_{count}(y;\mu_i,\sigma_i^2 )+\delta_{ij} \log \pi_j(s_j)+(1−\delta_{ij})\log (1−\pi_j (sj))$.
12
+#' The responsibilities are defined as $z_{ij} = pr(\delta_{ij}=1 | data)$.
13
+#'
14
+#' @param z Matrix (m x n) of estimate responsibilities (probabilities that a count comes from a spike distribution at 0).
15
+#' @param zeroIndices Index (matrix m x n) of counts that are zero/non-zero.
16
+#' @param mmZero The zero model, the model matrix to account for the change in the number of OTUs observed as a linear effect of the depth of coverage.
17
+#' @return List of the zero fit (zero mean model) coefficients, variance - scale parameter (scalar), and normalized residuals of length sum(zeroIndices).
18
+#'
19
+#' @name doZeroMStep
20
+#' @seealso \code{\link{fitZig}}
21
+doZeroMStep <-
22
+function(z, zeroIndices, mmZero)
23
+{
24
+	pi=sapply(1:ncol(zeroIndices), function(j) {
25
+		if (sum(zeroIndices[,j])==0){
26
+			return(1e-6)
27
+		}
28
+
29
+		tmp=mean(z[zeroIndices[,j],j],na.rm=TRUE)
30
+		ifelse(tmp<=1e-8, 1e-8, ifelse(tmp>=1-(1e-8),1-(1e-8),tmp)) 
31
+		})
32
+	zeroLM=lm.fit(mmZero, qlogis(pi))
33
+	zeroCoef=zeroLM$coef
34
+
35
+	r=zeroLM$residuals
36
+	sigma=sd(r)+(1e-3)
37
+
38
+	list(zeroCoef=zeroCoef, sigma=sigma, residuals=r/sigma)
39
+}
0 40
new file mode 100644
... ...
@@ -0,0 +1,26 @@
1
+#' export the normalized eSet dataset as a matrix.
2
+#'
3
+#' This function allows the user to take the normalized dataset or counts and output
4
+#' the dataset to the user's workspace as a tab-delimited file, etc.
5
+#'
6
+#' @param jobj An eSet object with count data.
7
+#' @param output Output file name.
8
+#' @return NA
9
+#'
10
+#' @name export_mat
11
+#' @aliases exportMatrix
12
+#' @seealso \code{\link{cumNorm}}
13
+#' @examples
14
+#' export_mat(jobj,output="~/Desktop/normMatrix.tsv");
15
+
16
+exportMat <-
17
+function(mat,output="~/Desktop/matrix.tsv"){
18
+	matrix = mat;
19
+	
20
+	mat = array(NA,dim=c((nrow(matrix)+1),(ncol(matrix)+1)));
21
+	mat[1,2:ncol(mat)] = colnames(matrix);
22
+	mat[2:nrow(mat),2:ncol(mat)] = matrix;
23
+    mat[2:nrow(mat),1] = rownames(matrix);
24
+    mat[1,1] = "Taxa and Samples"
25
+	write(t(mat),file=output,sep="\t",ncolumns=ncol(mat))	
26
+}
0 27
new file mode 100644
... ...
@@ -0,0 +1,37 @@
1
+#' Various statistics of the count data.
2
+#'
3
+#' A matrix of values for each sample. The matrix consists of sample ids, the sample scaling factor, quantile value, and the number of number of features.
4
+#'
5
+#' @param obj An eSet object with count data.
6
+#' @param p Quantile value to calculate the scaling factor and quantiles for the various samples.
7
+#' @param output Output file name.
8
+#' @return None.
9
+#'
10
+#' @name export_stats
11
+#' @seealso \code{\link{cumNorm}} \code{\link{quantile}}
12
+#' @examples
13
+#' export_stats(obj,p=1,output="~/Desktop/obj-stats.tsv")
14
+
15
+exportStats <-
16
+function(obj,p= cumNormStat(obj),output="~/Desktop/res.stats.tsv"){
17
+
18
+			xx=MRcounts(obj)
19
+			xx[xx==0]=NA
20
+			qs=matrixStats::colQuantiles(xx,p=p,na.rm=TRUE)
21
+			s95 = colSums(xx<=qs,na.rm=TRUE)
22
+			xx[xx>0] = 1;
23
+			xx[is.na(xx)]=0
24
+			
25
+			newMat <- array(NA,dim=c(4,ncol(xx)+1));
26
+			newMat[1,1] = "Subject"
27
+			newMat[2,1] = "Scaling factor"
28
+			newMat[3,1] = "Quantile value"
29
+			newMat[4,1] = "Number of features"
30
+
31
+			newMat[1,2:ncol(newMat)]<-sampleNames(obj);
32
+			newMat[2,2:ncol(newMat)]<-s95;
33
+			newMat[3,2:ncol(newMat)]<-qs;
34
+			newMat[4,2:ncol(newMat)]<-colSums(xx)
35
+
36
+			write((newMat),file = output,sep = "\t",ncolumns = 4);
37
+}
0 38
new file mode 100644
... ...
@@ -0,0 +1,112 @@
1
+#' Computes the weighted fold-change estimates and t-statistics.
2
+#'
3
+#' Wrapper to actually run the Expectation-maximization algorithm and estimate $f_{count}$ fits.
4
+#' Maximum-likelihood estimates are approximated using the EM algorithm where we treat mixture membership $\deta_{ij}$ = 1 if $y_{ij}$
5
+#' is generated from the zero point mass as latent indicator variables. The density is defined as $f_zig(y_{ij} = \pi_j(S_j) \cdot f_{0}(y_{ij}) 
6
+#' +(1-\pi_j (S_j))\cdot f_{count}(y_{ij};\mu_i,\sigma_i^2)$.
7
+#' The log-likelihood in this extended model is
8
+#' $(1−\delta_{ij}) \log f_{count}(y;\mu_i,\sigma_i^2 )+\delta_{ij} \log \pi_j(s_j)+(1−\delta_{ij})\log (1−\pi_j (sj))$.
9
+#' The responsibilities are defined as $z_{ij} = pr(\delta_{ij}=1 | data)$.
10
+#'
11
+#' @param obj An eSet object with count data.
12
+#' @param mod The model for the count distribution.
13
+#' @param s95 A vector of size M of the scaling values to be included in the model.
14
+#' @param zeroMod The zero model, the model to account for the change in the number of OTUs observed as a linear effect of the depth of coverage.
15
+#' @param useS95offset Boolean, whether to include the default scaling parameters in the model or not.
16
+#' @param control The settings for fitZig. 
17
+#' @param s The raw total counts for the various samples.
18
+#' @return The fits, posterior probabilities, posterior probabilities used at time of convergence for each feature, ebayes (limma object) fit, among other data. 
19
+#'
20
+#' @name fitZig
21
+#' @seealso \code{\link{cumNorm}} \code{\link{zigControl}}
22
+#' @examples
23
+#' model = model.matrix(~1+type+log2(s95/1000+1))
24
+#' res = fitZig(obj = obj,mod=mod,useS95offset=FALSE)
25
+fitZig <-
26
+function(obj,mod,zeroMod=NULL,useS95offset=TRUE,control=zigControl()){
27
+
28
+# Initialization
29
+	tol = control$tol;
30
+	maxit     = control$maxit;
31
+	verbose   = control$verbose;
32
+	
33
+    stopifnot( is( obj, "MRexperiment" ) )
34
+    if(any(is.na(normFactors(obj)))){return("At least one NA normalization factors")}
35
+	
36
+	y = MRcounts(obj)
37
+	nc = ncol(y) #nsamples
38
+	nr = nrow(y) #nfeatures
39
+
40
+	zeroIndices=(y==0)
41
+	z=matrix(0,nrow=nr, ncol=nc)
42
+	z[zeroIndices]=0.5
43
+	zUsed = z;
44
+	curIt=0
45
+	nllOld=rep(Inf, nr)
46
+	nll=rep(Inf, nr)
47
+	nllUSED=nll
48
+	stillActive=rep(TRUE, nr);
49
+	stillActiveNLL=rep(1, nr)
50
+	
51
+# Normalization step
52
+		Nmatrix = log2(y+1)
53
+		
54
+		
55
+# Initializing the model matrix
56
+	if(useS95offset==TRUE){
57
+		mmCount=cbind(mod,log2(as.matrix(normFactors(obj))/1000 +1))}
58
+	else{ 
59
+        mmCount=mod
60
+    }
61
+
62
+	if(is.null(zeroMod)){
63
+        mmZero=model.matrix(~1+log(libSize(obj)))
64
+    } else{ 
65
+        mmZero=zeroMod 
66
+    }
67
+	
68
+	modRank=ncol(mmCount);
69
+# E-M Algorithm
70
+		while(any(stillActive) && curIt<maxit) {
71
+	
72
+# M-step for count density (each feature independently)
73
+			if(curIt==0){
74
+				fit=doCountMStep(z, Nmatrix, mmCount, stillActive);
75
+			} else {
76
+				fit=doCountMStep(z, Nmatrix, mmCount, stillActive,fit)
77
+			}
78
+
79
+# M-step for zero density (all features together)
80
+			zeroCoef = doZeroMStep(z, zeroIndices, mmZero)
81
+			
82
+# E-step
83
+			z = doEStep(fit$residuals, zeroCoef$residuals, zeroIndices)
84
+			zzdata<-getZ(z,zUsed,stillActive,nll,nllUSED);
85
+			zUsed = zzdata$zUsed;
86
+# NLL 
87
+			nll = getNegativeLogLikelihoods(z, fit$residuals, zeroCoef$residuals)
88
+			eps = getEpsilon(nll, nllOld)
89
+			active = isItStillActive(eps, tol,stillActive,stillActiveNLL,nll)
90
+			stillActive = active$stillActive;
91
+			stillActiveNLL = active$stillActiveNLL;
92
+			if(verbose==TRUE){
93
+				cat(sprintf("it=%2d, nll=%0.2f, log10(eps+1)=%0.2f, stillActive=%d\n", curIt, mean(nll,na.rm=TRUE), log10(max(eps,na.rm=TRUE)+1), sum(stillActive)))
94
+			}
95
+			nllOld=nll
96
+			curIt=curIt+1
97
+    
98
+			if(sum(rowSums((1-z)>0)<=modRank,na.rm=TRUE)>0){
99
+				k = which(rowSums((1-z)>0)<=modRank)
100
+				stillActive[k] = FALSE;
101
+				stillActiveNLL[k] = nll[k]
102
+			}
103
+		}
104
+	
105
+        assayData(obj)[["z"]] <- z
106
+        assayData(obj)[["zUsed"]] <- zUsed
107
+
108
+		eb=limma::ebayes(fit$fit)
109
+		dat = list(fit=fit$fit,countResiduals=fit$residuals,
110
+				   z=z,eb=eb,taxa=as.vector(unlist(fData(obj)[1])),counts=y,zeroMod =mmZero,stillActive=stillActive,stillActiveNLL=stillActiveNLL,zeroCoef=zeroCoef)
111
+		return(dat)
112
+	}
0 113
new file mode 100644
... ...
@@ -0,0 +1,20 @@
1
+#' Compute the value of the count density function from the count model residuals.
2
+#'
3
+#' Calculate density values from a normal: $f(x) = 1/(\sqrt (2 \pi ) \sigma ) e^-((x - \mu )^2/(2 \sigma^2))$.
4
+#' Maximum-likelihood estimates are approximated using the EM algorithm where we treat mixture membership $\deta_{ij}$ = 1 if $y_{ij}$
5
+#' is generated from the zero point mass as latent indicator variables. The density is defined as $f_zig(y_{ij} = \pi_j(S_j) \cdot f_{0}(y_{ij}) 
6
+#' +(1-\pi_j (S_j))\cdot f_{count}(y_{ij};\mu_i,\sigma_i^2)$.
7
+#' The log-likelihood in this extended model is
8
+#' $(1−\delta_{ij}) \log f_{count}(y;\mu_i,\sigma_i^2 )+\delta_{ij} \log \pi_j(s_j)+(1−\delta_{ij})\log (1−\pi_j (sj))$.
9
+#' The responsibilities are defined as $z_{ij} = pr(\delta_{ij}=1 | data)$.
10
+#'
11
+#' @param residuals Residuals from the count model.
12
+#' @param log Whether or not we are calculating from a log-normal distribution.
13
+#' @return Density values from the count model residuals.
14
+#'
15
+#' @name getCountDensity
16
+#' @seealso \code{\link{fitZig}}
17
+getCountDensity <-
18
+function(residuals, log=FALSE){
19
+	dnorm(residuals,log=log)
20
+}
0 21
new file mode 100644
... ...
@@ -0,0 +1,18 @@
1
+#' Calculate the relative difference between iterations of the negative log-likelihoods.
2
+#'
3
+#' Maximum-likelihood estimates are approximated using the EM algorithm where we treat mixture membership $\deta_{ij}$ = 1 if $y_{ij}$
4
+#' is generated from the zero point mass as latent indicator variables. The log-likelihood in this extended model is
5
+#' $(1−\delta_{ij}) \log f_{count}(y;\mu_i,\sigma_i^2 )+\delta_{ij} \log \pi_j(s_j)+(1−\delta_{ij})\log (1−\pi_j (sj))$.
6
+#' The responsibilities are defined as $z_{ij} = pr(\delta_{ij}=1 | data)$.
7
+#'
8
+#' @param nll Vector of size M with the current negative log-likelihoods.
9
+#' @param nllOld Vector of size M with the previous iterations negative log-likelihoods.
10
+#' @return Vector of size M of the relative differences between the previous and current iteration nll.
11
+#'
12
+#' @name getEpsilon
13
+#' @seealso \code{\link{fitZig}}
14
+getEpsilon <-
15
+function(nll, nllOld){
16
+	eps=(nllOld-nll)/nllOld
17
+	ifelse(!is.finite(nllOld), Inf, eps)
18
+}
0 19
new file mode 100644
... ...
@@ -0,0 +1,23 @@
1
+#' Calculate the negative log-likelihoods for the various features given the residuals.
2
+#'
3
+#' Maximum-likelihood estimates are approximated using the EM algorithm where we treat mixture membership $\deta_{ij}$ = 1 if $y_{ij}$
4
+#' is generated from the zero point mass as latent indicator variables. The log-likelihood in this extended model is
5
+#' $(1−\delta_{ij}) \log f_{count}(y;\mu_i,\sigma_i^2 )+\delta_{ij} \log \pi_j(s_j)+(1−\delta_{ij})\log (1−\pi_j (sj))$.
6
+#' The responsibilities are defined as $z_{ij} = pr(\delta_{ij}=1 | data and current  values)$.
7
+#'
8
+#' @param z Matrix (m x n) of estimate responsibilities (probabilities that a count comes from a spike distribution at 0).
9
+#' @param countResiduals Residuals from the count model.
10
+#' @param zeroResiduals Residuals from the zero model.
11
+#' @return Vector of size M of the negative log-likelihoods for the various features.
12
+#'
13
+#' @name getNegativeLogLikelihoods
14
+#' @seealso \code{\link{fitZig}}
15
+getNegativeLogLikelihoods <-
16
+function(z, countResiduals, zeroResiduals){
17
+	pi=getPi(zeroResiduals)
18
+	countDensity=getCountDensity(countResiduals, log=TRUE)
19
+	res=(1-z) * countDensity
20
+	res=res+sweep(z, 2, log(pi), FUN="*")
21
+	res=res+sweep(1-z,2,log(1-pi), FUN="*")
22
+	-rowSums(res)
23
+}
0 24
new file mode 100644
... ...
@@ -0,0 +1,15 @@
1
+#' Calculate the mixture proportions from the zero model / spike mass model residuals.
2
+#'
3
+#' F(x) = 1 / (1 + exp(-(x-m)/s)) (the CDF of the logistic distribution).
4
+#' Provides the probability that a real-valued random variable X with a given probability distribution will be found at a value less than or equal to x.
5
+#' The output are the mixture proportions for the samples given the residuals from the zero model.
6
+#'
7
+#' @param residuals Residuals from the zero model.
8
+#' @return Mixture proportions for each sample.
9
+#'
10
+#' @name getPi
11
+#' @seealso \code{\link{fitZig}}
12
+getPi <-
13
+function(residuals){
14
+	plogis(residuals)
15
+}
0 16
\ No newline at end of file
1 17
new file mode 100644
... ...
@@ -0,0 +1,24 @@
1
+#' Calculate the current Z estimate responsibilities (posterior probabilities)
2
+#'
3
+#' @param z Matrix (m x n) of estimate responsibilities (probabilities that a count comes from a spike distribution at 0).
4
+#' @param zUsed Matrix (m x n) of estimate responsibilities (probabilities that a count comes from a spike distribution at 0) that are actually used (following convergence). 
5
+#' @param stillActive A vector of size M booleans saying if a feature is still active or not.
6
+#' @param nll Vector of size M with the current negative log-likelihoods.
7
+#' @param nllUSED Vector of size M with the converged negative log-likelihoods.
8
+#' @return A list of updated zUsed and nllUSED.
9
+#'
10
+#' @name getZ
11
+#' @seealso \code{\link{fitZig}}
12
+getZ <-
13
+function(z,zUsed,stillActive,nll,nllUSED){
14
+
15
+	nllUSED[stillActive] = nll[stillActive]
16
+	k =which(nll< (nllUSED))
17
+	if(length(k)>0){
18
+		zUsed[k,]=z[k,]
19
+		nllUSED[k] = nll[k]
20
+	}
21
+	zUsed[stillActive,] = z[stillActive,]
22
+	dat = list(zUsed = zUsed,nllUSED = nllUSED)
23
+	return(dat);
24
+}
0 25
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... ...
@@ -0,0 +1,24 @@
1
+#' Function to determine if a feature is still active.
2
+#'
3
+#' In the Expectation Maximization routine features posterior probabilities routinely converge based on a tolerance threshold. This function checks
4
+#' whether or not the feature's negative log-likelihood (measure of the fit) has changed or not.
5
+#'
6
+#' @param eps Vector of size M (features) representing the relative difference between the new nll and old nll.
7
+#' @param tol The threshold tolerance for the difference
8
+#' @param stillActive A vector of size M booleans saying if a feature is still active or not.
9
+#' @param stillActiveNLL A vector of size M recording the negative log-likelihoods of the various features, updated for those still active.
10
+#' @param nll Vector of size M with the current negative log-likelihoods.
11
+#' @return None.
12
+#'
13
+#' @name isItStillActive
14
+#' @seealso \code{\link{fitZig}}
15
+
16
+isItStillActive <-
17
+function(eps, tol,stillActive,stillActiveNLL,nll){
18
+	stillActive[stillActive]=!is.finite(eps[stillActive]) | eps[stillActive]>tol
19
+	stillActive[which(is.na(eps))]=FALSE
20
+
21
+	stillActiveNLL[stillActive]=nll[stillActive]
22
+	dat = list(stillActive=stillActive,stillActiveNLL = stillActiveNLL)
23
+	return(dat)
24
+}
0 25
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... ...
@@ -0,0 +1,38 @@
1
+#' Load a count dataset associated with a study.
2
+#'
3
+#' Load a matrix of OTUs in a tab delimited format
4
+#'
5
+#' @param file Path and filename of the actual data file.
6
+#' @return An object of count data.
7
+#'
8
+#' @name load_meta
9
+#' @aliases metagenomicLoader
10
+#' @seealso \code{\link{load_phenoData}}
11
+#' @examples
12
+#' obj = load_meta("~/Desktop/testFile.tsv")
13
+load_meta <-
14
+function(file,sep="\t")
15
+{
16
+	dat2 <- read.table(file,header=FALSE,sep="\t");
17
+    # load names 
18
+	subjects <- array(0,dim=c(ncol(dat2)-1));
19
+	for(i in 1:length(subjects)) {
20
+		subjects[i] <- as.character(dat2[1,i+1]);
21
+	}
22
+	
23
+	classes <-c("character",rep("numeric",length(subjects)));
24
+	dat3 <- read.table(file,header=FALSE,skip=1,sep=sep,colClasses=classes);
25
+	
26
+	taxa<- as.matrix(dat3[,1]);
27
+
28
+	matrix <- array(0, dim=c(length(taxa),length(subjects)));
29
+	for(i in (1:length(subjects))){
30
+		matrix[,i] <- as.numeric(dat3[,i+1]);
31
+	}	
32
+	
33
+	colnames(matrix) = subjects;
34
+    rownames(matrix) = taxa;
35
+	obj <- list(counts=as.data.frame(matrix), taxa=as.data.frame(taxa))
36
+	
37
+	return(obj);
38
+}
0 39
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... ...
@@ -0,0 +1,37 @@
1
+#' Load a count dataset associated with a study set up in a Qiime format.
2
+#'
3
+#' Load a matrix of OTUs in Qiime's format
4
+#'
5
+#' @param file Path and filename of the actual data file.
6
+#' @return An object of count data.
7
+#'
8
+#' @name load_metaQ
9
+#' @aliases qiimeLoader
10
+#' @seealso \code{\link{load_meta}} \code{\link{load_phenoData}}
11
+#' @examples
12
+#' obj = load_metaQ("~/Desktop/testFile.tsv")
13
+load_metaQ <-
14
+function(file)
15
+{	
16
+	dat2 <- read.table(file,header=FALSE,sep="\t");
17
+# load names 
18
+	subjects <- array(0,dim=c(ncol(dat2)-1));
19
+	for(i in 1:length(subjects)) {
20
+		subjects[i] <- as.character(dat2[1,i+1]);
21
+	}
22
+    classes <-c("character",rep("numeric",(length(subjects)-1)),"character");
23
+	dat3 <- read.table(file,header=TRUE,sep="\t",colClasses=classes);
24
+	
25
+	taxa<- dat3[,1+length(subjects)];
26
+	taxa<-as.matrix(taxa);
27
+	
28
+	matrix <- array(0, dim=c(length(taxa),(length(subjects)-1)));
29
+	for(i in (1:(length(subjects)-1))){
30
+		matrix[,i] <- as.numeric(dat3[,i+1]);
31
+	}	
32
+	
33
+	colnames(matrix) = subjects[-length(subjects)];
34
+	obj <- list(counts=matrix, taxa=as.data.frame(taxa),otus = as.data.frame(dat3[,1]))
35
+	
36
+	return(obj);
37
+}
0 38
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... ...
@@ -0,0 +1,51 @@
1
+#' Load a clinical/phenotypic dataset associated with a study.
2
+#'
3
+#' Load a matrix of metadata associated with a study.
4
+#'
5
+#' @param file Path and filename of the actual clinical file.
6
+#' @param tran Boolean. If the covariates are along the columns and samples along the rows, then tran should equal TRUE.
7
+#' @param sep The separator for the file.
8
+#' @return The metadata as a dataframe.
9
+#'
10
+#' @name load_phenoData
11
+#' @aliases phenoData
12
+#' @seealso \code{\link{load_meta}}
13
+#' @examples
14
+#' clin = load_phenoData("~/Desktop/testFile.tsv")
15
+
16
+load_phenoData <-
17
+function(file,tran=FALSE,sep="\t")
18
+{
19
+	dat2 <- read.table(file,header=FALSE,sep=sep);
20
+
21
+# no. of subjects 
22
+	subjects <- array(0,dim=c(ncol(dat2)-1));
23
+	for(i in 1:length(subjects)) {
24
+		subjects[i] <- as.character(dat2[1,i+1]);
25
+	}
26
+# no. of rows
27
+	rows <- nrow(dat2);
28
+	
29
+# load remaining counts
30
+	matrix <- array(NA, dim=c(length(subjects),rows-1));
31
+	covar = array(NA,dim=c(rows-1,1)); 
32
+	
33
+	for(i in 1:(rows)-1){
34
+		for(j in 1:(length(subjects))){ 
35
+			matrix[j,i] <- as.character(dat2[i+1,j+1]);
36
+		}
37
+		covar[i] = as.character(dat2[i+1,1]);
38
+	}  
39
+	
40
+		
41
+	phenoData<-as.data.frame(matrix);
42
+	
43
+	colnames(phenoData) = covar;
44
+	if(length(unique(subjects))==(length(subjects))){
45
+		rownames(phenoData) = subjects;
46
+	}
47
+    if(tran==TRUE){
48
+        phenoData = as.data.frame(t(phenoData))
49
+	}
50
+	return(phenoData);
51
+}
0 52
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... ...
@@ -0,0 +1,30 @@
1
+plotCorr <- function(obj,n,log=TRUE,norm=TRUE,fun=cor,...) {
2
+    if (log == TRUE) {
3
+        if (norm == TRUE) {
4
+            mat = log2(cumNormMat(obj) + 1)
5
+        }
6
+        else {
7
+            mat = log2(MRcounts(obj) + 1)
8
+        }
9
+    }
10
+    else {
11
+        if (norm == TRUE) {
12
+            mat = cumNormMat(obj)
13
+        }
14
+        else {
15
+            mat = MRcounts(obj)
16
+        }
17
+    }
18
+    otusToKeep <- which(rowSums(mat) > 0)
19
+    otuVars = rowSds(mat[otusToKeep, ])
20
+    otuIndices = otusToKeep[order(otuVars, decreasing = TRUE)[1:n]]
21
+    mat2 = mat[otuIndices, ]
22
+    cc = as.matrix(fun(t(mat2)))
23
+    hc = hclust(dist(mat2))
24
+    otuOrder = hc$order
25
+    cc = cc[otuOrder, otuOrder]
26
+    heatmapCols = colorRampPalette(brewer.pal(9, "RdBu"))(50)
27
+    heatmap.2(t(cc), col = heatmapCols, ...)
28
+    invisible()
29
+}
30
+
0 31
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... ...
@@ -0,0 +1,62 @@
1
+#' Basic plot function of the raw or normalized data.
2
+#'
3
+#' This function plots the abundance of a particular OTU by class. The function uses
4
+#' the estimated posterior probabilities to make technical zeros transparent. 
5
+#'
6
+#' @param obj An eSet object with count data.
7
+#' @param otuIndex A list of the otus with the same annotation.
8
+#' @param classIndex A list of the samples in their respective groups.
9
+#' @param norm Whether or not to normalize the counts.
10
+#' @param normp The value at which to scale the counts by and then log.
11
+#' @param no Which of the otuIndex to plot.
12
+#' @param factor Factor value for jitter
13
+#' @param pch Standard pch value for the plot command.
14
+#' @param jitter Boolean to jitter the count data or not.
15
+#' @param ret Boolean to return the observed data that would have been plotted.
16
+#' @param ... Additional plot arguments.
17
+#' @return NA
18
+#' @note \code{\link{detect}} makes use of settings.
19
+#'
20
+#' @name plotGenus
21
+#' @aliases genusPlot
22
+#' @seealso \code{\link{cumNorm}}
23
+#' @examples 
24
+#' classIndex=list(controls=which(type=="Control"))
25
+#' classIndex$cases=which(type=="Case")
26
+#' otuIndex = which(taxa == "E-coli")
27
+#' plotGenus(obj,otu=12,classIndex,xlab="OTU log-normalized counts")
28
+
29
+plotGenus <-
30
+function(obj,otuIndex,classIndex,norm=TRUE,no=1:length(otuIndex),jitter=TRUE,factor=1,pch=21,ret=FALSE,...){
31
+
32
+	l=lapply(otuIndex[no], function(i) lapply(classIndex, function(j) {
33
+        if(norm==FALSE){ x=log2(MRcounts(obj)[i,j]+1) }
34
+        else if(norm==TRUE){  x=log2(MRcounts(obj,norm=TRUE)+1)[i,j]; }
35
+        x 
36
+        }))
37
+
38
+	l=unlist(l,recursive=FALSE)
39
+	if(!is.list(l)) stop("l must be a list\n")
40
+	y=unlist(l)
41
+	x=rep(seq(along=l),sapply(l,length))
42
+
43
+	z = posterior.probs(obj)
44
+    #if(!is.null(z)){
45
+    #    z = 1-z;
46
+    #    lz=lapply(classIndex,function(j){(z[otuIndex[no],j])})
47
+    #    z = unlist(lz)
48
+    #    blackCol=t(col2rgb("black"))
49
+    #    col=rgb(blackCol,alpha=z)
50
+    #} else {
51
+        blackCol=t(col2rgb("black"))
52
+        col=rgb(blackCol)
53
+    #}
54
+
55
+	if(jitter) x=jitter(x,factor)
56
+	plot(x,y,col=col,pch=pch,...)
57
+	if(ret) list(x=x,y=y)
58
+}
59
+
60
+
61
+
62
+
0 63
new file mode 100644
... ...
@@ -0,0 +1,24 @@
1
+plotMRheatmap <- function(obj,n,trials,log=TRUE,norm=TRUE,...) {
2
+  
3
+  if(log==TRUE){
4
+    if(norm==TRUE){
5
+        mat = log2(cumNormMat(obj)+1);
6
+    }else{
7
+        mat = log2(MRcounts(obj)+1)
8
+    }
9
+  } else{
10
+    if(norm==TRUE){
11
+        mat = cumNormMat(obj)    
12
+    }else{
13
+        mat = MRcounts(obj)
14
+    }
15
+  }
16
+  otusToKeep <- which(rowSums(mat)>0);
17
+  otuVars=rowSds(mat[otusToKeep,]);
18
+	otuIndices=otusToKeep[order(otuVars,decreasing=TRUE)[1:n]];
19
+	mat2=mat[otuIndices,];
20
+  heatmapCols=colorRampPalette(brewer.pal(9,"RdBu"))(50);
21
+  heatmapColColors=brewer.pal(12,"Set3")[as.integer(factor(trials))];
22
+	heatmap.2(mat2,col=heatmapCols,ColSideColors=heatmapColColors,...);
23
+  invisible()
24
+}
0 25
\ No newline at end of file
1 26
new file mode 100644
... ...
@@ -0,0 +1,62 @@
1
+#' Basic plot function of the raw or normalized data.
2
+#'
3
+#' This function plots the abundance of a particular OTU by class. The function uses
4
+#' the estimated posterior probabilities to make technical zeros transparent. 
5
+#'
6
+#' @param obj An eSet object with count data.
7
+#' @param otu The row number/OTU to plot.
8
+#' @param classIndex A list of the samples in their respective groups.
9
+#' @param norm Whether or not to normalize the counts.
10
+#' @param normp The value at which to scale the counts by and then log.
11
+#' @param factor Factor value for jitter.
12
+#' @param pch Standard pch value for the plot command.
13
+#' @param jitter Boolean to jitter the count data or not.
14
+#' @param ret Boolean to return the observed data that would have been plotted.
15
+#' @param ... Additional plot arguments.
16
+#' @return NA
17
+#'
18
+#' @name plotOTU
19
+#' @aliases otuplot
20
+#' @seealso \code{\link{cumNorm}}
21
+#' @examples 
22
+#' classIndex=list(controls=which(type=="Control"))
23
+#' classIndex$cases=which(type=="Case")
24
+#' plotOTU(obj,otu=12,classIndex,xlab="OTU log-normalized counts")
25
+
26
+plotOTU <-
27
+function(obj,otu,classIndex,norm=TRUE,factor=1,pch=21,jitter=TRUE,ret=FALSE,...){
28
+
29
+	l=lapply(classIndex, function(j){
30
+        if(norm==FALSE){ 
31
+            log2(MRcounts(obj)[otu,j]+1) 
32
+        }
33
+        else if(norm==TRUE){
34
+            if(any(is.na(normFactors(obj)))){
35
+                log2(cumNormMat(obj)[otu,j]+1)
36
+            } else{ 
37
+                log2(MRcounts(obj,norm=TRUE)[otu,j]+1)
38
+            }
39
+        }
40
+        })
41
+
42
+	z = posterior.probs(obj)
43
+    y=unlist(l)
44
+    x=rep(seq(along=l),sapply(l,length))
45
+
46
+    if(!is.null(z)){
47
+        z = 1-z;
48
+        lz=lapply(classIndex,function(j){(z[otu,j])})
49
+        z = unlist(lz)
50
+        blackCol=t(col2rgb("black"))
51
+        col=rgb(blackCol,alpha=z)
52
+    } else {
53
+        blackCol=t(col2rgb("black"))
54
+        col=rgb(blackCol)
55
+    }
56
+    
57
+	if(jitter) x=jitter(x,factor)
58
+
59
+	plot(x,y,col=col,pch=pch,bg=col,...)
60
+	if (ret)
61
+		list(x=x,y=y)
62
+}
0 63
new file mode 100644
... ...
@@ -0,0 +1,18 @@
1
+#' Settings for the fitZig function
2
+#'
3
+#' @param tol The tolerance for the difference in negative log likelihood estimates for a feature to still be active.
4
+#' @param maxit The maximum number of iterations for the expectation-maximization algorithm.
5
+#' @param verbose Whether to display iterative step summary statistics or not.
6
+#' @return The value for the tolerance, maximum no. of iterations, and the verbose warning.
7
+#' @note \code{\link{fitZig}} makes use of zigControl.
8
+#'
9
+#' @name zigControl
10
+#' @aliases settings2
11
+#' @seealso \code{\link{fitZig}} \code{\link{cumNorm}} \code{\link{plotOTU}}
12
+#' @examples
13
+#' control =  zigControl(tol=1e-10,maxit=10,verbose=FALSE)
14
+
15
+zigControl <-function(tol=1e-4,maxit=10,verbose=TRUE){
16
+	set <-list(tol=tol,maxit=maxit,verbose=verbose);
17
+	return(set)	
18
+}
0 19
new file mode 100644
1 20
Binary files /dev/null and b/data/lungData.rda differ
2 21
new file mode 100644
3 22
Binary files /dev/null and b/data/mouseData.rda differ
4 23
new file mode 100644
... ...
@@ -0,0 +1,18 @@
1
+citEntry(entry="article",
2
+         title = "Robust statistical methods for differential abundance analysis of marker gene survey data",
3
+         author = personList( as.person("Joseph N. Paulson"), 
4
+                              as.person("Oscar Colin Stine"),
5
+                              as.person("Hector Corrada Bravo"),
6
+                              as.person("Mihai Pop")),
7
+         year = 2013,
8
+         journal = "In submission",
9
+         volume = "",
10
+         pages = "",
11
+         doi = "",
12
+         url = "",
13
+         
14
+         textVersion = 
15
+         paste("JN Paulson, OC Stine, HC Bravo, M Pop:", 
16
+               "Robust statistical methods for differential abundance analysis of marker gene survey data.",
17
+                "In submission" ) )
18
+
0 19
new file mode 100644
... ...
@@ -0,0 +1,1001 @@
1
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91
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92
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93
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94
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95
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96
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97
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98
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99
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100
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101
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102
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103
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104
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105
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106
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107
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108
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109
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110
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111
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112
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113
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114
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115
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116
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117
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118
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119
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120
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121
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122
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123
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124
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125
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126
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127
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128
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129
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130
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131
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132
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133
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134
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135
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136
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137
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138
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355
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356
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361
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362
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364
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366
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371
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375
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376
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380
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381
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382
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383
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384
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385
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386
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387
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388
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389
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390
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391
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392
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393
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394
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395
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396
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397
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398
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399
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400
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401
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402
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403
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404
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405
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406
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407
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408
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409
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410
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411
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412
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413
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414
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415
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416
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417
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418
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419
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420
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421
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422
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423
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424
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425
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426
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427
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428
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429
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430
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431
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432
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433
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434
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435
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436
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437
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438
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439
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440
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441
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442
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443
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444
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445
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446
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447
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448
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449
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450
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451
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452
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453
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454
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455
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456
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457
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458
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459
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460
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461
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462
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463
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464
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465
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466
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467
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468
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469
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470
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471
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472
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473
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474
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475
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476
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477
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478
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479
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480
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481
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482
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483
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484
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485
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486
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487
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488
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489
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490
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491
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492
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493
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