### R code from vignette source 'metaseqr-pdf.Rnw' ### Encoding: UTF-8 ################################################### ### code chunk number 1: init-init ################################################### library(metaseqR) ################################################### ### code chunk number 2: init-metaseqr (eval = FALSE) ################################################### ## library(metaseqR) ## help(metaseqr) # or ## help(metaseqr.main) ################################################### ### code chunk number 3: help-1 (eval = FALSE) ################################################### ## help(hg18.exon.data) ## help(mm9.gene.data) ################################################### ### code chunk number 4: data-1 ################################################### data("mm9.gene.data",package="metaseqR") ################################################### ### code chunk number 5: head-1 ################################################### head(mm9.gene.counts) ################################################### ### code chunk number 6: random-1 ################################################### sample.list.mm9 ################################################### ### code chunk number 7: random-2 ################################################### libsize.list.mm9 ################################################### ### code chunk number 8: example-1 ################################################### library(metaseqR) data("mm9.gene.data",package="metaseqR") result <- metaseqr( counts=mm9.gene.counts, sample.list=sample.list.mm9, contrast=c("e14.5_vs_adult_8_weeks"), libsize.list=libsize.list.mm9, annotation="download", org="mm9", count.type="gene", normalization="edger", statistics="edger", pcut=0.05, fig.format=c("png","pdf"), export.what=c("annotation","p.value","meta.p.value", "adj.meta.p.value","fold.change"), export.scale=c("natural","log2"), export.values="normalized", export.stats=c("mean","sd","cv"), export.where="~/metaseqr_test", restrict.cores=0.8, gene.filters=list( length=list( length=500 ), avg.reads=list( average.per.bp=100, quantile=0.25 ), expression=list( median=TRUE, mean=FALSE, quantile=NA, known=NA, custom=NA ), biotype=get.defaults("biotype.filter","mm9") ), out.list=TRUE ) ################################################### ### code chunk number 9: head-2 ################################################### head(result[["data"]][["e14.5_vs_adult_8_weeks"]]) ################################################### ### code chunk number 10: example-2 ################################################### library(metaseqR) data("mm9.gene.data",package="metaseqR") result <- metaseqr( counts=mm9.gene.counts, sample.list=sample.list.mm9, contrast=c("e14.5_vs_adult_8_weeks"), libsize.list=libsize.list.mm9, annotation="download", org="mm9", count.type="gene", when.apply.filter="prenorm", normalization="edaseq", statistics=c("deseq","edger"), meta.p="fisher", qc.plots=c( "mds","biodetection","countsbio","saturation","readnoise","filtered", "correl","pairwise","boxplot","gcbias","lengthbias","meandiff", "meanvar","rnacomp","deheatmap","volcano","biodist","venn" ), fig.format=c("png","pdf"), preset="medium.normal", export.where="~/metaseqr_test2", out.list=TRUE ) ################################################### ### code chunk number 11: example-3 (eval = FALSE) ################################################### ## library(metaseqR) ## data("mm9.gene.data",package="metaseqR") ## result <- metaseqr( ## counts=mm9.gene.counts, ## sample.list=sample.list.mm9, ## contrast=c("e14.5_vs_adult_8_weeks"), ## libsize.list=libsize.list.mm9, ## annotation="download", ## org="mm9", ## count.type="gene", ## normalization="edaseq", ## statistics=c("deseq","edger"), ## meta.p="fisher", ## fig.format=c("png","pdf"), ## preset="medium.normal", ## out.list=TRUE ## ) ################################################### ### code chunk number 12: example-4 (eval = FALSE) ################################################### ## # A full example pipeline with exon counts ## data("hg19.exon.data",package="metaseqR") ## metaseqr( ## counts=hg19.exon.counts, ## sample.list=sample.list.hg19, ## contrast=c("normal_vs_paracancerous","normal_vs_cancerous", ## "normal_vs_paracancerous_vs_cancerous"), ## libsize.list=libsize.list.hg19, ## id.col=4, ## annotation="download", ## org="hg19", ## count.type="exon", ## normalization="edaseq", ## statistics="deseq", ## pcut=0.05, ## qc.plots=c( ## "mds","biodetection","countsbio","saturation","rnacomp","pairwise", ## "boxplot","gcbias","lengthbias","meandiff","meanvar","correl", ## "deheatmap","volcano","biodist","filtered" ## ), ## fig.format=c("png","pdf"), ## export.what=c("annotation","p.value","adj.p.value","fold.change","stats","counts"), ## export.scale=c("natural","log2","log10","vst"), ## export.values=c("raw","normalized"), ## export.stats=c("mean","median","sd","mad","cv","rcv"), ## restrict.cores=0.8, ## gene.filters=list( ## length=list( ## length=500 ## ), ## avg.reads=list( ## average.per.bp=100, ## quantile=0.25 ## ), ## expression=list( ## median=TRUE, ## mean=FALSE ## ), ## biotype=get.defaults("biotype.filter","hg19") ## ) ## ) ################################################### ### code chunk number 13: example-5 (eval = FALSE) ################################################### ## # A full example pipeline with exon counts ## data("hg19.exon.data",package="metaseqR") ## metaseqr( ## counts=hg19.exon.counts, ## sample.list=sample.list.hg19, ## contrast=c("normal_vs_paracancerous","normal_vs_cancerous", ## "normal_vs_paracancerous_vs_cancerous"), ## libsize.list=libsize.list.hg19, ## id.col=4, ## annotation="download", ## org="hg19", ## count.type="exon", ## normalization="edaseq", ## statistics="deseq", ## preset="medium.normal", ## restrict.cores=0.8 ## ) ################################################### ### code chunk number 14: example-6 ################################################### # A full example pipeline with exon counts data("mm9.gene.data",package="metaseqR") multic <- check.parallel(0.8) weights <- estimate.aufc.weights( counts=as.matrix(mm9.gene.counts[,9:12]), normalization="edaseq", statistics=c("edger","limma"), nsim=1,N=10,ndeg=c(2,2),top=4,model.org="mm9", seed=42,multic=multic,libsize.gt=1e+5 ) ################################################### ### code chunk number 15: head-3 ################################################### weights ################################################### ### code chunk number 16: help-2 (eval = FALSE) ################################################### ## help(stat.edgeR) ################################################### ### code chunk number 17: help-3 (eval = FALSE) ################################################### ## help(metaseqr) ################################################### ### code chunk number 18: session-info ################################################### sessionInfo()