## ----init-init, eval=TRUE, echo=FALSE, message=FALSE, warning=FALSE------ library(metaseqR) ## ----init-metaseqr, eval=FALSE, echo=TRUE, warning=FALSE----------------- # library(metaseqR) # help(metaseqr) # or # help(metaseqr.main) ## ----help-1, eval=FALSE, echo=TRUE--------------------------------------- # help(hg18.exon.data) # help(mm9.gene.data) ## ----data-1, eval=TRUE, echo=TRUE---------------------------------------- data("mm9.gene.data",package="metaseqR") ## ----head-1, eval=TRUE, echo=TRUE---------------------------------------- head(mm9.gene.counts) ## ----random-1, eval=TRUE, echo=TRUE-------------------------------------- sample.list.mm9 ## ----random-2, eval=TRUE, echo=TRUE-------------------------------------- libsize.list.mm9 ## ----example-1, eval=TRUE, echo=TRUE, tidy=FALSE, message=TRUE, warning=FALSE---- library(metaseqR) data("mm9.gene.data",package="metaseqR") out.dir <- tempdir() print(out.dir) 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=out.dir, 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 ) ## ----head-2, eval=TRUE, echo=TRUE---------------------------------------- head(result[["data"]][["e14.5_vs_adult_8_weeks"]]) ## ----example-2, eval=TRUE, echo=TRUE, tidy=FALSE, message=TRUE, warning=FALSE---- library(metaseqR) data("mm9.gene.data",package="metaseqR") out.dir2 <- tempdir() print(out.dir2) 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=out.dir2, out.list=TRUE ) ## ----example-3, eval=FALSE, echo=TRUE, tidy=FALSE, message=FALSE, warning=FALSE---- # library(metaseqR) # data("mm9.gene.data",package="metaseqR") # out.dir <- tempdir() # print(out.dir) # 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, # export.where=out.dir # ) ## ----example-4, eval=FALSE, echo=TRUE, tidy=FALSE------------------------ # # A full example pipeline with exon counts # data("hg19.exon.data",package="metaseqR") # out.dir <- tempdir() # print(out.dir) # 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") # ), # export.where=out.dir # ) ## ----example-5, eval=FALSE, echo=TRUE, tidy=FALSE------------------------ # # A full example pipeline with exon counts # data("hg19.exon.data",package="metaseqR") # out.dir <- tempdir() # print(out.dir) # 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, # export.where=out.dir # ) ## ----example-6, eval=TRUE, echo=TRUE, tidy=FALSE------------------------- 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 ) ## ----head-3, eval=TRUE, echo=TRUE---------------------------------------- weights ## ----help-2, eval=FALSE, echo=TRUE--------------------------------------- # help(stat.edgeR) ## ----help-3, eval=FALSE, echo=TRUE--------------------------------------- # help(metaseqr) ## ----session-info, eval=TRUE, echo=FALSE--------------------------------- sessionInfo()