## ----install-0, eval=FALSE, echo=TRUE----------------------------------------- # if(!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # BiocManager::install("metaseqR2") ## ----load-library, echo=TRUE, eval=TRUE, message=FALSE, warning=FALSE--------- library(metaseqR2) ## ----seed-0, eval=TRUE, echo=TRUE--------------------------------------------- set.seed(42) ## ----data-1, eval=TRUE, echo=TRUE--------------------------------------------- data("mm9GeneData",package="metaseqR2") ## ----head-1, eval=TRUE, echo=TRUE--------------------------------------------- head(mm9GeneCounts) ## ----random-1, eval=TRUE, echo=TRUE------------------------------------------- sampleListMm9 ## ----random-2, eval=TRUE, echo=TRUE------------------------------------------- libsizeListMm9 ## ----example-1, eval=TRUE, echo=TRUE, tidy=FALSE, message=TRUE, warning=FALSE---- library(metaseqR2) data("mm9GeneData",package="metaseqR2") # You can explore the results in the session's temporary directory print(tempdir()) result <- metaseqr2( counts=mm9GeneCounts, sampleList=sampleListMm9, contrast=c("adult_8_weeks_vs_e14.5"), libsizeList=libsizeListMm9, annotation="embedded", embedCols=list( idCol=4, gcCol=5, nameCol=8, btCol=7 ), org="mm9", countType="gene", normalization="edger", statistics="edger", pcut=0.05, qcPlots=c( "mds","filtered","correl","pairwise","boxplot","gcbias", "lengthbias","meandiff","meanvar","deheatmap","volcano", "mastat" ), figFormat=c("png","pdf"), exportWhat=c("annotation","p_value","adj_p_value","fold_change"), exportScale=c("natural","log2"), exportValues="normalized", exportStats=c("mean","sd","cv"), exportWhere=file.path(tempdir(),"test1"), restrictCores=0.01, geneFilters=list( length=list( length=500 ), avgReads=list( averagePerBp=100, quantile=0.25 ), expression=list( median=TRUE, mean=FALSE, quantile=NA, known=NA, custom=NA ), biotype=getDefaults("biotypeFilter","mm9") ), outList=TRUE ) ## ----head-2, eval=TRUE, echo=TRUE--------------------------------------------- head(result[["data"]][["adult_8_weeks_vs_e14.5"]]) ## ----example-2, eval=TRUE, echo=TRUE, tidy=FALSE, message=FALSE, warning=FALSE---- library(metaseqR2) data("mm9GeneData",package="metaseqR2") result <- metaseqr2( counts=mm9GeneCounts, sampleList=sampleListMm9, contrast=c("adult_8_weeks_vs_e14.5"), libsizeList=libsizeListMm9, annotation="embedded", embedCols=list( idCol=4, gcCol=5, nameCol=8, btCol=7 ), org="mm9", countType="gene", whenApplyFilter="prenorm", normalization="edaseq", statistics=c("deseq","edger"), metaP="fisher", #qcPlots=c( # "mds","biodetection","countsbio","saturation","readnoise","filtered", # "correl","pairwise","boxplot","gcbias","lengthbias","meandiff", # "meanvar","rnacomp","deheatmap","volcano","mastat","biodist","statvenn" #), qcPlots=c( "mds","filtered","correl","pairwise","boxplot","gcbias", "lengthbias","meandiff","meanvar","deheatmap","volcano", "mastat" ), restrictCores=0.01, figFormat=c("png","pdf"), preset="medium_normal", exportWhere=file.path(tempdir(),"test2"), outList=TRUE ) ## ----example-3, eval=TRUE, echo=TRUE, tidy=FALSE, message=FALSE, warning=FALSE---- library(metaseqR2) data("mm9GeneData",package="metaseqR2") result <- metaseqr2( counts=mm9GeneCounts, sampleList=sampleListMm9, contrast=c("adult_8_weeks_vs_e14.5"), libsizeList=libsizeListMm9, annotation="embedded", embedCols=list( idCol=4, gcCol=5, nameCol=8, btCol=7 ), org="mm9", countType="gene", normalization="edaseq", statistics=c("deseq","edger"), metaP="fisher", qcPlots=c( "mds","filtered","correl","pairwise","boxplot","gcbias", "lengthbias","meandiff","meanvar","deheatmap","volcano", "mastat" ), restrictCores=0.01, figFormat=c("png","pdf"), preset="medium_normal", outList=TRUE, exportWhere=file.path(tempdir(),"test3") ) ## ----example-4, eval=TRUE, echo=TRUE, tidy=FALSE, message=FALSE, warning=FALSE---- library(metaseqR2) data("mm9GeneData",package="metaseqR2") result <- metaseqr2( counts=mm9GeneCounts, sampleList=sampleListMm9, contrast=c("adult_8_weeks_vs_e14.5"), libsizeList=libsizeListMm9, annotation="embedded", embedCols=list( idCol=4, gcCol=5, nameCol=8, btCol=7 ), org="mm9", countType="gene", normalization="edaseq", statistics=c("edger","limma"), metaP="fisher", figFormat="png", preset="medium_basic", qcPlots=c( "mds","filtered","correl","pairwise","boxplot","gcbias", "lengthbias","meandiff","meanvar","deheatmap","volcano", "mastat" ), restrictCores=0.01, outList=TRUE, exportWhere=file.path(tempdir(),"test4") ) ## ----example-5, eval=TRUE, echo=TRUE, tidy=FALSE, message=FALSE, warning=FALSE---- data("mm9GeneData",package="metaseqR2") weights <- estimateAufcWeights( counts=as.matrix(mm9GeneCounts[,9:12]), normalization="edaseq", statistics=c("edger","limma"), nsim=1,N=10,ndeg=c(2,2),top=4,modelOrg="mm10", rc=0.01,libsizeGt=1e+5 ) ## ----head-3, eval=TRUE, echo=TRUE--------------------------------------------- weights ## ----example-6, eval=TRUE, echo=TRUE, tidy=FALSE, message=FALSE, warning=FALSE---- data("hg19pvalues",package="metaseqR2") # Examine the data head(hg19pvalues) # Now combine the p-values using the Simes method pSimes <- apply(hg19pvalues,1,combineSimes) # The harmonic mean method with PANDORA weights w <- getWeights("human") pHarm <- apply(hg19pvalues,1,combineHarmonic,w) # The PANDORA method pPandora <- apply(hg19pvalues,1,combineWeight,w) ## ----help-2, eval=TRUE, echo=TRUE, message=FALSE------------------------------ help(statEdger) ## ----si-1, eval=TRUE, echo=TRUE----------------------------------------------- sessionInfo()