## ----message=FALSE, warning=FALSE, include=FALSE------------------------------ options(rmarkdown.html_vignette.check_title = FALSE) ## ----message=FALSE, warning=FALSE--------------------------------------------- library(sesame) ## ----------------------------------------------------------------------------- ## As sesame and sesameData are under active development, this documentation is ## specific to the following version of R, sesame, sesameData and ExperimentHub: sesame_checkVersion() ## ----message=FALSE------------------------------------------------------------ sesameDataCache() ## ----------------------------------------------------------------------------- tools::R_user_dir("ExperimentHub", which="cache") ## ----base1, eval=FALSE-------------------------------------------------------- # betas = openSesame("path_to_idats", BPPARAM = BiocParallel::MulticoreParam(2)) ## ----base2, eval=FALSE-------------------------------------------------------- # ## The above openSesame call is equivalent to: # betas = do.call(cbind, BiocParallel::bplapply( # searchIDATprefixes(idat_dir), function(pfx) { # getBetas(prepSesame(readIDATpair(pfx), "QCDPB")) # }, BPPARAM = BiocParallel::MulticoreParam(2))) # # ## or even more explicitly (if one needs to control argument passed # ## to a specific preprocessing function) # betas = do.call(cbind, BiocParallel::bplapply( # searchIDATprefixes(idat_dir), function(pfx) { # getBetas(noob(pOOBAH(dyeBiasNL(inferInfiniumIChannel(qualityMask( # readIDATpair(pfx))))))) # }, BPPARAM = BiocParallel::MulticoreParam(2))) ## ----base12, eval=FALSE------------------------------------------------------- # betas = openSesame(idat_dir, func = getBetas) # getBetas is the default # sdfs = openSesame(idat_dir, func = NULL) # return SigDF list # allele_freqs = openSesame(idat_dir, func = getAFs) # SNP allele frequencies # sdfs = openSesame(sdfs, prep = "Q", func = NULL) # take and return SigDFs ## ----base14, eval=FALSE------------------------------------------------------- # pvals = openSesame(idat_dir, func = pOOBAH, return.pval=TRUE) ## ----base9-------------------------------------------------------------------- sdf = sesameDataGet('EPIC.1.SigDF') sdf_preped = openSesame(sdf, prep="DB", func=NULL) ## ----base10, echo=FALSE, result="asis"---------------------------------------- library(knitr) df <- data.frame(rbind( c("EPICv2/EPIC/HM450", "human", "QCDPB"), c("EPICv2/EPIC/HM450", "non-human organism", "SQCDPB"), c("MM285", "mouse", "TQCDPB"), c("MM285", "non-mouse organism", "SQCDPB"), c("Mammal40", "human", "HCDPB"), c("Mammal40", "non-human organism", "SHCDPB"))) colnames(df) <- c("Platform", "Sample Organism", "Prep Code") kable(df, caption="Recommended Preprocessing") ## ----base11------------------------------------------------------------------- prepSesameList() ## ----eval=FALSE--------------------------------------------------------------- # betas = getBetas(sdf_from_EPICv2, collapseToPfx = TRUE) # ## or # betas = openSesame("path_to_idats", collapseToPfx = TRUE) # ## by default the method for collapsing is to make means # betas = openSesame("path_to_idats", collapseToPfx = TRUE, collapseMethod = "mean") # ## one can also switch to min detection p-value # betas = openSesame("path_to_idats", collapseToPfx = TRUE, collapseMethod = "minPval") ## ----eval=FALSE--------------------------------------------------------------- # betas = betasCollapseToPfx(betas, BPPARAM=BiocParallel::MulticoreParam(2)) ## ----------------------------------------------------------------------------- sessionInfo()