## ---- include = FALSE--------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----setup-------------------------------------------------------------------- library(scShapes) library(BiocParallel) set.seed(0xBEEF) ## ----example, results='hide', message=FALSE, warning=FALSE-------------------- # Loading and preparing data for input data(scData) ## ----filtered----------------------------------------------------------------- scData_filt <- filter_counts(scData$counts, perc.zero = 0.1) ## ----message=FALSE, warning=FALSE--------------------------------------------- scData_KS <- ks_test(counts=scData$counts, cexpr=scData$covariates, lib.size=scData$lib_size, BPPARAM=SnowParam(workers=8,type="SOCK")) # Select genes significant from the KS test. # By default the 'ks_sig' function performs Benjamini-Hochberg correction for multiple hypothese testing # and selects genes significant at p-value of 0.01 scData_KS_sig <- ks_sig(scData_KS) # Subset UMI counts corresponding to the genes significant from the KS test scData.sig.genes <- scData$counts[rownames(scData$counts) %in% names(scData_KS_sig$genes),] ## ----message=FALSE, warning=FALSE--------------------------------------------- scData_models <- fit_models(counts=scData.sig.genes, cexpr=scData$covariates, lib.size=scData$lib_size, BPPARAM=SnowParam(workers=8,type="SOCK")) ## ----message=FALSE, warning=FALSE--------------------------------------------- scData_bicvals <- model_bic(scData_models) # select model with least bic value scData_least.bic <- lbic_model(scData_bicvals, scData$counts) ## ----message=FALSE, warning=FALSE--------------------------------------------- scData_gof <- gof_model(scData_least.bic, cexpr=scData$covariates, lib.size=scData$lib_size, BPPARAM=SnowParam(workers=8,type="SOCK")) ## ----message=FALSE, warning=FALSE--------------------------------------------- scData_fit <- select_model(scData_gof) ## ----message=FALSE, warning=FALSE--------------------------------------------- scData_params <- model_param(scData_models, scData_fit, model=NULL) ## ----eval=FALSE--------------------------------------------------------------- # ifnb.DD.genes <- change_shape(ifnb.distr) ## ----------------------------------------------------------------------------- sessionInfo()