## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE) knitr::opts_chunk$set(fig.width=5, fig.height=4) ## ----results='hide', message=FALSE, warning=FALSE----------------------------- library(BDMMAcorrect) require(SummarizedExperiment) data(Microbiome_dat) ### Access phenotypes information col_data <- colData(Microbiome_dat) pheno <- data.frame(col_data$main, col_data$confounder) batch <- col_data[,3] ### Access taxonomy read counts counts <- t(assay(Microbiome_dat)) ### Indicate whether the phenotype variables are continuous continuous <- mcols(col_data)[1:2,] ## ----echo=TRUE---------------------------------------------------------------- figure = VBatch(Microbiome_dat = Microbiome_dat, method = "bray") print(figure) ## ----echo=TRUE---------------------------------------------------------------- main_variable <- pheno[,1] main_variable[main_variable == 0] <- "Control" main_variable[main_variable == 1] <- "Case" figure <- VBatch(Microbiome_dat = Microbiome_dat, main_variable = main_variable, method = "bray") print(figure[[1]]) ## ----echo=TRUE---------------------------------------------------------------- print(figure[[2]]) ## ----echo=TRUE---------------------------------------------------------------- output <- BDMMA(Microbiome_dat = Microbiome_dat, burn_in = 4000, sample_period = 4000) print(output$selected.taxa) head(output$parameter_summary) print(output$PIP) print(output$bFDR) ## ----include=FALSE------------------------------------------------------------ knitr::opts_chunk$set(fig.width=6, fig.height=2.5) ## ----echo=TRUE---------------------------------------------------------------- figure <- trace_plot(trace = output$trace, param = c("alpha_1", "beta1_10")) print(figure) ## ----echo=TRUE---------------------------------------------------------------- ### Simulate counts counts <- rmultinom(100,10000,rep(0.02,50)) ### Simulate covariates main <- rbinom(100,1,0.5) confounder <- rnorm(100,0,1) ### Simulate batches batch <- c(rep(1,50),rep(2,50)) library(SummarizedExperiment) col_data <- DataFrame(main, confounder, batch) mcols(col_data)$continous <- c(0L, 1L, 0L) ### pack different datasets into a SummarizedExperiment object Microbiome_dat <- SummarizedExperiment(list(counts), colData=col_data)