## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, # fig.align = "center", comment = ">" ) ## ----eval = FALSE------------------------------------------------------------- # # install.packages("BiocManager") # BiocManager::install("POMA") ## ----warning = FALSE, message = FALSE, comment = FALSE------------------------ library(POMA) library(patchwork) ## ----warning = FALSE, comment = NA-------------------------------------------- # imputation using the default method KNN example_data <- st000336 %>% PomaImpute() example_data ## ----warning = FALSE---------------------------------------------------------- none <- PomaNorm(example_data, method = "none") auto_scaling <- PomaNorm(example_data, method = "auto_scaling") level_scaling <- PomaNorm(example_data, method = "level_scaling") log_scaling <- PomaNorm(example_data, method = "log_scaling") log_transformation <- PomaNorm(example_data, method = "log_transformation") vast_scaling <- PomaNorm(example_data, method = "vast_scaling") log_pareto <- PomaNorm(example_data, method = "log_pareto") ## ----warning = FALSE---------------------------------------------------------- dim(SummarizedExperiment::assay(none)) dim(SummarizedExperiment::assay(auto_scaling)) dim(SummarizedExperiment::assay(level_scaling)) dim(SummarizedExperiment::assay(log_scaling)) dim(SummarizedExperiment::assay(log_transformation)) dim(SummarizedExperiment::assay(vast_scaling)) dim(SummarizedExperiment::assay(log_pareto)) ## ----message = FALSE, warning = FALSE----------------------------------------- a <- PomaBoxplots(none, group = "samples", jitter = FALSE) + ggplot2::ggtitle("Not Normalized") b <- PomaBoxplots(auto_scaling, group = "samples", jitter = FALSE, legend_position = "none") + ggplot2::ggtitle("Auto Scaling") + ggplot2::theme(axis.text.x = ggplot2::element_blank()) c <- PomaBoxplots(level_scaling, group = "samples", jitter = FALSE, legend_position = "none") + ggplot2::ggtitle("Level Scaling") + ggplot2::theme(axis.text.x = ggplot2::element_blank()) d <- PomaBoxplots(log_scaling, group = "samples", jitter = FALSE, legend_position = "none") + ggplot2::ggtitle("Log Scaling") + ggplot2::theme(axis.text.x = ggplot2::element_blank()) e <- PomaBoxplots(log_transformation, group = "samples", jitter = FALSE, legend_position = "none") + ggplot2::ggtitle("Log Transformation") + ggplot2::theme(axis.text.x = ggplot2::element_blank()) f <- PomaBoxplots(vast_scaling, group = "samples", jitter = FALSE, legend_position = "none") + ggplot2::ggtitle("Vast Scaling") + ggplot2::theme(axis.text.x = ggplot2::element_blank()) g <- PomaBoxplots(log_pareto, group = "samples", jitter = FALSE, legend_position = "none") + ggplot2::ggtitle("Log Pareto") + ggplot2::theme(axis.text.x = ggplot2::element_blank()) a (b + c + d) / (e + f + g) ## ----message = FALSE, warning = FALSE----------------------------------------- h <- PomaDensity(none, group = "features", legend_position = "none") + ggplot2::ggtitle("Not Normalized") i <- PomaDensity(auto_scaling, group = "features", legend_position = "none") + ggplot2::ggtitle("Auto Scaling") + ggplot2::theme(axis.title.x = ggplot2::element_blank(), axis.title.y = ggplot2::element_blank()) j <- PomaDensity(level_scaling, group = "features", legend_position = "none") + ggplot2::ggtitle("Level Scaling") + ggplot2::theme(axis.title.x = ggplot2::element_blank(), axis.title.y = ggplot2::element_blank()) k <- PomaDensity(log_scaling, group = "features", legend_position = "none") + ggplot2::ggtitle("Log Scaling") + ggplot2::theme(axis.title.x = ggplot2::element_blank(), axis.title.y = ggplot2::element_blank()) l <- PomaDensity(log_transformation, group = "features", legend_position = "none") + ggplot2::ggtitle("Log Transformation") + ggplot2::theme(axis.title.x = ggplot2::element_blank(), axis.title.y = ggplot2::element_blank()) m <- PomaDensity(vast_scaling, group = "features", legend_position = "none") + ggplot2::ggtitle("Vast Scaling") + ggplot2::theme(axis.title.x = ggplot2::element_blank(), axis.title.y = ggplot2::element_blank()) n <- PomaDensity(log_pareto, group = "features", legend_position = "none") + ggplot2::ggtitle("Log Pareto") + ggplot2::theme(axis.title.x = ggplot2::element_blank(), axis.title.y = ggplot2::element_blank()) h (i + j + k) / (l + m + n) ## ----------------------------------------------------------------------------- sessionInfo()