--- title: "Tutorial" output: rmarkdown::html_vignette: fig_width: 11 fig_height: 8 vignette: > %\VignetteIndexEntry{Tutorial} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ``` ## Load the dataset A subset of invasive breast carcinoma data from primary tumor tissue. See ``?tcga`` for more information on loading the full dataset or metadata. ```{r data} library(tcgaViz) library(ggplot2) data(tcga) head(tcga$genes) head(tcga$cells$Cibersort_ABS) ``` ## Violin plot of cell subtypes And perform a significance of a Wilcoxon adjusted test according to the expression level (high or low) of a selected gene. ```{r plot, warning = FALSE} df <- convert2biodata( algorithm = "Cibersort_ABS", disease = "breast invasive carcinoma", tissue = "Primary Tumor", gene_x = "ICOS" ) (stats <- calculate_pvalue(df)) plot(df, stats = stats) ``` ## Advanced parameters With [ggplot2::theme()](https://ggplot2.tidyverse.org/reference/theme.html) expressions. ```{r advanced} (df <- convert2biodata( algorithm = "Cibersort_ABS", disease = "breast invasive carcinoma", tissue = "Primary Tumor", gene_x = "ICOS", stat = "quantile" )) (stats <- calculate_pvalue( df, method_test = "t_test", method_adjust = "bonferroni", p_threshold = 0.01 )) plot( df, stats = stats, type = "boxplot", dots = TRUE, xlab = "Expression level of the 'ICOS' gene by cell type", ylab = "Percent of relative abundance\n(from the Cibersort_ABS algorithm)", title = toupper("Differential analysis of immune cell type abundance based on RNASeq gene-level expression from The Cancer Genome Atlas"), axis.text.y = element_text(size = 8, hjust = 0.5), plot.title = element_text(face = "bold", hjust = 0.5), plot.subtitle = element_text(size = , face = "italic", hjust = 0.5), draw = FALSE ) + labs( subtitle = paste("Breast Invasive Carcinoma (BRCA; Primary Tumor):", "Student's t-test with Bonferroni (P < 0.01)") ) ``` ## Session information ```{r end, echo = FALSE} sessionInfo() ```