---
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()
```