---
title: "5-Minute Tutorial: RNA-Seq Analysis with bambamR"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{5-Minute Tutorial: RNA-Seq Analysis with bambamR}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
resource_files:
- figures/logo.svg
- figures/logo-dark.svg
---
[](https://github.com/CTTIR/bambamR/actions/workflows/R-CMD-check.yaml)
[](https://cttir.github.io/bambamR/)
[](https://CRAN.R-project.org/package=bambamR)
[](https://app.codecov.io/gh/CTTIR/bambamR?branch=main)
[](https://cran.r-project.org/package=bambamR)
[](https://cran.r-project.org/package=bambamR)
[](https://opensource.org/licenses/MIT)
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.width = 7,
fig.height = 5
)
```
This tutorial walks through a complete RNA-seq analysis in under 5
minutes using only bambamR's built-in example data. **No Bioconductor
packages are required.**
## Load the Package
```{r}
library(bambamR)
```
## 1. Load Data and Normalize
```{r}
# Bundled example: 200 genes x 10 samples (5 control, 5 treatment)
ex <- bb_example_counts()
cpm <- bb_normalize(ex$counts, method = "cpm")
cat(nrow(cpm), "genes,", ncol(cpm), "samples\n")
```
## 2. PCA: Do the Groups Separate?
```{r pca, fig.width=6, fig.height=4.5}
bb_pca(cpm, ex$metadata, color_by = "condition", label = TRUE)
```
## 3. Volcano Plot: What Changed?
Use the pre-computed DE results (no Bioconductor needed):
```{r volcano, fig.width=7, fig.height=5}
de <- bb_example_de()
bb_volcano(de, fc_cutoff = 1, p_cutoff = 0.05, n_label = 6)
```
## 4. MA Plot: Effect vs. Expression
```{r ma, fig.width=7, fig.height=4.5}
bb_ma_plot(de)
```
## 5. Heatmap: Top DE Genes
```{r heatmap, fig.width=7, fig.height=6}
bb_heatmap(cpm, de_result = de, n_genes = 20)
```
## 6. Oncoplot: Mutation Landscape
```{r oncoplot, fig.width=10, fig.height=6}
mut <- bb_example_mutations()
bb_oncoplot(mut$mutations, n_genes = 10, annotation_df = mut$clinical)
```
## 7. Export
```{r export, eval = FALSE}
bb_export_csv(de, "my_results.csv")
```
## Done!
In 7 steps you went from a count matrix to publication-ready PCA,
volcano, MA, heatmap, and onco plots -- all without Bioconductor.
### What's next?
- With Bioconductor: `bb_deseq2()`, `bb_edger()`, `bb_limma_voom()`
for real DE analysis
- From raw data: `bb_pipeline()` handles FASTQ -> alignment -> counts -> DE -> plots
- Interactive: `bb_run_app()` launches a Shiny dashboard
- Customization: every plot returns a ggplot2 object, so add
`+ theme()`, `+ labs()`, `+ scale_*()` as needed
- See `vignette("bambamR-quickstart")` for the full walkthrough and
`vignette("bambamR-oncoplot")` for oncoplot customization
