index.Rmd

---
title: "U-BDS bulk RNA-Seq workshop"
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# `r fontawesome::fa("chart-line", fill = "#295135")` Overview 

Recent advancements in RNA-sequencing (RNA-Seq) have led to impactful technological 
breakthroughs in RNA-Seq quantification at high resolution, including single-cell 
RNA-Seq and spatial transcriptomics. However, bulk RNA-Seq 
(defined as: RNA-Seq quantification from averaged gene expression ; 
averaged expression across all cell types from tissue collected without 
spatial resolution), continues to play an important role in transcriptomics 
for various reasons, including but not limited to: 

1. High volume of publicly available datasets  (a good starting point for 
initial hypothesis testing) 
2. Cost effectivess 
3. It has been well standardized over the last decade, 
and thus, molecular and analysis methods are robust for the scope of the technique.

## Bulk RNA-Seq as a *foundational block* to transcriptomics analysis 

To data scientists seeking to analyze the latest transcriptomics approaches 
(e.g.: single-cell, or spatial), bulk RNA-Seq analysis methods are foundational 
blocks to transcriptomics. For example, familiarity with bulk RNA-Seq methods is
critical for the efficient analysis of cell specific pseudobulk data derived from
single-cell experiments. Furthermore, many enrichment analysis methods and 
visualization approaches are shared or built from classical methods from bulk
RNA-Seq analysis. Thus, familiarity with bulk transcriptomics is a critical skill
to have for any transcriptomics analysis.

## Pre-requirements

Workshop participants are strongly encouraged to have familiarity with Unix and R (introductory level).

The Software Carpentry provides excellent introductory level material, which can be found in the following links:

* `r fontawesome::fa("r-project", fill = "steelblue")`: <https://swcarpentry.github.io/r-novice-gapminder/>
* `r fontawesome::fa("terminal", fill = "#295135")`: <https://swcarpentry.github.io/shell-novice/>

If you have already attended one of the previous U-BDS workshops for R and Unix
programming, then you should meet the pre-requirements. If you have not, we
heavily encourage participants to go through the materials from the links above
prior to attendance. 

_____________

# `r fontawesome::fa("list", fill = "#295135")` Scope of the workshop 

This workshop will cover the foundational topics and methods to bulk RNA-Seq analysis, including:

* Introduction to raw data management and data exploration
* Secondary analysis with `nf-core/rnaseq`
* Tertiary analysis topics: quality and control, data normalization and differential 
gene expression analysis with `DESeq2`, gene annotation, gene enrichment analysis 
(gene-ontology and gene set enrichment analysis)
* Fundamentals of data visualization for transcriptomics

# `r fontawesome::fa("chalkboard", fill = "#295135")` Authors 

* Austyn Trull
* Bharat Mishra, PhD
* Lara Ianov, PhD

A.T., B.M. and L.I. designed workshop content and initial outline. A.T. contributed
to installation instructions, data management, and secondary analysis materials. B.M.
contributed to Docker container creation and tertiary analysis materials 
(including DEG analysis, GSEA, GO, visualization etc.). 
L.I. supervised all work, reviewed all content and managed website design and deployment.

Nilesh Kumar, PhD and Luke Potter, PhD also provided additional edits and revisions to source material.

## Additional credits

In addition to U-BDS's best practices and code written by U-BDS, sections of the
teaching material for this workshop (parts of tertiary analysis), contains materials
which have been adapted or modified from the following sources (we thank the
curators and maintainers of all of these resources for their wonderful contributions,
compiling the best practices, and easy to follow training guides for beginners):

* Beta phase of carpentries material: <https://carpentries-incubator.github.io/bioc-rnaseq/index.html>
* Love MI, Huber W, Anders S (2014). “Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2.” Genome Biology, 15, 550. <https://doi.org/10.1186/s13059-014-0550-8> ; vignette: <https://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html>
* Additional references and materials: 
  * <https://alexslemonade.github.io/refinebio-examples/03-rnaseq/00-intro-to-rnaseq.html>
  * <https://bioc.ism.ac.jp/packages/3.7/bioc/vignettes/enrichplot/inst/doc/enrichplot.html>

We would also like to thank the following groups for support:

* UAB's Research Computing (HPC resources and workshop logistics with resources)
* nf-core community (rnaseq pipeline)

We would also like to thank the authors of the dataset which we implement
in our workshop:

* Koch CM, Chiu SF, Akbarpour M, Bharat A, Ridge KM, Bartom ET, Winter DR.
A Beginner's Guide to Analysis of RNA Sequencing Data. Am J Respir Cell Mol Biol.
2018 Aug;59(2):145-157. doi: 10.1165/rcmb.2017-0430TR. PMID: 29624415; PMCID: PMC6096346.

Lastly, we would also like to thank Kristen Coutinho and the UAB Informatics Club 
for the dataset suggestion, and preliminary discussions for this workshop.

## `r fontawesome::fa("chalkboard-user", fill = "#295135")` Workshop instructors 

* Austyn Trull
* Bharat Mishra, PhD
* Nilesh Kumar, PhD
* Luke Potter, PhD
* Lara Ianov, PhD

# `r fontawesome::fa("book", fill = "#295135")` Citation

If these materials are beneficial to your analysis and research, please cite it with the following DOI:

> UAB Biological Data Science Core, Bulk RNA-seq Workshop [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15539954.svg)](https://doi.org/10.5281/zenodo.15539954)