additional_resources.Rmd

---
title: "RNASeq: Additional resources"
author: "Bharat Mishra, Ph.D., Austyn Trull, Lara Ianov, Ph.D."
---

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

# Complex designs

`DESeq2` also allows for the analysis of complex designs. We heavily encourage trainees 
to read the entire `DESeq2` vignette to learn more:

https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html

Briefly, a few examples may include the need to control for variables, such as
a batch effect or biological variable (e.g.: sex, age etc.). The standard process
through which this can be performed in `DESeq2` is by including the additional
variable(s) as part of the design. For example, the following design would test
for differences in a variable called `condition`, while controlling for `batch`.

```
design= ~ batch + condition
```

Note: Does the order of variables matter?

The best practice for `DESeq2` is to add your variable of interest (to be tested
for DEG analysis), at the last position in the design (as shown in the example
above). This order aids in ensuring that default results are pulled from the variable of interest.

## Time-series experiments


`DESeq2` can be used to analyze time course experiments. [This workflow is a great
introduction for this type of analysis](https://master.bioconductor.org/packages/release/workflows/vignettes/rnaseqGene/inst/doc/rnaseqGene.html#time-course-experiments:~:text=9-,Time%20course%20experiments,-DESeq2%20can%20be).

## Other complex designs

While `DESeq2` is a great tool for many RNA-seq analysis, it also has its limitations.
*One particular limitation to be aware of is that it does not support differential
expression analysis with random effects*. A great resource for this type of analysis,
is `limma` (specifically `limma-voom`). While the `limma` vignette can be quite large
due to its historical role in microarray data, it's well organized and searchable to 
direct users to RNA-seq data and it's `limma-voom` approach.

See more at: http://bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf

# Environment reproducibility

As discussed, secondary analysis with nf-core pipelines already implement what is known as 
software containers (Docker, Singularity), however tertiary analysis and visualization 
requires users to build their own custom containers. 
This can be a critical step in aiding your analysis to be portable and reproducible.

Unfortunately, due to time limitations and workshop scope, we are unable to
demonstrate how to build your own containers. To learn more about Docker and
Singularity containers, we encourage all trainees to visit our training website
for a detailed 3-part tutorial in the "Intro to Docker with RStudio" tab from 
<https://u-bds.github.io/training_guides/>.

# U-BDS Data Science Office hours

If you have follow-up questions about this workshop or any other data science
topic which cover domain that U-BDS cover, please attend our office hours.

It is every Thursday from 1:30pm-2:00. The registration link can be found
[here](https://uab.zoom.us/meeting/register/tZ0rduCuqzotGtHShsawHLyRROqH3Sdz71mf#/registration).

# U-BDS services

If you would like to learn more about the core services please visit our main 
UAB webpage at: <https://www.uab.edu/cores/ircp/bds>. If you would like to have
a consultation, please fill out the "Enquiry Form" present on the website.