---
title: "Frequently asked questions"
author: "Developed by [Gabriel Hoffman](http://gabrielhoffman.github.io/)"
date: "Run on `r format(Sys.time())`"
output:
  rmarkdown::html_document:
    highlight: pygments
    toc: true
    toc_depth: 3
    fig_width: 5
bibliography: library.bib
vignette: >
  %\VignetteIndexEntry{6) Frequently asked questions}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
  %\usepackage[utf8]{inputenc}
---

<!---
cd /Users/gabrielhoffman/workspace/repos
R
library('knitr')
rmarkdown::render('variancePartition/vignettes/FAQ.Rmd')


rmarkdown::render('FAQ.Rmd')

--->

```{r setup, echo=FALSE, results="hide"}
knitr::opts_chunk$set(
  tidy = FALSE, cache = TRUE,
  dev = "png",
  package.startup.message = FALSE,
  message = FALSE, error = FALSE, warning = TRUE
)
options(width = 100)
```	


<style>
body {
text-align: justify}
</style>

# Interperting the residual variance
In general, I recommend against interpreting the fraction of variance explained by residuals.  This fraction is driven by: 

1. the particulars of the study design
2. measurement precision (i.e. high read counts give more precise measurements)
3. biological variability
4. technical variability (i.e. batch effects).   

If you have additional  variables that explain variation in measured gene expression, you should include them in order to avoid confounding with your variable of interest.  But a particular residual fraction is not ‘good’ or ‘bad’ and is not a good metric of determining whether more variables should be included.

# Current GitHub issues
See [GitHub page](https://github.com/DiseaseNeuroGenomics/variancePartition/issues) for up-to-date responses to users' questions.

# Session Info
<details>
```{r session, echo=FALSE}
sessionInfo()
```
</details>