index.Rmd

---
title: "Linear models in R"
output: 
    html_document:
        theme: cerulean
        css: style.css
---

## Workshop notes

* [Linear models slideshow](slides/linear_thinking.html)
* [Linear models workshop](topics/linear_models.html)

* [RNA-Seq differential expression slideshow](slides/rnaseq_thinking.html)
* [RNA-Seq differential expression workshop](topics/rnaseq.html)

## Setup

This workshop is designed to work with RStudio running in [Posit Cloud](https://posit.cloud/). Go to https://posit.cloud/ and create a new project. Monash users can log in with their Monash Google account. The workshop can also be done using R locally on your laptop (if doing this, we also recommend you create a new project to contain the files).

Running the R code below will download files and install packages used in this workshop.

```{r eval=FALSE}
# Download data
download.file(
  "https://monashdatafluency.github.io/r-linear/r-linear-files.zip",
  destfile="r-linear-files.zip")
unzip("r-linear-files.zip")

# Install some CRAN packages:
install.packages(c(
    "tidyverse", "reshape2", "multcomp", "emmeans", 
    "lme4", "lmerTest", "pbkrtest", "seriation", "BiocManager"))

# Install some Bioconductor packages:
BiocManager::install(c("limma","edgeR","ComplexHeatmap","topconfects"))
```

Now load the file `linear_models.R` in the `r-linear-files` folder.

## Files

* [r-linear-files.zip](r-linear-files.zip) - Files used in this workshop.

## Key functions to remember

Built-in to R: 

    lm, model.matrix, coef, sigma, df.residual,
    predict, confint, summary, anova, drop1, 
    I, poly

`splines` -- curve fitting:

    ns, bs

`multcomp` and `emmeans` -- linear hypothesis tests and multiple comparisons: 

    glht, mcp, confint, summary, emmeans

`limma` and `edgeR` -- fitting many models to gene expression data: 

    DGEList, calcNormFactors, cpm,
    voom, lmFit, contrasts.fit, eBayes, topTable,
    plotMDS, plotSA

## Links

* Postgraduate students at Monash can access [statistical consulting](https://www.monash.edu/researchinfrastructure/datascienceandai/capabilities/statistical-consulting), courtesy of the Data Science and AI Platform. This is a good service for beginner to intermediate statistical questions.

* The [Biostatistics Consulting Platform](https://www.monash.edu/medicine/sphpm/units/biostats-consulting) in the Monash Faculty of Medicine may be more suitable for advanced questions about experimental design and analysis.

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* [Monash Data Fluency](https://www.monash.edu/data-fluency)
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* [Monash Genomics and Bioinformatics Platform](https://www.monash.edu/researchinfrastructure/mgbp)

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* [More workshop material from Monash Genomics and Bioinformatics Platform](https://www.monash.edu/researchinfrastructure/bioinformatics/training)
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<br>

* [Course notes for PH525x.](http://genomicsclass.github.io/book/) Initial chapters of this edX course cover similar material to this workshop.

* [StatQuest videos on linear models.](https://www.youtube.com/watch?v=PaFPbb66DxQ&list=PLblh5JKOoLUIzaEkCLIUxQFjPIlapw8nU) A friendly but thorough introduction to key ideas.

* [Faraway (2014) "Linear models with R"](https://julianfaraway.github.io/faraway/LMR/)

* [Harrel (2015) "Regression Modeling Strategies"](https://hbiostat.org/rms/) has detailed practical advice for creating predictive models, such as models using biomarkers. [Frank Harrell's home page.](https://hbiostat.org/)

* [James, Witten, Hastie and Tibshirani (2013) "An Introduction to Statistical Learning"](https://www.statlearning.com/) describes fundamental ideas and methods in machine learning.

* Richard McElreath has a course called "Statistical Rethinking" with a Baysian approach and a focus on causal concepts which are important if you have observational rather than experimental data. The [2023 video lectures](https://www.youtube.com/watch?v=FdnMWdICdRs&list=PLDcUM9US4XdPz-KxHM4XHt7uUVGWWVSus&index=1) are a good place to start, and there is also a book.

* [Dance of the CIs app](http://logarithmic.net/2017/dance/) for intuition about Confidence Intervals.

* [The Art of Linear Algebra](https://github.com/kenjihiranabe/The-Art-of-Linear-Algebra/blob/main/The-Art-of-Linear-Algebra.pdf) for intuition about matrices and vectors -- sections 1-3 are relevant to this workshop.

* Mixed effects models are a popular next step beyond the fixed effects models covered in this workshop.
    * [Mixed Models in R](https://m-clark.github.io/mixed-models-with-R/)<br><br>

* Testing for differential gene expression often uses linear models. The developers of `limma` and `edgeR` at [WEHI](https://www.wehi.edu.au/research/research-fields/bioinformatics) have written some good introductions to this topic:
    * ["RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR"](https://bioconductor.org/packages/release/workflows/vignettes/RNAseq123/inst/doc/limmaWorkflow.html)
    * ["A guide to creating design matrices for gene expression experiments"](https://bioconductor.org/packages/release/workflows/vignettes/RNAseq123/inst/doc/designmatrices.html) (design matrix = model matrix)<br><br>

* [MGBP's RNA-Seq end-to-end workshop](https://monashbioinformaticsplatform.github.io/RNAseq_workshop_2024/)

* [Degust](https://degust.erc.monash.edu/) provides a friendly web interface for doing RNA-Seq differential expression.


## Author

This course has been developed for the [Monash Bioinformatics Platform](https://www.monash.edu/researchinfrastructure/bioinformatics) and [Monash Data Fluency](https://www.monash.edu/data-fluency) by Paul Harrison.

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="figures/CC-BY.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.

## Source code

* [GitHub repository](https://github.com/MonashDataFluency/r-linear)

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<div style="font-size: 75%">
[Solutions to challenges](topics/solutions.html)
</div>

<p style="margin-top: 2em; text-align: right">
<a href="https://www.monash.edu/researchinfrastructure/bioinformatics"><img src="figures/MBP-banner.png" width="675"></a>
</p>