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MIG Workshop: Managing batch effects in biological studies

Author: Yiwen (Eva) Wang

Tutor: Xiaochen Zhang

Audience Prerequisites Duration
Biologists, Computational biologists Intro to R, Intro to Experimental Designs, Intro to Linear Models ~ 3 hours

Description

This repository includes materials for our workshop 'Managing batch effects in biological studies'. This workshop introduces commonly encountered sources of batch effects, batch x treatment designs and the scale of batch influence. We will discuss the suitable applications and limitations of existing methods through illustrative case studies. Practical guidelines will also be provided for preprocessed input data, including batch-effect detection and management, and evaluation of method effectiveness through visual and numerical approaches. While our examples are based on microbiome data, the concepts presented in the workshop are applicable to all types of omics data.

Installation Requirements

Install R first, then RStudio. Download the most recent version of R and RStudio using the links below:

  • R (Preferably R version > 4.0)
  • RStudio

Install the R packages. Type the R command lines:

# CRAN packages
cran.pkgs <- c('pheatmap', 'vegan', 'ruv', 'ggplot2', 
               'performance', 'gridExtra')

install.packages(cran.pkgs)

# Bioconductor packages
bioc.pkgs <- c('mixOmics', 'sva', 'limma', 'Biobase', 'metagenomeSeq', 
               'PLSDAbatch')

if(!require("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install(bioc.pkgs)  

# Test if packages have been installed
sapply(c(cran.pkgs, bioc.pkgs), require, character.only = TRUE)

Materials

Click here to access the slides.

Click here to access the HTML workshop document.

Click here to access the Rscript.

Data

All data used for the workshop are in Rdata.

License

This repository is licensed under the GNU Affero General Public License v3.0.

References

[1] Wang, Y., & Lê Cao, K. A. (2020). Managing batch effects in microbiome data. Briefings in bioinformatics, 21(6), 1954-1970.

[2] Wang, Y., & Lê Cao, K. A. (2023). PLSDA-batch: a multivariate framework to correct for batch effects in microbiome data. Briefings in Bioinformatics, 24(2), bbac622.