Given a SummarizedExperiment container, what is the quickest way to identify a batch effect from one of the covariates found within the DataFrame in the colData slot? Right now I am plotting the principal components and colouring the samples by each of the covariates. I then have to check the first few components for any separation and colour by the covariates to see which is responsible. I have a large number of libraries I have to check and was wondering if there was a Bioconductor package to perform this step? I've looked at svaseq and RUVseq but I can't see that they produce any QC plots which will tell me if an effect is present and which covariate is responsible?
Question: Quick way to Identify batch effect from covariates?
4.0 years ago by
James Ashmore • 2.9k
UK/Edinburgh/MRC Centre for Regenerative Medicine
James Ashmore • 2.9k wrote:
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4.0 years ago by
chris86 • 330
United Kingdom, London
chris86 • 330 wrote:
Two methods are best used for analysis of batch effects.
- PCA with annotation - as you are doing, but relies on manual visual analysis
- PVCA - https://www.bioconductor.org/packages/release/bioc/html/pvca.html - this fits a mixed effects model to the principle components then looks at effects of various co variates, quantitatively. It is called principle variance components analysis.
Update: I highly recommend https://github.com/dswatson/bioplotr/blob/master/R/plot_drivers.R, this function it makes a great plot for examining for batch effects and more.
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