How do I perform a covariate correction in ROSALIND for RNA-seq data and how is it calculated?

You can eliminate confounding and undesirable "extra" experimental variables in Gene Expression experiments with Covariate Correction. ROSALIND intelligently determines which experiment attributes may be valid covariates, reducing complexity for this advanced capability.


For example, an experiment evaluating two treatments across three cell lines may benefit from covariate correction to eliminate confounding differences between cell lines. Without covariate correction, six comparisons for each treatment and cell line would be required to discover meaningful insights in differential expression. Using covariate correction, a single comparison enables the exploration of the treatment and control across all three cell lines, simultaneously.


The DESeq2 covariate model is used to perform the covariate correction.


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