Remove between-study batch effects for pooled expression analyses

Combines all studies into one expression matrix and removes study-level batch effects while preserving the biological condition effect.

Usage

mx_remove_batch(
  studies,
  method = c("ComBat-seq", "ComBat", "limma"),
  covariate = "condition"
)

Arguments

studies

A named list of metaXpressStudy objects.

method

Character scalar. "ComBat-seq" (default; negative binomial model on counts), "ComBat" (empirical Bayes on log2 CPM) or "limma" (removeBatchEffect on log2 CPM).

covariate

Name of a colData column whose effect must be preserved. Default: "condition". Use NULL only if you understand that condition effects confounded with study will be removed.

Value

The input list of studies, restricted to their common genes, with the corrected values in a new assay: "combat_seq_counts" for ComBat-seq or "log2cpm_batch_corrected" otherwise. The raw "counts" assay is untouched.

Details

Batch correction is not required, and should not be used, before per-study DE: mx_de analyses each study separately, so between-study batch effects never enter the per-study fold changes, and feeding batch-corrected values into count models invalidates their variance assumptions. Use this function only for pooled visualizations (PCA, heatmaps), consensus co-expression or other analyses on a merged matrix. Batch is defined as study of origin.

References

Johnson, W.E., Li, C. & Rabinovic, A. (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8(1), 118–127. doi:10.1093/biostatistics/kxj037

Zhang, Y., Parmigiani, G. & Johnson, W.E. (2020) ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genomics and Bioinformatics, 2(3), lqaa078. doi:10.1093/nargab/lqaa078

Nygaard, V., Rødland, E.A. & Hovig, E. (2016) Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses. Biostatistics, 17(1), 29–39. doi:10.1093/biostatistics/kxv027

See also

mx_normalize

Examples

if (FALSE) { # \dontrun{
  pooled <- mx_remove_batch(studies, method = "limma")
  SummarizedExperiment::assay(pooled[[1]], "log2cpm_batch_corrected")
} # }