topic/confounding

Tackling the widespread and critical impact of batch effects in high-throughput data

Asymptotic conditional singular value decomposition for high-dimensional genomic data

Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis

Collider scope: when selection bias can substantially influence observed associations

Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses

OSAT: a tool for sample-to-batch allocations in genomics experiments

Randomization in Laboratory Procedure Is Key to Obtaining Reproducible Microarray Results

DESeq2 vignette — "Analyzing RNA-seq data with DESeq2"

The SVA package for removing batch effects and other unwanted variation in high-throughput experiments

Design of High-Throughput Experiments and their Analyses

High-Throughput Count Data and Generalized Linear Models

batch-aliased-with-condition