Managing Batch Effects in Biological Data

Learn to identify, understand, and correct batch effects in biological datasets through this 27-minute conference talk from the Computational Genomics Summer Institute. Explore the fundamental challenges that batch effects pose in biological data analysis, where technical variations between experimental batches can confound true biological signals. Discover empirical Bayes methods for adjusting batch effects in microarray expression data, examine fast and accurate integration techniques for single-cell data using Harmony, and understand how to recover biological signals that may be lost during batch correction processes with CellANOVA. Gain practical insights into when and how to apply different batch correction methods, the trade-offs between removing technical artifacts and preserving genuine biological variation, and best practices for validating batch correction results in genomics research.