Are batch effects still relevant in the age of big data?. Issue 9 (September 2022) - Goh, Wilson Wen Bin; Yong, Chern Han; Wong, Limsoon
Abstract : Batch effects (BEs) are technical biases that may confound analysis of high-throughput biotechnological data. BEs are complex and effective mitigation is highly context-dependent. In particular, the advent of high-resolution technologies such as single-cell RNA sequencing presents new challenges. We first cover how BE modeling differs between traditional datasets and the new data landscape. We also discuss new approaches for measuring and mitigating BEs, including whether a BE is significant enough to warrant correction. Even with the advent of machine learning and artificial intelligence, the increased complexity of next-generation biotechnological data means increased complexities in BE management. We forecast that BEs will not only remain relevant in the age of big data but will become even more important. Highlights: As data expands in size and complexity, batch effect correction will become even more important. Batch effect-correction methods based on machine learning approaches will become commonplace. Better batch effect visualization methods are needed.