batcheffect

Batcheffect, often called batch effect, refers to systematic non-biological variation introduced when samples are processed in distinct groups or batches. Differences in sample handling, reagents, instrumentation, operators, or processing times can produce artifacts that appear as biological differences if not accounted for.

Batch effects are common in high-throughput assays such as microarrays, RNA sequencing, proteomics, and metabolomics, as well as large clinical data collections. They can cause clustering by batch in exploratory analyses, inflate or obscure real signals, and lead to spurious associations or reduced statistical power.

Detection involves exploratory data analysis, such as principal component analysis, which may reveal separation by batch rather than by experimental groups. Statistical tests and diagnostic plots, including surrogate variables and batch-aware models, can indicate batch structure.

Mitigation strategies include experimental design features like randomizing sample processing across batches, balancing biological groups within batches, and including batch as a covariate in statistical models. Computational methods for batch correction, such as ComBat, surrogate variable analysis (SVA), Harman, or removal of unwanted variation (RUV), adjust data to reduce batch-related variance while preserving biological signal. Validation with independent data or replication is recommended to assess residual effects and ensure biological conclusions are robust.

Limitations include the risk of removing genuine biological variation if batch and biology are confounded; over-correction can introduce artifacts. Therefore, batch correction should be applied cautiously and accompanied by proper experimental design.