March 21, 2024Open Access

Batch correction methods used in single cell RNA-sequencing analyses are often poorly calibrated

View Full Paper

Ask AI

Bookmark

Authors

SASindri Emmanúel AntonssonUniversity of Iceland PMPáll MelsteddeCODE Genetics (Iceland)

Discussion

Member takes

Implication

Benchmark evaluation reveals widespread calibration artifacts across batch correction algorithms in single-cell RNA-sequencing datasets, highlighting Harmony as the sole reliable tool.

Key Points

Most batch correction algorithms for scRNA-seq data are poorly calibrated, with MNN, SCVI, and LIGER introducing measurable artifacts that alter cell distances.
Assessment of seven widely used batch correction methods evaluated cell-distance distortions and cluster-level artifacts across single-cell RNA-sequencing datasets.
Highlights Harmony as the only consistently reliable method across all tests, supporting its exclusive recommendation for batch correction in single-cell RNA-sequencing.

Cite This Study

Antonsson et al. (2024) studied this question.

synapsesocial.com/papers/68e72f63b6db6435876a94c6 https://doi.org/10.1101/2024.03.19.585562

View Full Paper

Ask AI

Bookmark

Implication

Benchmark evaluation reveals widespread calibration artifacts across batch correction algorithms in single-cell RNA-sequencing datasets, highlighting Harmony as the sole reliable tool.

Batch correction methods used in single cell RNA-sequencing analyses are often poorly calibrated

Discussion

Member takes

Implication

Key Points

Cite This Study

Batch correction methods used in single cell RNA-sequencing analyses are often poorly calibrated

Discussion

Member takes

Implication

Key Points

Cite This Study

Also Consider

Also Consider