A systematic evaluation of highly variable gene selection methods for single-cell RNA-sequencing.

Abstract excerpt

Selecting highly variable genes (HVGs) is a critical step in single-cell RNA sequencing data analysis. We benchmark 47 HVG selection methods across 19 datasets, 18 evaluation criteria, and 5,358 settings. Hybrid methods – mixtures of multiple baseline HVG approaches – robustly outperform individual methods. Based on these findings, we develop mixHVG, an improved HVG selection strategy that integrates top-ranked...

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