StabCell: Stability selection for clustering and marker detection in single-cell RNA sequencing

StabCell stability selection for scRNA-seq integrates clustering with marker detection to produce stable clusters and stable marker genes, reducing false positives under low signal-to-noise and low sequencing depth.

• Why it matters: Conventional scRNA-seq workflows cluster cells and then test markers on the same data, creating selective inference that can overstate differences.
• What they did: StabCell repeatedly clusters and runs differential expression on complementary random subsamples, using the results to compute stable clustering, cell-specific stability scores, and stable marker gene sets.
• The result: In simulations, it keeps empirical per-family error rate (PFER) below prespecified thresholds and selects fewer false positive markers than the conventional approach, while remaining competitive for marker detection and improving robustness on an iPSC-to-cardiomyocyte dataset.

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