BbioRxiv29 Jul 2023

Directly selecting differentially expressed genes for single-cell clustering analyses

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Abstract

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Festem jointly selects DEGs and clusters scRNA‑seq data, boosting gene‑selection accuracy and cell‑type identification.

What’s new

i.Introduces Festem, a unified method for gene selection and DEG analysis in scRNA‑seq.
ii.Demonstrates high precision and recall for DEG detection in extensive simulations and real datasets.
iii.Shows Festem uncovers cell types missed by other methods and identifies novel prognostic biomarkers in intrahepatic cholangiocarcinoma.

How they did it

Festem examines each gene’s clustering information by modeling its marginal distribution as a mixture of cell‑type‑specific distributions. This allows direct selection of clustering‑informative DEGs and eliminates the selection‑bias inherent in separate gene‑selection and clustering steps.

What they found

Achieves high precision and recall for DEG detection (specific values not reported).
Enables more accurate clustering and cell‑type identification compared to existing methods.
Identifies CD8+ T‑cell subtypes with novel marker genes that serve as prognostic biomarkers in a large intrahepatic cholangiocarcinoma dataset.

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