Crafted experiments to evaluate feature selection methods for single-cell RNA-seq data.

ABSTRACT: While numerous methods have been developed for analyzing scRNA-seq data, benchmarking various methods remains challenging. There is a lack of ground truth datasets for evaluating novel gene selection and/or clustering methods. We propose the use of crafted experiments, a new approach based upon perturbing signals in a real dataset for comparing analysis methods. We demonstrate the effectiveness of crafted experiments for evaluating new univariate distribution-oriented suite of feature selection methods, called GOF. We show GOF selects features that robustly identify crafted features and perform well on real non-crafted data sets. Using varying ways of crafting, we also show the context in which each GOF method performs the best. GOF is implemented as an open-source R package and fre

SUBMITTER: Liu S 

PROVIDER: S-EPMC11920870 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature