Library size-stabilized metacells construction enhances co-expression network analysis in single-cell data.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) deciphers cell type-specific co-expression networks to resolve biological functions but remains constrained by data sparsity and compositional biases. Conventional metacells construction strategies mitigate sparsity by aggregating transcriptionally similar cells but often neglect systematic biases introduced by compositional data. This problem leads to spurious co-expression correlations and obscuring biologically meaningful interactions. Through mathematical modeling and simulations, we demonstrate that uncontrolled library size variance in traditional metacells inflates false-positive correlations and distorts co-expression networks. Here, we present LSMetacell (Library Size-stabilized Metacells), a computational framework that explicitly stabilizes

SUBMITTER: Zhang T 

PROVIDER: S-EPMC12626273 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature