ScSemiProfiler: Advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning.

ABSTRACT: Single-cell sequencing is a crucial tool for dissecting the cellular intricacies of complex diseases. Its prohibitive cost, however, hampers its application in expansive biomedical studies. Traditional cellular deconvolution approaches can infer cell type proportions from more affordable bulk sequencing data, yet they fall short in providing the detailed resolution required for single-cell-level analyses. To overcome this challenge, we introduce "scSemiProfiler", an innovative computational framework that marries deep generative models with active learning strategies. This method adeptly infers single-cell profiles across large cohorts by fusing bulk sequencing data with targeted single-cell sequencing from a few rigorously chosen representatives. Extensive validation across heterogeneous

SUBMITTER: Wang J 

PROVIDER: S-EPMC11252419 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature