MOSCATO: a supervised approach for analyzing multi-Omic single-Cell data.

ABSTRACT:

Background

Advancements in genomic sequencing continually improve personalized medicine, and recent breakthroughs generate multimodal data on a cellular level. We introduce MOSCATO, a technique for selecting features across multimodal single-cell datasets that relate to clinical outcomes. We summarize the single-cell data using tensors and perform regularized tensor regression to return clinically-associated variable sets for each 'omic' type.

Results

Robustness was assessed over simulations based on available single-cell simulation methods, and applicability was assessed through an example using CITE-seq data to detect genes associated with leukemia. We find that MOSCATO performs favorably in selecting network features while also shown to be applicable to real multimodal si

SUBMITTER: Towle-Miller LM 

PROVIDER: S-EPMC9351124 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature