Biophysically Interpretable Inference of Cell Types from Multimodal Sequencing Data.

ABSTRACT: Multimodal, single-cell genomics technologies enable simultaneous capture of multiple facets of DNA and RNA processing in the cell. This creates opportunities for transcriptome-wide, mechanistic studies of cellular processing in heterogeneous cell types, with applications ranging from inferring kinetic differences between cells, to the role of stochasticity in driving heterogeneity. However, current methods for determining cell types or 'clusters' present in multimodal data often rely on ad hoc or independent treatment of modalities, and assumptions ignoring inherent properties of the count data. To enable interpretable and consistent cell cluster determination from multimodal data, we present meK-Means (mechanistic K-Means) which integrates modalities and learns underlying, shared biophys

SUBMITTER: Chari T 

PROVIDER: S-EPMC10516047 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature