ScHiCSRS: a self-representation smoothing method with Gaussian mixture model for imputing single cell Hi-C data.

ABSTRACT:

Background

Single cell Hi-C (scHi-C) techniques make it possible to study cell-to-cell variability, but excess of zeros are makes scHi-C matrices extremely sparse and difficult for downstream analyses. The observed zeros are a combination of two events: structural zeros for which two loci never interact due to underlying biological mechanisms, or dropouts (sampling zeros) where two loci interact but not captured due to insufficient sequencing depth. Although data quality improvement approaches have been proposed, little has been done to differentiate these two types of zeros, even though such a distinction can greatly benefit downstream analysis such as clustering.

Results

We propose scHiCSRS, a self-representation smoothing method that improves data quality, and a Gaussian

SUBMITTER: Xie Q 

PROVIDER: S-EPMC12093726 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature