HiCImpute: A Bayesian hierarchical model for identifying structural zeros and enhancing single cell Hi-C data.

ABSTRACT: Single cell Hi-C techniques enable one to study cell to cell variability in chromatin interactions. However, single cell Hi-C (scHi-C) data suffer severely from sparsity, that is, the existence of excess zeros due to insufficient sequencing depth. Complicating the matter further is the fact that not all zeros are created equal: some are due to loci truly not interacting because of the underlying biological mechanism (structural zeros); others are indeed due to insufficient sequencing depth (sampling zeros or dropouts), especially for loci that interact infrequently. Differentiating between structural zeros and dropouts is important since correct inference would improve downstream analyses such as clustering and discovery of subtypes. Nevertheless, distinguishing between these two types of

SUBMITTER: Xie Q 

PROVIDER: S-EPMC9232133 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature