ScHiClassifier: a deep learning framework for cell type prediction by fusing multiple feature sets from single-cell Hi-C data.

ABSTRACT: Single-cell high-throughput chromosome conformation capture (Hi-C) technology enables capturing chromosomal spatial structure information at the cellular level. However, to effectively investigate changes in chromosomal structure across different cell types, there is a requisite for methods that can identify cell types utilizing single-cell Hi-C data. Current frameworks for cell type prediction based on single-cell Hi-C data are limited, often struggling with features interpretability and biological significance, and lacking convincing and robust classification performance validation. In this study, we propose four new feature sets based on the contact matrix with clear interpretability and biological significance. Furthermore, we develop a novel deep learning framework named scHiClassifie

SUBMITTER: Zhou X 

PROVIDER: S-EPMC11744636 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature