ScNCL: transferring labels from scRNA-seq to scATAC-seq data with neighborhood contrastive regularization.

ABSTRACT:

Motivation

scATAC-seq has enabled chromatin accessibility landscape profiling at the single-cell level, providing opportunities for determining cell-type-specific regulation codes. However, high dimension, extreme sparsity, and large scale of scATAC-seq data have posed great challenges to cell-type identification. Thus, there has been a growing interest in leveraging the well-annotated scRNA-seq data to help annotate scATAC-seq data. However, substantial computational obstacles remain to transfer information from scRNA-seq to scATAC-seq, especially for their heterogeneous features.

Results

We propose a new transfer learning method, scNCL, which utilizes prior knowledge and contrastive learning to tackle the problem of heterogeneous features. Briefly, scNCL transforms scATAC-

SUBMITTER: Yan X 

PROVIDER: S-EPMC10457667 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature