Integrating scRNA-seq and scATAC-seq with inter-type attention heterogeneous graph neural networks.

ABSTRACT: Single-cell multi-omics techniques, which enable the simultaneous measurement of multiple modalities such as RNA gene expression and Assay for Transposase-Accessible Chromatin (ATAC) within individual cells, have become a powerful tool for deciphering the intricate complexity of cellular systems. Most current methods rely on motif databases to establish cross-modality relationships between genes from RNA-seq data and peaks from ATAC-seq data. However, these approaches are constrained by incomplete database coverage, particularly for novel or poorly characterized relationships. To address these limitations, we introduce single-cell Multi-omics Integration (scMI), a heterogeneous graph embedding method that encodes both cells and modality features from single-cell RNA-seq and ATAC-seq data i

SUBMITTER: Cai L 

PROVIDER: S-EPMC11725394 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature