scCNMF: an integrated analysis model for paired single-cell RNA sequencing and assay for transposase-accessible chromatin sequencing data leveraging cell similarity and cis-regulatory potential
Yufei Zhang, Qiongyu Sheng, Huiran Zhan, Yiyuan Guo, Xiaoran Shi, Jing Qi
TL;DR
This paper introduces scCNMF, a new method for combining single-cell RNA and chromatin accessibility data to better understand cell states and gene regulation.
Contribution
scCNMF is a novel model that integrates scRNA-seq and scATAC-seq data by leveraging cell similarity and regulatory information.
Findings
scCNMF improves cell embeddings and clustering accuracy compared to existing methods.
The model enables biomarker identification and enhances the quality of scATAC-seq data.
scCNMF demonstrates competitive performance on real-world datasets.
Abstract
The integrated analysis of paired single-cell RNA sequencing (scRNA-seq) and single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) data is crucial for accurately characterizing cellular states and reconstructing gene regulatory networks. However, most integration methods fail to simultaneously consider the high sparsity of scATAC-seq data and regulatory interactions at the cellular level, limiting the biological interpretability and accuracy of their integration results. In this study, we present scCNMF, a novel model for the integrated analysis of paired scRNA-seq and scATAC-seq data. scCNMF based on the non-negative matrix factorization model, jointly incorporates cell similarity structures and prior regulatory information, leading to improved cell embeddings and enhanced clustering accuracy. We evaluate scCNMF on multiple real-world datasets and…
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Taxonomy
TopicsSingle-cell and spatial transcriptomics · Genomics and Chromatin Dynamics · Cell Image Analysis Techniques