Dimension reduction, cell clustering, and cell–cell communication inference for single-cell transcriptomics with DcjComm

Advances in single-cell transcriptomics provide an unprecedented opportunity to explore complex biological processes. However, computational methods for analyzing single-cell transcriptomics still have room for improvement especially in dimension reduction, cell clustering, and cell-cell communication inference. Herein, we propose a versatile method, named DcjComm, for comprehensive analysis of single-cell transcriptomics. DcjComm detects functional modules to explore expression patterns and performs dimension reduction and clustering to discover cellular identities by the non-negative matrix factorization-based joint learning model. DcjComm then infers cell-cell communication by integrating ligand-receptor pairs, transcription factors, and target genes. DcjComm demonstrates superior performance compared to state-of-the-art methods.

Authors

Institutions

Publication Details

Journal
Genome biology
Published
2024-09-09
DOI
https://doi.org/10.1186/s13059-024-03385-6
Citations
10
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
2.34

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Dimension reduction, cell clustering, and cell–cell communication inference for single-cell transcriptomics with DcjComm

Jinhao Que, Renjie Tan, Qian Ding, Yideng Cai et al.
10 citations
Genome biology
Single-cell and spatial transcriptomics
2.34
article

Dimension reduction, cell clustering, and cell–cell communication inference for single-cell transcriptomics with DcjComm

Jinhao Que, Renjie Tan, Qian Ding, Yideng Cai, Zhaochun Xu, Fenglan Pang, Guangfu Xue, Xiyun Jin, Yusong Liu, Haoxiu Sun, Hongxin Liu, Meng Luo, Pingping Wang, Yi Lin, Yuexin Yang, Wenyi Yang, Qinghua Jiang
article en
10 citations

Abstract

Advances in single-cell transcriptomics provide an unprecedented opportunity to explore complex biological processes. However, computational methods for analyzing single-cell transcriptomics still have room for improvement especially in dimension reduction, cell clustering, and cell-cell communication inference. Herein, we propose a versatile method, named DcjComm, for comprehensive analysis of single-cell transcriptomics. DcjComm detects functional modules to explore expression patterns and performs dimension reduction and clustering to discover cellular identities by the non-negative matrix factorization-based joint learning model. DcjComm then infers cell-cell communication by integrating ligand-receptor pairs, transcription factors, and target genes. DcjComm demonstrates superior performance compared to state-of-the-art methods.

Genome biologyVol. 25(1)
Harbin Medical University (CN), Harbin Institute of Technology (CN)
National Natural Science Foundation of China, National Science and Technology Major Project
Openalex Percentile: Top 11%
Single-cell and spatial transcriptomics
2.34
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.