SifiNet: a robust and accurate method to identify feature gene sets and annotate cells

SifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cell...

Full description

Saved in:
Bibliographic Details
Published in:Nucleic acids research 2024-05, Vol.52 (9), p.e46-e46
Main Authors: Gao, Qi, Ji, Zhicheng, Wang, Liuyang, Owzar, Kouros, Li, Qi-Jing, Chan, Cliburn, Xie, Jichun
Format: Article
Language:English
Subjects:
Algorithms
Chromatin Immunoprecipitation Sequencing - methods
Cluster Analysis
Computational Biology - methods
Gene Expression Profiling - methods
Humans
Methods
Molecular Sequence Annotation
Sequence Analysis, RNA - methods
Single-Cell Analysis - methods
Software
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:SifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cellular annotation pipelines, thereby circumventing potential inaccuracies in clustering that may compromise subsequent analyses. Consequently, SifiNet has demonstrated superior performance in multiple experimental datasets compared with other state-of-the-art methods. SifiNet can analyze both single-cell RNA and ATAC sequencing data, thereby rendering comprehensive multi-omic cellular profiles. It is conveniently available as an open-source R package.
ISSN:0305-1048
1362-4962
DOI:10.1093/nar/gkae307