SifiNet: a robust and accurate method to identify feature gene sets and annotate cells.
Qi GaoZhicheng JiLiuyang WangDadong ZhangQi-Jing LiCliburn ChanJichun XiePublished in: Nucleic acids research (2024)
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.
Keyphrases
- cell death
- single cell
- rna seq
- cell cycle arrest
- high throughput
- copy number
- genome wide
- high resolution
- induced apoptosis
- machine learning
- genome wide identification
- deep learning
- big data
- gene expression
- dna methylation
- mass spectrometry
- oxidative stress
- endoplasmic reticulum stress
- artificial intelligence
- mesenchymal stem cells
- genome wide analysis