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
- single cell
- rna seq
- high throughput
- induced apoptosis
- copy number
- high resolution
- machine learning
- genome wide identification
- electronic health record
- cell cycle arrest
- big data
- deep learning
- signaling pathway
- cell proliferation
- mesenchymal stem cells
- climate change
- oxidative stress
- cell death
- mass spectrometry
- human health
- risk assessment
- data analysis
- neural network