ArticleNucleic acids research2024
SifiNet: a robust and accurate method to identify feature gene sets and annotate cells.
Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- B-Lightning: using bait genes for marker gene hunting in single-cell data with complex heterogeneity.Briefings in bioinformatics · 2024Article
- eSVD-DE: cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings.BMC bioinformatics · 2024Article
- eSVD-DE: Cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings.bioRxiv : the preprint server for biology · 2024Article
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7 authors.
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Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.