ArticleJournal of computational biology : a journal of computational molecular cell biology2022
UNIFAN: A Tool for Unsupervised Single-Cell Clustering and Annotation.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Mapping Cell Identity from scRNA-seq: A primer on computational methods.Computational and structural biotechnology journal · 2025Review
- scSemiProfiler: Advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning.Nature communications · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
UNIFAN is an unsupervised cell type annotation tool for single-cell RNA sequencing data (scRNA-seq). Given single-cell expression data as input, UNIFAN outputs cell clusters as well as annotations for each cluster. The clustering process utilizes information on pathways and biological processes and these are also used to annotate the resulting clusters. In this software article, we focus on how to install UNIFAN and on the main steps involved in using UNIFAN for cell type annotations.
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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.