ArticleNAR genomics and bioinformatics2024
scATAcat: cell-type annotation for scATAC-seq data.
Article in NAR genomics and bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- scMOG: A graph neural network method for regulatory relationship-preserving single-cell multi-omics integration.Quantitative biology (Beijing, China) · 2026Article
- scATAnno: Automated Cell Type Annotation for Single-cell ATAC-seq Data.Genomics, proteomics & bioinformatics · 2026Article
- Reporter CRISPR screens decipher cis-regulatory and trans-regulatory principles at the Xist locus.Nature structural & molecular biology · 2025Article
- Fast clustering and cell-type annotation of scATAC data using pre-trained embeddings.NAR genomics and bioinformatics · 2024Article
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Authors and funding
2 authors.
Funding
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Abstract
Cells whose accessibility landscape has been profiled with scATAC-seq cannot readily be annotated to a particular cell type. In fact, annotating cell-types in scATAC-seq data is a challenging task since, unlike in scRNA-seq data, we lack knowledge of 'marker regions' which could be used for cell-type annotation. Current annotation methods typically translate accessibility to expression space and rely on gene expression patterns. We propose a novel approach, scATAcat, that leverages characterized bulk ATAC-seq data as prototypes to annotate scATAC-seq data. To mitigate the inherent sparsity of single-cell data, we aggregate cells that belong to the same cluster and create pseudobulk. To demonstrate the feasibility of our approach we collected a number of datasets with respective annotations to quantify the results and evaluate performance for scATAcat. scATAcat is available as a python package at https://github.com/aybugealtay/scATAcat.
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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.