ArticleNucleic acids research2019
Classifying cells with Scasat, a single-cell ATAC-seq analysis tool.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- A generic reference defined by consensus peaks for single-cell ATAC-seq data analysis.Nature communications · 2026Article
- Computational Analyses and Challenges of Single-cell ATAC-seq.Genomics, proteomics & bioinformatics · 2025Review
- Application of computational algorithms for single-cell RNA-seq and ATAC-seq in neurodegenerative diseases.Briefings in functional genomics · 2025Review
- Integration of unpaired single cell omics data by deep transfer graph convolutional network.PLoS computational biology · 2025Article
- Best practices for differential accessibility analysis in single-cell epigenomics.Nature communications · 2024Article
- Fast clustering and cell-type annotation of scATAC data using pre-trained embeddings.NAR genomics and bioinformatics · 2024Article
- Single-cell ATAC sequencing identifies sleepy macrophages during reciprocity of cytokines inMicrobiology spectrum · 2024Article
- GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership.Genome biology · 2023Article
- GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership.bioRxiv : the preprint server for biology · 2023Article
- Cellcano: supervised cell type identification for single cell ATAC-seq data.Nature communications · 2023Article
- AgeAnno: a knowledgebase of single-cell annotation of aging in human.Nucleic acids research · 2023Article
- Characterizing cis-regulatory elements using single-cell epigenomics.Nature reviews. Genetics · 2023Review
- Temporally divergent regulatory mechanisms govern neuronal diversification and maturation in the mouse and marmoset neocortex.Nature neuroscience · 2022Article
- scEpiLock: A Weakly Supervised Learning Framework forBiomolecules · 2022Article
- scEnhancer: a single-cell enhancer resource with annotation across hundreds of tissue/cell types in three species.Nucleic acids research · 2022Article
- Single-Cell Analysis of the Transcriptome and Epigenome.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Feature selection revisited in the single-cell era.Genome biology · 2021Review
- Single-cell chromatin state analysis with Signac.Nature methods · 2021Article
- Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research.Biomedicines · 2021Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
ATAC-seq is a recently developed method to identify the areas of open chromatin in a cell. These regions usually correspond to active regulatory elements and their location profile is unique to a given cell type. When done at single-cell resolution, ATAC-seq provides an insight into the cell-to-cell variability that emerges from otherwise identical DNA sequences by identifying the variability in the genomic location of open chromatin sites in each of the cells. This paper presents Scasat (single-cell ATAC-seq analysis tool), a complete pipeline to process scATAC-seq data with simple steps. Scasat treats the data as binary and applies statistical methods that are especially suitable for binary data. The pipeline is developed in a Jupyter notebook environment that holds the executable code along with the necessary description and results. It is robust, flexible, interactive and easy to extend. Within Scasat we developed a novel differential accessibility analysis method based on information gain to identify the peaks that are unique to a cell. The results from Scasat showed that open chromatin locations corresponding to potential regulatory elements can account for cellular heterogeneity and can identify regulatory regions that separates cells from a complex population.
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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.