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Modeling fragment counts improves single-cell ATAC-seq analysis

bioRxiv (Cold Spring Harbor Laboratory)(2022)10.1101/2022.05.04.490536Source: DataRank Database

Modeling fragment counts improves single-cell ATAC-seq analysis is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2022). On theSindex it has a DataRank of 0.661. It has been cited 14 times, with 11 citing works in its 1-hop citation network.

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0.661DataRank · unranked

Scored on demand from live citation data

Open Access14 citations · 11 citing works in its network

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (2 grants)OA: greenTopics (23)IDs (PubMed, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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DataRank Breakdown

Base Score 61%Citation Network 39%

Base Score Contribution

0.406

From this paper's citation signal

Citation Network Contribution

0.255

From 9 citing papers with measurable signal

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Top 5 citers driving the network score

Ranked by each citer's contribution to N(p) — log1p(C_q) divided by its reference count — out of 11 citers.

multiDGD: A versatile deep generative model for multi-omics data
Nature Communications202432 citations61 referencesContributes 0.057
CellCharter reveals spatial cell niches associated with tissue remodeling and cell plasticity
Nature Genetics2023185 citations100 referencesContributes 0.052
Benchmarking computational methods for single-cell chromatin data analysis
Genome biology202417 citations56 referencesContributes 0.052
Benchmarking computational methods for single-cell chromatin data analysis
bioRxiv (Cold Spring Harbor Laboratory)20234 citations47 referencesContributes 0.034
CellCharter reveals spatial cell niches associated with tissue remodeling and cell plasticity
bioRxiv (Cold Spring Harbor Laboratory)202315 citations97 referencesContributes 0.029

Authors (5)

David S. Fischer ORCID,Vicente A. Yépez ORCID,Fabian Joachim Theis ORCID,Julia-Stefanie Frick ORCID,Laura D. Martens ORCID

Live enrichment

PubMed:30945415 OpenAlex:https://openalex.org/W4229083873

Deutsche Forschungsgemeinschaft

Grant: unidentified

unidentified

European Commission

Grant: 101054957

Learning and modeling the molecular response of single cells to drug perturbations

greenLicense: cc-by-nc-nd

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2022/05/04/2022.05.04.490536.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2022.05.04.490536

repositoryhttps://doi.org/10.1101/2022.05.04.490536

https://doi.org/10.1038/s41592-023-02112-6

Fields of Study

Single-cell and spatial transcriptomicsCancer Genomics and DiagnosticsT-cell and B-cell Immunology0301 basic medicine0303 health sciences03 medical and health sciences

Keywords

Fragment (logic)Computational biologyIdentification (biology)Cluster analysisChromatinBiologyData miningComputer scienceGeneticsArtificial intelligenceDNAAlgorithmChromatin Immunoprecipitation SequencingHigh-Throughput Nucleotide SequencingSequence Analysis, DNASingle-Cell AnalysisBrief Communication