Pilot corpus: NIH-funded biomedical datasets. Any DOI can be scored on demand — network effects sharpen as coverage grows.

Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data

bioRxiv (Cold Spring Harbor Laboratory)(2020)10.1101/2020.12.01.405886Source: DataRank Database

Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2020). On theSindex it has a DataRank of 0. It has been cited 28 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access28 citations

Download PDF

Cite:

DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (3 grants)OA: greenTopics (27)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)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

Run a calibrated FAIR evaluation for this paper →

We only score data papers we can read in full — never from an abstract alone.

Authors (3)

Philipp Berens ORCID,Dmitry Kobak ORCID,Jan Lause ORCID

Live enrichment

PubMed:23304386 OpenAlex:https://openalex.org/W3109556021

Deutsche Forschungsgemeinschaft

Grant: unidentified

unidentified

National Institutes of Health

Grant: 5U19MH114830-05

A comprehensive whole-brain atlas of cell types in the mouse

National Institutes of Health

Grant: 1U19MH114830-01

A comprehensive whole-brain atlas of cell types in the mouse

greenLicense: cc-by-nc-nd

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2021/07/31/2020.12.01.405886.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2020.12.01.405886

repositoryhttps://doi.org/10.1101/2020.12.01.405886

https://doi.org/10.1186/s13059-021-02451-7

Fields of Study

Single-cell and spatial transcriptomicsGene expression and cancer classificationStatistical Methods and Inference0206 medical engineering02 engineering and technology

Keywords

OverdispersionNormalization (sociology)Count dataStatisticsHeteroscedasticityMathematicsPoisson distributionNegative binomial distributionComputer sciencePrincipal Component AnalysisQH301-705.5Sequence Analysis, RNAOrganogenesisShort ReportQH426-470RetinaDatabases, GeneticGeneticsRegression AnalysisRNA-SeqBiology (General)Algorithms