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Per-sample standardization and asymmetric winsorization lead to accurate clustering of RNA-seq expression profiles

Bioinformatics(2021)10.1093/bioinformatics/btab091Source: DataRank Database

Per-sample standardization and asymmetric winsorization lead to accurate clustering of RNA-seq expression profiles is a research paper published in Bioinformatics (2021). On theSindex it has a DataRank of 0. It has been cited 8 times.

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0DataRank · unranked

Scored on demand from live citation data

Open Access8 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (4 grants)Impact (FWCI 0.37)OA: greenTopics (16)IDs (PubMed, PMC, 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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Authors (2)

Stefano Maria Pagnotta ORCID,Davide Risso ORCID

Live enrichment

PubMed:33560368 PMC:PMC8388024 OpenAlex:https://openalex.org/W3126537113

Silicon Valley Community Foundation

Grant: CZF2019-002443

National Institutes of Health

Grant: 2U24CA180996

AIRC Foundation

Grant: IG 2018-ID.21846

NCI NIH HHS

Grant: U24 CA180996

FWCI

0.37

Citation Percentile

0.5%

Citation Trend

2021

2023

2024

2025

2026

greenLicense: cc-by

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/8388024

publisherhttp://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btab091/36394843/btab091.pdf

publisherhttps://academic.oup.com/bioinformatics/article-pdf/37/16/2356/50339623/btab091.pdf

journalhttps://doi.org/10.1093/bioinformatics/btab091

Fields of Study

Single-cell and spatial transcriptomicsGenomics and Phylogenetic StudiesGene expression and cancer classification

Keywords

Cluster analysisComputer scienceStandardizationSample (material)Data miningTransformation (genetics)Source codeCode (set theory)Artificial intelligenceBiologyGeneGeneticsSet (abstract data type)