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kimma: flexible linear mixed effects modeling with kinship covariance for RNA-seq data

bioRxiv (Cold Spring Harbor Laboratory)(2022)10.1101/2022.10.10.508946Source: DataRank Database

kimma: flexible linear mixed effects modeling with kinship covariance for RNA-seq data is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2022). On theSindex it has a DataRank of 0. It has been cited 4 times.

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (3 grants)OA: greenTopics (25)IDs (PubMed, OpenAlex)

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Authors (13)

Kiana Mitchell,Sashank Batchu,R. Max Segnitz ORCID,Basilin Benson ORCID,Tomasz Janczyk

Live enrichment

PubMed:26207759 OpenAlex:https://openalex.org/W4304183674

National Institutes of Health

Grant: 5U19AI162583-02

Host Pathogen Variation & TB Pathogenesis

National Institutes of Health

Grant: 5U01AI115642-03

Natural Resistance to Mycobacterium Tuberculosis Infection

National Institutes of Health

Grant: 5R01AI124348-03

Resistance to MTB infection in HIV infected individuals in Uganda and S. Africa

greenLicense: CC BY

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2022/10/11/2022.10.10.508946.1.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2022.10.10.508946

repositoryhttps://doi.org/10.1101/2022.10.10.508946

https://doi.org/10.1093/bioinformatics/btad279

Fields of Study

Gene expression and cancer classificationBioinformatics and Genomic NetworksSingle-cell and spatial transcriptomics0301 basic medicine0303 health sciences03 medical and health sciences

Keywords

CovarianceKinshipCovariateEstimation of covariance matricesSensitivity (control systems)MathematicsMixed modelLinear modelComputer scienceStatisticsAlgorithmEngineeringOriginal PaperSequence Analysis, RNAGene Expression ProfilingLinear ModelsHumansRNA-SeqSoftware