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Randomized Spatial PCA (RASP): a computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data

bioRxiv (Cold Spring Harbor Laboratory)(2024)10.1101/2024.12.20.629785Source: DataRank Database

Randomized Spatial PCA (RASP): a computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2024). On theSindex it has a DataRank of 0.

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Enrichment:Funding (6 grants)OA: greenTopics (27)IDs (PubMed, OpenAlex)

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Authors (3)

Brittany K Goods,H. Robert Frost ORCID,Ian K. Gingerich ORCID

Live enrichment

PubMed:39763720 OpenAlex:https://openalex.org/W4405742835

National Institutes of Health

Grant: 3P30CA023108-41S8

Cancer Center Support Grant

National Institutes of Health

Grant: 5R35GM146586-04

Gene set analysis of single cell genomics

National Institutes of Health

Grant: 3P20GM130454-05S2

NOSI cloud computing Supplement

NIGMS NIH HHS

Grant: P20 GM130454

NCI NIH HHS

Grant: P30 CA023108

NIGMS NIH HHS

Grant: R35 GM146586

greenLicense: cc-by-nc-nd

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2024/12/24/2024.12.20.629785.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2024.12.20.629785

repositoryhttps://doi.org/10.1101/2024.12.20.629785

repositoryhttps://pubmed.ncbi.nlm.nih.gov/39763720

Fields of Study

Single-cell and spatial transcriptomicsGene expression and cancer classificationMolecular Biology Techniques and Applications0206 medical engineering02 engineering and technology

Keywords

SmoothingDimensionality reductionComputer scienceSpatial analysisPrincipal component analysisData miningCurse of dimensionalityComputational biologyArtificial intelligenceBiologyMathematicsComputer visionStatisticsMiceGene Expression ProfilingAnimalsHumansCluster AnalysisTranscriptomeArticleAlgorithmsResearch Article