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Single cell RNA-seq denoising using a deep count autoencoder

bioRxiv (Cold Spring Harbor Laboratory)(2018)10.1101/300681Source: DataRank Database

Single cell RNA-seq denoising using a deep count autoencoder is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2018). On theSindex it has a DataRank of 3.3. It has been cited 86 times, with 79 citing works in its 1-hop citation network.

N/A

3.3DataRank · unranked

Scored on demand from live citation data

Open Access86 citations · 79 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 (1 grants)OA: greenTopics (46)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 20%Citation Network 80%

Base Score Contribution

0.670

From this paper's citation signal

Citation Network Contribution

2.6

From 62 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 79 citers.

Semisupervised Generative Autoencoder for Single-Cell Data
Journal of Computational Biology201917 citations14 referencesContributes 0.206
Deep generative modeling for single-cell transcriptomics
Nature Methods20182,706 citations52 referencesContributes 0.152
OUTRIDER: A Statistical Method for Detecting Aberrantly Expressed Genes in RNA Sequencing Data
The American Journal of Human Genetics2018235 citations46 referencesContributes 0.119
Transcriptional output, cell types densities and normalization in spatial transcriptomics
bioRxiv (Cold Spring Harbor Laboratory)20188 citations19 referencesContributes 0.116
Droplet scRNA-seq is not zero-inflated
bioRxiv (Cold Spring Harbor Laboratory)201941 citations33 referencesContributes 0.113

Authors (5)

Lukas M. Simon ORCID,Maria Mircea ORCID,Nikola S. Mueller ORCID,Fabian Joachim Theis ORCID,Gökçen Eraslan ORCID

Live enrichment

PubMed:26657633 OpenAlex:https://openalex.org/W2797287022

European Commission

Grant: 753039

Exploring the cellular heterogeneity underlying 'tumor-educated' platelets

greenLicense: cc-by-nc-nd

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2018/04/13/300681.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/300681

repositoryhttps://doi.org/10.1101/300681

repositoryhttps://www.nature.com/articles/s41467-018-07931-2

Fields of Study

Single-cell and spatial transcriptomicsGene Regulatory Network AnalysisCell Image Analysis Techniques0301 basic medicine0206 medical engineering02 engineering and technology03 medical and health sciences

Keywords

Imputation (statistics)Count dataNoise reductionComputer scienceOverdispersionNegative binomial distributionAutoencoderArtificial intelligencePattern recognition (psychology)ScalabilityData miningNoise (video)RNA-SeqMissing dataMachine learningStatisticsMathematicsArtificial neural networkPoisson distributionGeneBiologyGene expressionTranscriptomeBlood CellsModels, StatisticalSequence Analysis, RNAScienceGene Expression ProfilingQComputational BiologyArticlePhenotypeGene Expression RegulationRNA, Small CytoplasmicLeukocytes, MononuclearAnimalsRNASingle-Cell AnalysisCaenorhabditis elegans