Featured Publications
G2S3: A gene graph-based imputation method for single-cell RNA sequencing data
Wu W, Liu Y, Dai Q, Yan X, Wang Z. G2S3: A gene graph-based imputation method for single-cell RNA sequencing data. PLOS Computational Biology 2021, 17: e1009029. PMID: 34003861, PMCID: PMC8189489, DOI: 10.1371/journal.pcbi.1009029.Peer-Reviewed Original ResearchConceptsSingle-cell transcriptomic datasetsTranscriptomic datasetsGene expressionSingle-cell RNA sequencing technologySingle-cell transcriptomic studiesSingle-cell RNA sequencing dataRNA sequencing technologyRNA sequencing dataSingle-cell resolutionGene expression profilesAdjacent genesTranscriptomic studiesSequencing technologiesSequencing dataExpression profilesGene graphDownstream analysisGenesCell trajectoriesDropout eventsCell subtypesExpressionHigh data sparsityCells
2007
Inferring activity changes of transcription factors by binding association with sorted expression profiles
Cheng C, Yan X, Sun F, Li LM. Inferring activity changes of transcription factors by binding association with sorted expression profiles. BMC Bioinformatics 2007, 8: 452. PMID: 18021409, PMCID: PMC2194743, DOI: 10.1186/1471-2105-8-452.Peer-Reviewed Original ResearchConceptsTranscription factorsExpression profilesMicroarray dataTarget gene selectionPost-transcriptional modificationsChIP-chip dataMicroarray expression profilesExpression differentiationLow expression levelsProfile of expressionTarget genesRegulatory mechanismsGene expressionBiological processesMicroarray studiesAffinity dataGene selectionSame machineryExpression levelsGenesActivity changesSignificance cutoffDifferentiationMeaningful hypothesesAffinity scores