2024
Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data
Dai R, Chu T, Zhang M, Wang X, Jourdon A, Wu F, Mariani J, Vaccarino F, Lee D, Fullard J, Hoffman G, Roussos P, Wang Y, Wang X, Pinto D, Wang S, Zhang C, consortium P, Chen C, Liu C. Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data. Science Advances 2024, 10: eadh2588. PMID: 38781336, PMCID: PMC11114236, DOI: 10.1126/sciadv.adh2588.Peer-Reviewed Original ResearchConceptsHuman brain transcriptome dataBrain transcriptomic dataRNA-seqTranscriptome dataCell-type gene expressionGene expressionCell-type proportionsSingle-cell dataMultiple brain disordersBrain cell typesCell deconvolution methodsPostmortem brainsRNA sequencingBrain disordersBrain developmentSchizophreniaEQTLAlzheimer's diseaseCell typesOrganoid samplesBrainBiological applications
2020
SCELLECTOR: ranking amplification bias in single cells using shallow sequencing
Sarangi V, Jourdon A, Bae T, Panda A, Vaccarino F, Abyzov A. SCELLECTOR: ranking amplification bias in single cells using shallow sequencing. BMC Bioinformatics 2020, 21: 521. PMID: 33183232, PMCID: PMC7663899, DOI: 10.1186/s12859-020-03858-y.Peer-Reviewed Original ResearchConceptsMultiple displacement amplificationShallow sequencingSingle-cell platformsSingle-cell sequencingCoverage sequencing dataSingle cellsHuman neuronal cellsMosaic mutationsAmount of DNAAmplification qualityCell sequencingCoverage sequencingHigh-coverage dataSequencing dataHaplotype informationPhi29 polymeraseDNA damageIndividual cellsNeuronal cellsSequencingAmplification biasAllelic imbalancePresence of sitesMutationsFragment length