2023
Evaluation of zero counts to better understand the discrepancies between bulk and single-cell RNA-Seq platforms
Zyla J, Papiez A, Zhao J, Qu R, Li X, Kluger Y, Polanska J, Hatzis C, Pusztai L, Marczyk M. Evaluation of zero counts to better understand the discrepancies between bulk and single-cell RNA-Seq platforms. Computational And Structural Biotechnology Journal 2023, 21: 4663-4674. PMID: 37841335, PMCID: PMC10568495, DOI: 10.1016/j.csbj.2023.09.035.Peer-Reviewed Original ResearchSingle-cell RNA-seq platformsSingle-cell RNA sequencingBulk RNA-seq dataRNA-seq platformsNumber of transcriptsLow-expression genesRNA-seq dataSingle-cell dataExpression levelsLow sequencing depthDiscordant genesRNA sequencingSequencing technologiesExpression shiftsPathway levelBiological pathwaysGene levelSequencing depthTranscriptomic platformsGenesIndividual cellsSingle cellsRNA integrityPathwayCells
2015
A genome-wide approach to link genotype to clinical outcome by utilizing next generation sequencing and gene chip data of 6,697 breast cancer patients
Pongor L, Kormos M, Hatzis C, Pusztai L, Szabó A, Győrffy B. A genome-wide approach to link genotype to clinical outcome by utilizing next generation sequencing and gene chip data of 6,697 breast cancer patients. Genome Medicine 2015, 7: 104. PMID: 26474971, PMCID: PMC4609150, DOI: 10.1186/s13073-015-0228-1.Peer-Reviewed Original ResearchConceptsRNA-seq dataNext-generation sequencingBreast cancer patientsTranscriptomic fingerprintGenome-wide approachesGeneration sequencingClinical outcomesCancer patientsHuman gene mutationsTumor suppressor geneGene chip dataSuch genesRNA-seqGene mutationsLarge breast cancer cohortGene expressionChip dataSuppressor geneBreast cancer cohortGenesMicroarray dataMutationsSomatic mutationsClinical characteristicsCox regression