2024
Correlation of hormone receptor positive HER2-negative/MammaPrint high-2 breast cancer with triple negative breast cancer: Results from gene expression data from the ISPY2 trial.
Rios-Hoyo A, Xiong K, Marczyk M, García-Millán R, Wolf D, Huppert L, Nanda R, Yau C, Hirst G, van 't Veer L, Esserman L, Pusztai L. Correlation of hormone receptor positive HER2-negative/MammaPrint high-2 breast cancer with triple negative breast cancer: Results from gene expression data from the ISPY2 trial. Journal Of Clinical Oncology 2024, 42: 573-573. DOI: 10.1200/jco.2024.42.16_suppl.573.Peer-Reviewed Original ResearchGene expression dataGene expression analysisExpression dataExpressed genesExpression analysisTriple-negativeDistance analysisPathway analysisDifferential gene expression analysisCell cycle pathwayGene set enrichment analysisBreast cancerIngenuity Pathway AnalysisRate of pathological complete responseHigh-risk stage IIGlucocorticoid receptor signalingTriple negative breast cancerCycle pathwayPathological complete responseDNA repairEnrichment analysisOptimal treatment strategyNegative breast cancerI-SPY2 trialGenes
2023
Chapter 3 Single-cell transcriptomics
Marczyk M, Kujawa T, Papiez A, Polanska J. Chapter 3 Single-cell transcriptomics. 2023, 67-84. DOI: 10.1016/b978-0-323-91810-7.00015-7.ChaptersSingle-cell transcriptomicsSingle-cell technologiesCell trajectory inferenceGene expression dataSimilar cell typesTranscriptional signalsCancer cell line dataRNAseq dataGene expressionCellular heterogeneitySequencing platformsBreast cancer cell line dataExpression dataCell line dataGroups of cellsTrajectory inferenceCell typesIndividual cellsMolecular characteristicsProper processingCellsTranscriptomicsGenesExpressionCertain steps
2022
Investigating Sources of Zeros in 10× Single-Cell RNAseq Data
Slowik H, Zyla J, Marczyk M. Investigating Sources of Zeros in 10× Single-Cell RNAseq Data. Lecture Notes In Computer Science 2022, 13347: 71-80. DOI: 10.1007/978-3-031-07802-6_6.Peer-Reviewed Original ResearchSingle-cell levelSingle-cell RNA sequencingSingle-cell RNAseq dataNumber of transcriptsMulti-omics dataGene expression estimatesRibosomal genesRNA sequencingExpression profilingEnrichment analysisRNAseq dataBiological pathwaysSequencing platformsExpression dataGenesExpression estimatesIndividual cellsBreast cancer cell linesCancer cell linesCell linesSingle experimentLow mappabilityTranscriptsSequencingProfiling
2021
Importance of SNP Dependency Correction and Association Integration for Gene Set Analysis in Genome-Wide Association Studies
Marczyk M, Macioszek A, Tobiasz J, Polanska J, Zyla J. Importance of SNP Dependency Correction and Association Integration for Gene Set Analysis in Genome-Wide Association Studies. Frontiers In Genetics 2021, 12: 767358. PMID: 34956320, PMCID: PMC8696167, DOI: 10.3389/fgene.2021.767358.Peer-Reviewed Original ResearchGenome-wide association studiesOver-Representation AnalysisSingle nucleotide polymorphismsMeta-Analysis Gene-set EnrichmentSame geneTypical genome-wide association studyEnrichment analysisAssociation studiesGene-set analysisLD correctionGene set enrichmentGene expression dataGSEA-SNPCancer Genome Atlas (TCGA) databaseRelevant single nucleotide polymorphismsTranscriptomic levelGene levelExpression dataVariant associationsNonrandom associationGenesAssociation of SNPsMore lociAtlas databaseBreast cancer samples