2025
Deconer: An Evaluation Toolkit for Reference-based Deconvolution Methods Using Gene Expression Data
Zhang W, Zhang X, Liu Q, Wei L, Qiao X, Gao R, Liu Z, Wang X. Deconer: An Evaluation Toolkit for Reference-based Deconvolution Methods Using Gene Expression Data. Genomics Proteomics & Bioinformatics 2025, 23: qzaf009. PMID: 39963994, PMCID: PMC12221868, DOI: 10.1093/gpbjnl/qzaf009.Peer-Reviewed Original ResearchConceptsCell-type deconvolution algorithmsDemo dataGene expression dataCell-type proportionsCell-type deconvolution methodsSingle-cell sequencing dataGene expression datasetsCell-type deconvolution analysisReference-based methodsSequence dataExpression dataExpression datasetsTranscriptional dataGenesRare componentDeconvolution toolsComprehensive toolkitDeconvolution methodComputational methodsTranscriptionA Selective Review of Network Analysis Methods for Gene Expression Data
Li R, Yi H, Ma S. A Selective Review of Network Analysis Methods for Gene Expression Data. Methods In Molecular Biology 2025, 2880: 293-307. PMID: 39900765, DOI: 10.1007/978-1-0716-4276-4_14.Peer-Reviewed Original ResearchConceptsGene expression dataGene expression networksExpression dataDownstream analysisExpression networksGene expressionBiological processesGenesMolecular mechanismsBiological implicationsHigh-throughput profiling techniquesBiological findingsGlobal viewComplex interactionsProfiling techniquesRegulation
2024
Phenotype driven data augmentation methods for transcriptomic data
Janakarajan N, Graziani M, Martínez M. Phenotype driven data augmentation methods for transcriptomic data. Bioinformatics Advances 2024, 5: vbaf124. PMID: 40487930, PMCID: PMC12141816, DOI: 10.1093/bioadv/vbaf124.Peer-Reviewed Original ResearchAugmentation methodSupervised learning tasksData augmentation methodData augmentation strategyData augmentation methodsApplication of machine learning methodsSynthetic data pointsMachine learning methodsMachine learning modelsClass imbalanceLearning methodsLearning modelsTranscriptome dataLearning tasksAugmentation strategiesGene expression dataParametric estimationDataData pointsGamma-PoissonExpression dataNon-parametricEpigenome-wide association studies identify novel DNA methylation sites associated with PTSD: a meta-analysis of 23 military and civilian cohorts
Katrinli S, Wani A, Maihofer A, Ratanatharathorn A, Daskalakis N, Montalvo-Ortiz J, Núñez-Ríos D, Zannas A, Zhao X, Aiello A, Ashley-Koch A, Avetyan D, Baker D, Beckham J, Boks M, Brick L, Bromet E, Champagne F, Chen C, Dalvie S, Dennis M, Fatumo S, Fortier C, Galea S, Garrett M, Geuze E, Grant G, Hauser M, Hayes J, Hemmings S, Huber B, Jajoo A, Jansen S, Kessler R, Kimbrel N, King A, Kleinman J, Koen N, Koenen K, Kuan P, Liberzon I, Linnstaedt S, Lori A, Luft B, Luykx J, Marx C, McLean S, Mehta D, Milberg W, Miller M, Mufford M, Musanabaganwa C, Mutabaruka J, Mutesa L, Nemeroff C, Nugent N, Orcutt H, Qin X, Rauch S, Ressler K, Risbrough V, Rutembesa E, Rutten B, Seedat S, Stein D, Stein M, Toikumo S, Ursano R, Uwineza A, Verfaellie M, Vermetten E, Vinkers C, Ware E, Wildman D, Wolf E, Young R, Zhao Y, van den Heuvel L, Uddin M, Nievergelt C, Smith A, Logue M. Epigenome-wide association studies identify novel DNA methylation sites associated with PTSD: a meta-analysis of 23 military and civilian cohorts. Genome Medicine 2024, 16: 147. PMID: 39696436, PMCID: PMC11658418, DOI: 10.1186/s13073-024-01417-1.Peer-Reviewed Original ResearchConceptsEpigenome-wide association studiesDNA methylationPsychiatric Genomics ConsortiumPost-traumatic stress disorderAssociation studiesMeta-analysis of epigenome-wide association studiesMethylation levelsGenome-wide expression dataEpigenetic gene regulationBrain regionsPGC-PTSDAnnotated genesBlood cell proportionsCpG lociGene regulationSusceptibility to post-traumatic stress disorderExpression dataAssociated with post-traumatic stress disorderIllumina HumanMethylation450Genomics ConsortiumOccurrence of post-traumatic stress disorderAssociated with biological differencesCpGMultiple brain regionsPostmortem brain samplesHormone Receptor-Positive HER2-Negative/MammaPrint High-2 Breast Cancers Closely Resemble Triple-Negative Breast Cancers.
Rios-Hoyo A, Xiong K, Dai J, Yau C, Marczyk M, Garcia-Milian R, Wolf D, Huppert L, Nanda R, Hirst G, Cobain E, van 't Veer L, Esserman L, Pusztai L. Hormone Receptor-Positive HER2-Negative/MammaPrint High-2 Breast Cancers Closely Resemble Triple-Negative Breast Cancers. Clinical Cancer Research 2024, 31: 403-413. PMID: 39561272, PMCID: PMC11747811, DOI: 10.1158/1078-0432.ccr-24-1553.Peer-Reviewed Original ResearchPathological complete responseEvent-free survivalBreast cancerHER2 negative breast cancerHormone receptor-positive/HER2-negativePathologic complete response ratePrognostic risk categoriesTN breast cancerNegative breast cancerGene set analysisExpression of cell cycleGene expression dataLow-risk subgroupsHigh-risk groupMammaPrint assayNeoadjuvant trialsComplete responseER statusResidual cancerPrognostic groupsClinical featuresI-SPY2Prognostic assaysExpression dataTreatment strategiesPrioritizing disease-related rare variants by integrating gene expression data
Guo H, Urban A, Wong H. Prioritizing disease-related rare variants by integrating gene expression data. PLOS Genetics 2024, 20: e1011412. PMID: 39348415, PMCID: PMC11466430, DOI: 10.1371/journal.pgen.1011412.Peer-Reviewed Original ResearchConceptsGene expression dataRare variantsExpression dataRare variant association methodsExcess of rare variantsImpact of rare variantsContext of human diseaseHuman genetic variationGenetic variationGene expressionComplex diseasesHuman diseasesGenesMolecular mechanismsFunctional consequencesRare variant typesAlzheimer's diseaseVariant typeVariantsAssociation methodStatistical frameworkSimulation studySample sizeOmicsAlzheimerExpression and prognostic value of cell‐cycle‐associated genes in lung squamous cell carcinoma
Xu X, Jin K, Xu X, Yang Y, Zhou B. Expression and prognostic value of cell‐cycle‐associated genes in lung squamous cell carcinoma. The Journal Of Gene Medicine 2024, 26: e3735. PMID: 39171952, DOI: 10.1002/jgm.3735.Peer-Reviewed Original ResearchConceptsCell cycle-associated genesLung squamous carcinomaCell cycleMRNA expression dataGene expression profilesAssociated with positive prognosisCause of cancer-related deathExpression dataCancer Genome AtlasExpressed genesSquamous cell carcinomaLung squamous cell carcinomaTargeted therapy trialsGroup of patientsCancer-related deathsExpression of CDK4GenesExpression trendsExpression profilesMolecular studiesGenome AtlasSquamous carcinomaCell carcinomaPathological stagePrognostic valueSpatially Informed Gene Signatures for Response to Immunotherapy in Melanoma.
Aung T, Warrell J, Martinez-Morilla S, Gavrielatou N, Vathiotis I, Yaghoobi V, Kluger H, Gerstein M, Rimm D. Spatially Informed Gene Signatures for Response to Immunotherapy in Melanoma. Clinical Cancer Research 2024, 30: 3520-3532. PMID: 38837895, PMCID: PMC11326985, DOI: 10.1158/1078-0432.ccr-23-3932.Peer-Reviewed Original ResearchGene signatureResistance to immunotherapyResponse to immunotherapyPrediction of treatment outcomeResistant to treatmentAccurate prediction of treatment outcomePredictive of responseImmunotherapy outcomesMelanoma patientsMelanoma specimensValidation cohortPatient stratificationDiscovery cohortTreatment outcomesImmunotherapyMelanomaTumorPatientsCohortS100BOutcomesGene expression dataGenesCD68+macrophagesExpression dataCorrelation 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 trialGenesStochastic modeling of a gene regulatory network driving B cell development in germinal centers
Koshkin A, Herbach U, Martínez M, Gandrillon O, Crauste F. Stochastic modeling of a gene regulatory network driving B cell development in germinal centers. PLOS ONE 2024, 19: e0301022. PMID: 38547073, PMCID: PMC10977792, DOI: 10.1371/journal.pone.0301022.Peer-Reviewed Original ResearchConceptsGene regulatory network structureGene regulatory networksGene expression dataExpression dataB cell differentiationSingle-cellAssociated with cell developmentGC B cell differentiationStages of B-cell differentiationB cell developmentSelection of B cellsGene regulationRegulatory networksTranscriptome dataSystems biologyHigh-affinity antibodiesRegulatory mechanismsCell developmentGenesAdaptive immune systemMRNA distributionPlasmablast stageGerminal centersDifferentiationImmune systemHomotopic functional connectivity disruptions in schizophrenia and their associated gene expression
Cai M, Ji Y, Zhao Q, Xue H, Sun Z, Wang H, Zhang Y, Chen Y, Zhao Y, Zhang Y, Lei M, Wang C, Zhuo C, Liu N, Liu H, Liu F. Homotopic functional connectivity disruptions in schizophrenia and their associated gene expression. NeuroImage 2024, 289: 120551. PMID: 38382862, DOI: 10.1016/j.neuroimage.2024.120551.Peer-Reviewed Original ResearchConceptsVoxel-mirrored homotopic connectivityAbnormal VMHCAllen Human Brain AtlasFunctional connectivity disruptionsHuman Brain AtlasExtensive cortical regionsBrain gene expression profilesIllness durationHomotopic connectivitySchizophreniaDecoding analysesCortical regionsConnectivity disruptionsDevelopmental time windowCell communicationChanges compared to controlsExpression of identified genesGene expression dataBrain atlasesRegulation of cell communicationMeta-regression analysisGene expression profilesNervous system developmentExpression dataNeurosynthEstimating Cell-Type-Specific Gene Co-Expression Networks from Bulk Gene Expression Data with an Application to Alzheimer’s Disease
Su C, Zhang J, Zhao H. Estimating Cell-Type-Specific Gene Co-Expression Networks from Bulk Gene Expression Data with an Application to Alzheimer’s Disease. Journal Of The American Statistical Association 2024, 119: 811-824. PMID: 39280354, PMCID: PMC11394578, DOI: 10.1080/01621459.2023.2297467.Peer-Reviewed Original Research
2023
ML Based Phenotype Analysis Using Differential Gene Expression Data in Schizophrenia
D A, KJ S, Maniappan V, B S, S D, R P, Raja K, Oviya I. ML Based Phenotype Analysis Using Differential Gene Expression Data in Schizophrenia. 2023, 00: 1-7. DOI: 10.1109/icstcee60504.2023.10585167.Peer-Reviewed Original ResearchGene expression dataGene expression patternsGene expression profilesGene orientationSNP informationExpression dataGene expressionPhenotypic analysisExpression patternsGenesExpression profilesCell phenotypeBiological mechanismsBiological mechanisms of schizophreniaMechanisms of schizophreniaEqtnSNPsTherapeutic approachesPhenotypeSystematic characterization of photoperiodic gene expression patterns reveals diverse seasonal transcriptional systems in Arabidopsis
Leung C, Tarté D, Oliver L, Wang Q, Gendron J. Systematic characterization of photoperiodic gene expression patterns reveals diverse seasonal transcriptional systems in Arabidopsis. PLOS Biology 2023, 21: e3002283. PMID: 37699055, PMCID: PMC10497145, DOI: 10.1371/journal.pbio.3002283.Peer-Reviewed Original ResearchConceptsExpression patternsCis-element analysisPhenylpropanoid biosynthesis pathwaySeasonal expression patternsImportant cellular pathwaysGene expression patternsThousands of genesFunctional enrichment analysisGene expression dataAlign growthArabidopsis plantsPhotoperiodic floweringTranscriptional networksPhotoperiodic genesBiosynthesis pathwayCellular processesPhenylpropanoid pathwayTranscriptomic experimentsTranscriptomic dataTranscriptional systemCellular pathwaysEnrichment analysisGene expressionGene clusteringExpression dataAssessing transcriptomic reidentification risks using discriminative sequence models
Sadhuka S, Fridman D, Berger B, Cho H. Assessing transcriptomic reidentification risks using discriminative sequence models. Genome Research 2023, 33: 1101-1112. PMID: 37541758, PMCID: PMC10538488, DOI: 10.1101/gr.277699.123.Peer-Reviewed Original ResearchConceptsExpression quantitative trait lociGene expression dataExpression dataQuantitative trait lociOmics data setsGene expression profilesTrait lociGenomic regionsGenetic variationGene expressionExpression profilesMolecular insightsLinkage disequilibriumFunctional impactGenotypesTranscriptomicsLociSame individualDisequilibriumSequenceExpressionPrevious studiesFull extentData setsIntegrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings
Hicks E, Seah C, Cote A, Marchese S, Brennand K, Nestler E, Girgenti M, Huckins L. Integrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings. Translational Psychiatry 2023, 13: 129. PMID: 37076454, PMCID: PMC10115809, DOI: 10.1038/s41398-023-02412-7.Peer-Reviewed Original ResearchConceptsBioinformatics approachTranscriptomic dataBrain transcriptomeGenome-wide analysisDynamic transcriptional landscapeBrain gene expression dataGene expression dataTranscriptional landscapeTranscriptomic studiesIntegrating GeneticExpression dataPhenotypic signaturesGenomic driversTranscriptomeMajor depressive disorderValuable resourceRecent findingsEnvironmental influencesTranscriptomicsDepressive disorderGeneticsMultiple approachesPathophysiology of depressionSignaturesDysregulationChapter 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
Integrative analyses for the identification of idiopathic pulmonary fibrosis-associated genes and shared loci with other diseases
Chen M, Zhang Y, Adams T, Ji D, Jiang W, Wain LV, Cho M, Kaminski N, Zhao H. Integrative analyses for the identification of idiopathic pulmonary fibrosis-associated genes and shared loci with other diseases. Thorax 2022, 78: 792-798. PMID: 36216496, PMCID: PMC10083187, DOI: 10.1136/thorax-2021-217703.Peer-Reviewed Original ResearchConceptsTranscriptome-wide association analysisLocal genetic correlationsSingle-cell expression dataCandidate genesTranscription factorsIntegrative analysisGenomic regionsGenetic correlationsExpression dataTF target genesComplex genetic architectureTF binding sitesWide association studyPower of GWASSpecific DEGsGenetic architectureNew genesNovel genesCausal genesTarget genesGenetic basisEnrichment analysisAssociation studiesRegulatory roleAssociation analysisCINS: Cell Interaction Network inference from Single cell expression data
Yuan Y, Cosme C, Adams TS, Schupp J, Sakamoto K, Xylourgidis N, Ruffalo M, Li J, Kaminski N, Bar-Joseph Z. CINS: Cell Interaction Network inference from Single cell expression data. PLOS Computational Biology 2022, 18: e1010468. PMID: 36095011, PMCID: PMC9499239, DOI: 10.1371/journal.pcbi.1010468.Peer-Reviewed Original ResearchConceptsCell type interactionsSingle-cell expression dataSingle-cell RNA-seq dataRNA-seq dataScRNA-seq experimentsCell-cell interactionsExpression dataCell typesMouse datasetsNetwork inferenceCell interactionsInteraction predictionNetwork analysisInference pipelineGenesCINSProteinInteractionBayesian network analysisOMiCC: An expanded and enhanced platform for meta-analysis of public gene expression data
Liu C, Guo Y, Vrindten K, Lau W, Sparks R, Tsang J. OMiCC: An expanded and enhanced platform for meta-analysis of public gene expression data. STAR Protocols 2022, 3: 101474. PMID: 35880119, PMCID: PMC9307621, DOI: 10.1016/j.xpro.2022.101474.Peer-Reviewed Original ResearchMeSH KeywordsGene Expression
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