Zongming Ma
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Research
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2026
Integration of imaging-based and sequencing-based spatial omics mapping on the same tissue section via DBiTplus
Enninful A, Zhang Z, Klymyshyn D, Ingalls M, Yang M, Zong H, Bai Z, Farzad N, Su G, Baysoy A, Nam J, Lu Y, Bao S, Deng S, Zhang N, Braubach O, Xu M, Ma Z, Fan R. Integration of imaging-based and sequencing-based spatial omics mapping on the same tissue section via DBiTplus. Nature Methods 2026, 1-13. PMID: 41540123, DOI: 10.1038/s41592-025-02948-0.Peer-Reviewed Original ResearchSequencing-based spatial transcriptomicsProtein imagingTranscriptome scaleDeterministic barcodingSingle-cell atlasCell-by-cell mannerComputational pipelineOmics approachesSpatial barcodingBiological pathwaysClinical specimensCellular heterogeneityTranscriptomeSpatial transcriptomicsCell typesSpatial omicsSequence plusTissue sectionsFrozen mouse embryosBarcodingMouse embryosHuman lymph nodesExploration of biological mechanismsLymphoma tissueLymph nodes
2025
CellLENS enables cross-domain information fusion for enhanced cell population delineation in single-cell spatial omics data
Zhu B, Gao S, Chen S, Wang Y, Yeung J, Bai Y, Huang A, Yeo Y, Liao G, Mao S, Jiang Z, Rodig S, Wong K, Shalek A, Nolan G, Jiang S, Ma Z. CellLENS enables cross-domain information fusion for enhanced cell population delineation in single-cell spatial omics data. Nature Immunology 2025, 26: 963-974. PMID: 40404817, PMCID: PMC12317664, DOI: 10.1038/s41590-025-02163-1.Peer-Reviewed Original ResearchConceptsDe novo discoveryTissue-specific functionsCell populationsCell molecular profilesCell's local environmentTranscriptomic datasetsOmics dataPopulation delineationSpatial omics dataImmune cell heterogeneityOmics technologiesCell heterogeneitySpatial omicsSpatial omics technologiesComputational methodsImmune cell populationsTissue typesOmics domainsTissue localization
2024
Recovery of biological signals lost in single-cell batch integration with CellANOVA
Zhang Z, Mathew D, Lim T, Mason K, Martinez C, Huang S, Wherry E, Susztak K, Minn A, Ma Z, Zhang N. Recovery of biological signals lost in single-cell batch integration with CellANOVA. Nature Biotechnology 2024, 43: 1861-1877. PMID: 39592777, DOI: 10.1038/s41587-024-02463-1.Peer-Reviewed Original Research
2023
Integration of spatial and single-cell data across modalities with weakly linked features
Chen S, Zhu B, Huang S, Hickey J, Lin K, Snyder M, Greenleaf W, Nolan G, Zhang N, Ma Z. Integration of spatial and single-cell data across modalities with weakly linked features. Nature Biotechnology 2023, 42: 1096-1106. PMID: 37679544, PMCID: PMC11638971, DOI: 10.1038/s41587-023-01935-0.Peer-Reviewed Original ResearchSample canonical correlation coefficients of high-dimensional random vectors with finite rank correlations
Ma Z, Yang F. Sample canonical correlation coefficients of high-dimensional random vectors with finite rank correlations. Bernoulli 2023, 29 DOI: 10.3150/22-bej1525.Peer-Reviewed Original ResearchOrganization of the human intestine at single-cell resolution
Hickey J, Becker W, Nevins S, Horning A, Perez A, Zhu C, Zhu B, Wei B, Chiu R, Chen D, Cotter D, Esplin E, Weimer A, Caraccio C, Venkataraaman V, Schürch C, Black S, Brbić M, Cao K, Chen S, Zhang W, Monte E, Zhang N, Ma Z, Leskovec J, Zhang Z, Lin S, Longacre T, Plevritis S, Lin Y, Nolan G, Greenleaf W, Snyder M. Organization of the human intestine at single-cell resolution. Nature 2023, 619: 572-584. PMID: 37468586, PMCID: PMC10356619, DOI: 10.1038/s41586-023-05915-x.Peer-Reviewed Original ResearchConceptsCell typesGene regulatory differencesOpen-chromatin assayCell transcriptional programSingle-nucleus RNASingle-cell resolutionChromatin assaysIndividual cell typesDisease heritabilityCell compositionTranscriptional programsSingle-nucleusDifferentiation cascadeHuman intestineSymbiotic relationshipRegulatory differencesHuman biologySingle cellsComplex organismsImmunological nicheExtractable nutrientsCellsIntestinal sitesEpithelial subtypesImmune surveillanceCommunity Detection With Contextual Multilayer Networks
Ma Z, Nandy S. Community Detection With Contextual Multilayer Networks. IEEE Transactions On Information Theory 2023, 69: 3203-3239. DOI: 10.1109/tit.2023.3238352.Peer-Reviewed Original ResearchMinimum mean square errorCommunity detectionApproximate message passing algorithmMessage passing algorithmThreshold of phase transitionPhase transition thresholdMean square errorStudy community detectionPassing algorithmSignal-to-noise ratioSpectral initializationPhase transitionMultilayer networksMultiple data sourcesRandom guessingTransition thresholdTheoretical predictionsDeshpande et al.Square errorAsymptotic regimeAlgorithmData sourcesSharp thresholdsNetworkRegimeRobust single-cell matching and multimodal analysis using shared and distinct features
Zhu B, Chen S, Bai Y, Chen H, Liao G, Mukherjee N, Vazquez G, McIlwain D, Tzankov A, Lee I, Matter M, Goltsev Y, Ma Z, Nolan G, Jiang S. Robust single-cell matching and multimodal analysis using shared and distinct features. Nature Methods 2023, 20: 304-315. PMID: 36624212, PMCID: PMC9911356, DOI: 10.1038/s41592-022-01709-7.Peer-Reviewed Original Research
2022
Global and individualized community detection in inhomogeneous multilayer networks
Chen S, Liu S, Ma Z. Global and individualized community detection in inhomogeneous multilayer networks. The Annals Of Statistics 2022, 50 DOI: 10.1214/22-aos2202.Peer-Reviewed Original ResearchOptimal signal detection in some spiked random matrix models: Likelihood ratio tests and linear spectral statistics
Banerjee D, Ma Z. Optimal signal detection in some spiked random matrix models: Likelihood ratio tests and linear spectral statistics. The Annals Of Statistics 2022, 50 DOI: 10.1214/21-aos2150.Peer-Reviewed Original Research
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