HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics
Yuan X, Ma Y, Gao R, Cui S, Wang Y, Fa B, Ma S, Wei T, Ma S, Yu Z. HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics. Nature Communications 2024, 15: 5700. PMID: 38972896, PMCID: PMC11228050, DOI: 10.1038/s41467-024-49846-1.Peer-Reviewed Original ResearchConceptsSpatially variable genesVariable genesSpatial expression patternsSpatial transcriptomics technologiesSpatial transcriptomics researchTranscriptome researchTranscriptomic technologiesBiological functionsExpression patternsSpatial transcriptomicsGenesState-of-the-art methodsColorectal cancer dataHarnessing TME depicted by histological images to improve cancer prognosis through a deep learning system
Gao R, Yuan X, Ma Y, Wei T, Johnston L, Shao Y, Lv W, Zhu T, Zhang Y, Zheng J, Chen G, Sun J, Wang Y, Yu Z. Harnessing TME depicted by histological images to improve cancer prognosis through a deep learning system. Cell Reports Medicine 2024, 5: 101536. PMID: 38697103, PMCID: PMC11149411, DOI: 10.1016/j.xcrm.2024.101536.Peer-Reviewed Original ResearchColorectal cancer cohortTumor microenvironmentCancer prognosisCancer cohortCancer Genome Atlas-Breast CancerAssociated with cancer prognosisImprove cancer prognosisPrognosis prediction modelBreast cancerConcordance indexClinical availabilitySurvival modelsSpatial transcriptomicsST expressionCancer typesPrognosisCancerSurvivalHistological images