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scRNA-seq analysis of neuroblastoma
To characterize NB tumors, we performed scRNA-seq analysis using NB primary tumor samples, and circulating T cells sorted from peripheral blood of healthy controls and NB patients.openaire +1 more source
Recursive Clustering of Cellular Diversity in scRNA-Seq Data
Journal of Computational BiologyIn scRNA-seq analysis, cell clusters are typically defined by a single round of feature extraction and clustering. This approach may miss phenotypic differences in cell types that are characterized by genes not sufficiently represented in the feature set derived using all cells, such as rare cell types. This work explores an alternative approach, where
Michael Squires, Peng Qiu
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Deep enhanced constraint clustering based on contrastive learning for scRNA-seq data
Briefings in Bioinformatics, 2023Yanglan Gan, Guangwei Xu, Wenjing Guo
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scGMAAE: Gaussian mixture adversarial autoencoders for diversification analysis of scRNA-seq data
Briefings in Bioinformatics, 2023Jianping Zhao +2 more
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scGCC: Graph Contrastive Clustering With Neighborhood Augmentations for scRNA-Seq Data Analysis
IEEE Journal of Biomedical and Health Informatics, 2023Shengwen Tian +2 more
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Heterogeneity of immune cells in human atherosclerosis revealed by scRNA-Seq
Cardiovascular Research, 2021Jenifer Vallejo +2 more
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The single-cell RNA sequencing data of lung tissues of male C57BL/6J mice raised in normoxic condition or in hypoxic condition for 3, 7, 14, 21 or 28 days.
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