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Deep learning applications in single-cell genomics and transcriptomics data analysis

BIOMEDICINE & PHARMACOTHERAPY (2023)

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Summary: Clustering methods in single-cell RNA-seq data have been widely used for investigating tumor heterogeneity. Deep clustering methods, which have the strength to capture high-dimensional information, are gaining increasing attention. However, existing methods cannot effectively utilize attribute and structure information simultaneously. In this study, we propose a novel single-cell deep fusion clustering model that combines attributed and structure-attention features. Experimental results demonstrate the effectiveness of our approach in fusing attributes, structure, and attention information in single-cell RNA-seq data. This work has important implications for studying cell subpopulations and tumor microenvironment.

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SCIBER: a simple method for removing batch effects from single-cell RNA-sequencing data

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Article Biochemical Research Methods

scDCCA: deep contrastive clustering for single-cell RNA-seq data based on auto-encoder network

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Summary: The advances in single-cell ribonucleic acid sequencing (scRNA-seq) have enabled the exploration of cellular heterogeneity and human diseases at the cellular level. However, the high dimensionality, noise, and sparsity of scRNA-seq data pose a challenge for cell clustering. In this study, we propose a new deep contrastive clustering algorithm scDCCA, which incorporates a denoising autoencoder and a dual contrastive learning module to extract valuable features and improve cell clustering performance.

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Article Biochemical Research Methods

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Wolfgang Kopp et al.

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Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data

Chunman Zuo et al.

Summary: The study introduces a single-cell multimodal variational autoencoder model that effectively integrates transcriptomic and chromatin accessibility information, accurately representing the multilayer profiles of cells and demonstrating good capabilities in dissecting cellular heterogeneity, denoising and imputing data, as well as constructing associations between multilayer omics data.

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Joseph T. Glessner et al.

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Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram

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Joint probabilistic modeling of single-cell multi-omic data with totalVI

Adam Gayoso et al.

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Jie Xia et al.

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