ArticleInternational journal of molecular sciences2020
Dimension Reduction and Clustering Models for Single-Cell RNA Sequencing Data: A Comparative Study.
Article in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- scTIDE: Deciphering Critical Transitions Through Cell-Perturbed Manifold Graphs and Optimal Transport Conditional Flow Matching.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Advanced physiological maturation of human iPSC-derived cardiomyocytes using an algorithm-directed optimization of defined media components.Nature communications · 2026Article
- DiSTect: a Bayesian model for disease-associated gene discovery and prediction in spatial transcriptomics.Bioinformatics (Oxford, England) · 2025Article
- Enabling scalable single-cell transcriptomic analysis through distributed computing with Apache spark.Scientific reports · 2025Article
- Evaluating discrepancies in dimensionality reduction for time-series single-cell RNA-sequencing data.Briefings in bioinformatics · 2025Article
- Infusing structural assumptions into dimensionality reduction for single-cell RNA sequencing data to identify small gene sets.Communications biology · 2025Article
- Interpreting single-cell and spatial omics data using deep neural network training dynamics.Nature computational science · 2024Article
- An introduction to representation learning for single-cell data analysis.Cell reports methods · 2023Review
- Article
- ANPELA: Significantly Enhanced Quantification Tool for Cytometry-Based Single-Cell Proteomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Epi-Impute: Single-Cell RNA-seq Imputation via Integration with Single-Cell ATAC-seq.International journal of molecular sciences · 2023Article
- Deciphering endothelial heterogeneity in health and disease at single-cell resolution: progress and perspectives.Cardiovascular research · 2023Article
- Application of single-cell RNA sequencing on human testicular samples: a comprehensive review.International journal of biological sciences · 2023Review
- scSemiAE: a deep model with semi-supervised learning for single-cell transcriptomics.BMC bioinformatics · 2022Article
- Discovering cell types using manifold learning and enhanced visualization of single-cell RNA-Seq data.Scientific reports · 2022Article
- Single-Cell Analysis of the Transcriptome and Epigenome.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Dimensionality Reduction and Louvain Agglomerative Hierarchical Clustering for Cluster-Specified Frequent Biomarker Discovery in Single-Cell Sequencing Data.Frontiers in genetics · 2022Article
- Single-cell transcriptome analysis identifies skin-specific T-cell responses in systemic sclerosis.Annals of the rheumatic diseases · 2021Article
- Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations on single cell data.PLoS computational biology · 2021Article
- Current and Prospective Methods for Assessing Anti-Tumor Immunity in Colorectal Cancer.International journal of molecular sciences · 2021Review
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8 authors.
Funding
Abstract
With recent advances in single-cell RNA sequencing, enormous transcriptome datasets have been generated. These datasets have furthered our understanding of cellular heterogeneity and its underlying mechanisms in homogeneous populations. Single-cell RNA sequencing (scRNA-seq) data clustering can group cells belonging to the same cell type based on patterns embedded in gene expression. However, scRNA-seq data are high-dimensional, noisy, and sparse, owing to the limitation of existing scRNA-seq technologies. Traditional clustering methods are not effective and efficient for high-dimensional and sparse matrix computations. Therefore, several dimension reduction methods have been introduced. To validate a reliable and standard research routine, we conducted a comprehensive review and evaluation of four classical dimension reduction methods and five clustering models. Four experiments were progressively performed on two large scRNA-seq datasets using 20 models. Results showed that the feature selection method contributed positively to high-dimensional and sparse scRNA-seq data. Moreover, feature-extraction methods were able to promote clustering performance, although this was not eternally immutable. Independent component analysis (ICA) performed well in those small compressed feature spaces, whereas principal component analysis was steadier than all the other feature-extraction methods. In addition, ICA was not ideal for fuzzy C-means clustering in scRNA-seq data analysis. K-means clustering was combined with feature-extraction methods to achieve good results.
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