ArticleBioinformatics (Oxford, England)2024
CTEC: a cross-tabulation ensemble clustering approach for single-cell RNA sequencing data analysis.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- scMEDAL: interpretable single-cell transcriptomics analysis with batch effect visualization via deep mixed-effects autoencoder.Nature communications · 2026Article
- gLeiden: accelerated community detection algorithms using directed and undirected graphs on GPUs.Bioinformatics advances · 2026Article
- A clustering method for single-cell RNA sequencing data based on denoising and masking learning.Frontiers in bioinformatics · 2026Article
- RGCN-BA: relational graph convolutional network with batch awareness for single-cell RNA sequencing clustering.Briefings in bioinformatics · 2025Article
- scDRMAE: integrating masked autoencoder with residual attention networks to leverage omics feature dependencies for accurate cell clustering.Bioinformatics (Oxford, England) · 2024Article
- Evolutionary Mechanism Based Conserved Gene Expression Biclustering Module Analysis for Breast Cancer Genomics.Biomedicines · 2024Article
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Abstract
motivationCell-type clustering is a crucial first step for single-cell RNA-seq data analysis. However, existing clustering methods often provide different results on cluster assignments with respect to their own data pre-processing, choice of distance metrics, and strategies of feature extraction, thereby limiting their practical applications.
resultsWe propose Cross-Tabulation Ensemble Clustering (CTEC) method that formulates two re-clustering strategies (distribution- and outlier-based) via cross-tabulation. Benchmarking experiments on five scRNA-Seq datasets illustrate that the proposed CTEC method offers significant improvements over the individual clustering methods. Moreover, CTEC-DB outperforms the state-of-the-art ensemble methods for single-cell data clustering, with 45.4% and 17.1% improvement over the single-cell aggregated from ensemble clustering method (SAFE) and the single-cell aggregated clustering via Mixture model ensemble method (SAME), respectively, on the two-method ensemble test. AVAILABILITY AND IMPLEMENTATION: The source code of the benchmark in this work is available at the GitHub repository https://github.com/LWCHN/CTEC.git.
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