ArticleBMC bioinformatics2020
Impact of data preprocessing on cell-type clustering based on single-cell RNA-seq data.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Network clustering algorithms and preprocessing pipelines for robust cell type identification in single-cell RNA sequencing data.Scientific reports · 2026Article
- Seurat function argument values in scRNA-seq data analysis: potential pitfalls and refinements for biological interpretation.Frontiers in bioinformatics · 2025Article
- On Leveraging Machine Learning in Sport Science in the Hypothetico-deductive Framework.Sports medicine - open · 2024Article
- Computational approach for decoding malaria drug targets from single-cell transcriptomics and finding potential drug molecule.Scientific reports · 2024Article
- The effect of data transformation on low-dimensional integration of single-cell RNA-seq.BMC bioinformatics · 2024Article
- Omada: robust clustering of transcriptomes through multiple testing.GigaScience · 2024Article
- Robust, scalable, and informative clustering for diverse biological networks.Genome biology · 2023Article
- Big Data in Gastroenterology Research.International journal of molecular sciences · 2023Review
- scIMC: a platform for benchmarking comparison and visualization analysis of scRNA-seq data imputation methods.Nucleic acids research · 2022Article
- Data-driven assessment of dimension reduction quality for single-cell omics data.Patterns (New York, N.Y.) · 2022Article
- Modelling the bioinformatics tertiary analysis research process.BMC bioinformatics · 2021Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundAdvances in single-cell RNA-seq technology have led to great opportunities for the quantitative characterization of cell types, and many clustering algorithms have been developed based on single-cell gene expression. However, we found that different data preprocessing methods show quite different effects on clustering algorithms. Moreover, there is no specific preprocessing method that is applicable to all clustering algorithms, and even for the same clustering algorithm, the best preprocessing method depends on the input data.
resultsWe designed a graph-based algorithm, SC3-e, specifically for discriminating the best data preprocessing method for SC3, which is currently the most widely used clustering algorithm for single cell clustering. When tested on eight frequently used single-cell RNA-seq data sets, SC3-e always accurately selects the best data preprocessing method for SC3 and therefore greatly enhances the clustering performance of SC3.
conclusionThe SC3-e algorithm is practically powerful for discriminating the best data preprocessing method, and therefore largely enhances the performance of cell-type clustering of SC3. It is expected to play a crucial role in the related studies of single-cell clustering, such as the studies of human complex diseases and discoveries of new cell types.
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