ArticleScientific reports2020
Identification of cell types from single cell data using stable clustering.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- A two-pronged strategy eliminates dissociation artifacts for high-fidelity neuroimmune single-cell transcriptomics.Communications biology · 2026Article
- A scalable, multi-resolution consensus clustering approach for prioritizing robust signals from high-throughput screens.Briefings in bioinformatics · 2026Article
- CANTAO: guiding clustering and annotation in single-cell RNA sequencing using average overlap.Molecular systems biology · 2026Article
- Adding highly variable genes to spatially variable genes can improve cell type clustering performance in spatial transcriptomics data.Bioinformatics advances · 2026Article
- GNODEVAE: a graph-based ODE-VAE enhances clustering for single-cell data.BMC genomics · 2025Article
- CHOIR improves significance-based detection of cell types and states from single-cell data.Nature genetics · 2025Article
- Multiplets in scRNA-seq data: Extent of the problem and efficacy of methods for removal.PloS one · 2025Article
- The impact of dropouts in scRNAseq dense neighborhood analysis.Computational and structural biotechnology journal · 2025Article
- A method for in silico exploration of potential glioblastoma multiforme attractors using single-cell RNA sequencing.Scientific reports · 2024Article
- The specious art of single-cell genomics.PLoS computational biology · 2023Article
- ClonoCluster: A method for using clonal origin to inform transcriptome clustering.Cell genomics · 2023Article
- Decision level integration of unimodal and multimodal single cell data with scTriangulate.Nature communications · 2023Article
- Article
- Clustering Deviation Index (CDI): a robust and accurate internal measure for evaluating scRNA-seq data clustering.Genome biology · 2022Article
- Decoding brain memory formation by single-cell RNA sequencing.Briefings in bioinformatics · 2022Article
- Machine learning of COVID-19 clinical data identifies population structures with therapeutic potential.iScience · 2022Article
- Benchmarking clustering algorithms on estimating the number of cell types from single-cell RNA-sequencing data.Genome biology · 2022Article
- Machine learning for cell type classification from single nucleus RNA sequencing data.PloS one · 2022Article
- Single-cell transcriptomics: a novel precision medicine technique in nephrology.The Korean journal of internal medicine · 2021Review
- DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics.International journal of molecular sciences · 2021Article
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4 authors.
Funding
Abstract
Single-cell RNA-seq (scRNASeq) has become a powerful technique for measuring the transcriptome of individual cells. Unlike the bulk measurements that average the gene expressions over the individual cells, gene measurements at individual cells can be used to study several different tissues and organs at different stages. Identifying the cell types present in the sample from the single cell transcriptome data is a common goal in many single-cell experiments. Several methods have been developed to do this. However, correctly identifying the true cell types remains a challenge. We present a framework that addresses this problem. Our hypothesis is that the meaningful characteristics of the data will remain despite small perturbations of data. We validate the performance of the proposed method on eight publicly available scRNA-seq datasets with known cell types as well as five simulation datasets with different degrees of the cluster separability. We compare the proposed method with five other existing methods: RaceID, SNN-Cliq, SINCERA, SEURAT, and SC3. The results show that the proposed method performs better than the existing methods.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.