ArticleNucleic acids research2019
Latent cellular analysis robustly reveals subtle diversity in large-scale single-cell RNA-seq data.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Unveiling gene modules at Atlas scale through hierarchical clustering of single-cell data.Nature communications · 2026Article
- Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.Cell reports methods · 2026Article
- Biomaterial-mediated Cell Atlas: an insight from single-cell and spatial transcriptomics.Bioactive materials · 2025Review
- Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.bioRxiv : the preprint server for biology · 2025Article
- Comparative benchmarking of single-cell clustering algorithms for transcriptomic and proteomic data.Genome biology · 2025Article
- SpatialKNifeY (SKNY): Extending from spatial domain to surrounding area to identify microenvironment features with single-cell spatial omics data.PLoS computational biology · 2025Article
- Conditional Activation of c-MYC in Distinct Catecholaminergic Cells Drives Development of Neuroblastoma or Somatostatinoma.Cancer research · 2025Article
- Optimization of clustering parameters for single-cell RNA analysis using intrinsic goodness metrics.Frontiers in bioinformatics · 2025Article
- Diverse NKT cells regulate early inflammation and neurological outcomes after cardiac arrest and resuscitation.Science translational medicine · 2024Article
- Synergic activity of FGFR2 and MEK inhibitors in the treatment of FGFR2-amplified cancers of unknown primary.Molecular therapy : the journal of the American Society of Gene Therapy · 2024Article
- Single cell transcriptomic profiling identifies tumor-acquired and therapy-resistant cell states in pediatric rhabdomyosarcoma.Nature communications · 2024Article
- Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference.Briefings in bioinformatics · 2024Article
- CellSTAR: a comprehensive resource for single-cell transcriptomic annotation.Nucleic acids research · 2024Article
- Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference.bioRxiv : the preprint server for biology · 2023Article
- Review
- An interpretable single-cell RNA sequencing data clustering method based on latent Dirichlet allocation.Briefings in bioinformatics · 2023Article
- Single-cell transcriptomics reveal a hyperacute cytokine and immune checkpoint axis after cardiac arrest in patients with poor neurological outcome.Med (New York, N.Y.) · 2023Article
- Genome-wide mapping of cancer dependency genes and genetic modifiers of chemotherapy in high-risk hepatoblastoma.Nature communications · 2023Article
- Evaluation of zero counts to better understand the discrepancies between bulk and single-cell RNA-Seq platforms.Computational and structural biotechnology journal · 2023Article
- ASTER: accurately estimating the number of cell types in single-cell chromatin accessibility data.Bioinformatics (Oxford, England) · 2023Article
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10 authors.
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Abstract
Single-cell RNA sequencing (scRNA-seq) is a powerful tool for characterizing the cell-to-cell variation and cellular dynamics in populations which appear homogeneous otherwise in basic and translational biological research. However, significant challenges arise in the analysis of scRNA-seq data, including the low signal-to-noise ratio with high data sparsity, potential batch effects, scalability problems when hundreds of thousands of cells are to be analyzed among others. The inherent complexities of scRNA-seq data and dynamic nature of cellular processes lead to suboptimal performance of many currently available algorithms, even for basic tasks such as identifying biologically meaningful heterogeneous subpopulations. In this study, we developed the Latent Cellular Analysis (LCA), a machine learning-based analytical pipeline that combines cosine-similarity measurement by latent cellular states with a graph-based clustering algorithm. LCA provides heuristic solutions for population number inference, dimension reduction, feature selection, and control of technical variations without explicit gene filtering. We show that LCA is robust, accurate, and powerful by comparison with multiple state-of-the-art computational methods when applied to large-scale real and simulated scRNA-seq data. Importantly, the ability of LCA to learn from representative subsets of the data provides scalability, thereby addressing a significant challenge posed by growing sample sizes in scRNA-seq data analysis.
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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.