ArticleGenome research2018
bigSCale: an analytical framework for big-scale single-cell data.
Article in Genome research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- Gene regulatory network transitions reveal the central transcription factors in lung adenocarcinoma progression.NPJ systems biology and applications · 2026Article
- Partial Deletion ofeNeuro · 2026Article
- Capicua regulates the survival of Cajal-Retzius cells in the postnatal hippocampus.Cell death & disease · 2025Article
- Single-cell transcriptomic profiling of heart reveals ANGPTL4 linking fibroblasts and angiogenesis in heart failure with preserved ejection fraction.Journal of advanced research · 2025Article
- Augmenting the human interactome for disease prediction through gene networks inferred from human cell atlas.Animal cells and systems · 2025Article
- Detecting significant expression patterns in single-cell and spatial transcriptomics with a flexible computational approach.Scientific reports · 2024Article
- Clustering single-cell RNA sequencing data via iterative smoothing and self-supervised discriminative embedding.Oncogene · 2024Article
- Building and analyzing metacells in single-cell genomics data.Molecular systems biology · 2024Review
- High throughput functional profiling of genes at intraocular pressure loci reveals distinct networks for glaucoma.Human molecular genetics · 2024Article
- A novel interpretable deep transfer learning combining diverse learnable parameters for improved T2D prediction based on single-cell gene regulatory networks.Scientific reports · 2024Article
- Epithelial plasticity and innate immune activation promote lung tissue remodeling following respiratory viral infection.Nature communications · 2023Article
- Probabilistic tensor decomposition extracts better latent embeddings from single-cell multiomic data.Nucleic acids research · 2023Article
- Standardization of Single-Cell RNA-Sequencing Analysis Workflow to Study Drosophila Ovary.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Integrated analysis of single-cell RNA-seq and bulk RNA-seq reveals RNA N6-methyladenosine modification associated with prognosis and drug resistance in acute myeloid leukemia.Frontiers in immunology · 2023Article
- Diminished function of cytotoxic T- and NK- cells in severe alcohol-associated hepatitis.Metabolism and target organ damage · 2022Article
- A new gene set identifies senescent cells and predicts senescence-associated pathways across tissues.Nature communications · 2022Article
- Metacells untangle large and complex single-cell transcriptome networks.BMC bioinformatics · 2022Article
- Evaluation of single-cell RNA-seq clustering algorithms on cancer tumor datasets.Computational and structural biotechnology journal · 2022Article
- Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics.Nature communications · 2021Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell RNA sequencing (scRNA-seq) has significantly deepened our insights into complex tissues, with the latest techniques capable of processing tens of thousands of cells simultaneously. Analyzing increasing numbers of cells, however, generates extremely large data sets, extending processing time and challenging computing resources. Current scRNA-seq analysis tools are not designed to interrogate large data sets and often lack sensitivity to identify marker genes. With bigSCale, we provide a scalable analytical framework to analyze millions of cells, which addresses the challenges associated with large data sets. To handle the noise and sparsity of scRNA-seq data, bigSCale uses large sample sizes to estimate an accurate numerical model of noise. The framework further includes modules for differential expression analysis, cell clustering, and marker identification. A directed convolution strategy allows processing of extremely large data sets, while preserving transcript information from individual cells. We evaluated the performance of bigSCale using both a biological model of aberrant gene expression in patient-derived neuronal progenitor cells and simulated data sets, which underlines the speed and accuracy in differential expression analysis. To test its applicability for large data sets, we applied bigSCale to assess 1.3 million cells from the mouse developing forebrain. Its directed down-sampling strategy accumulates information from single cells into index cell transcriptomes, thereby defining cellular clusters with improved resolution. Accordingly, index cell clusters identified rare populations, such as reelin (
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Registered trials
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.