ArticleGenome biology2023
Consequences and opportunities arising due to sparser single-cell RNA-seq datasets.
Article in Genome biology, 2023. 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, 48 citations in OpenAlex.
- BaGGLS: a Bayesian shrinkage framework for interpretable modeling of interactions in high-dimensional biological data.Bioinformatics (Oxford, England) · 2026Article
- Evaluating the role of pretraining dataset size and diversity on single-cell foundation model performance.Nature methods · 2026Article
- scMEDAL: interpretable single-cell transcriptomics analysis with batch effect visualization via deep mixed-effects autoencoder.Nature communications · 2026Article
- Pan-cancer analysis of single-cell profiles with polygenic signals reveals genetic influences on tumor immunity across cancers.Communications biology · 2026Article
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- The tumor microenvironment of medulloblastoma: from emerging biological insights to novel therapeutic targeting.Frontiers in oncology · 2026Review
- Integrating genome size variation, ISSR-derived genetic structure, and phenotypical traits from the Southern Peruvian Andean maize (Frontiers in plant science · 2026Article
- Evaluating the role of pre-training dataset size and diversity on single-cell foundation model performance.bioRxiv : the preprint server for biology · 2025Article
- Mouse-Specific Single cell cytokine activity prediction and Estimation (MouSSE).PLoS computational biology · 2025Article
- DeepAtlas: a tool for effective manifold learning.bioRxiv : the preprint server for biology · 2025Article
- Pathway Analysis Interpretation in the Multi-Omic Era.Biotech (Basel (Switzerland)) · 2025Review
- Triple-effect correction for Cell Painting data with contrastive and domain-adversarial learning.Nature communications · 2025Article
- Prioritizing perturbation-responsive gene patterns using interpretable deep learning.Nature communications · 2025Article
- Facilitate integrated analysis of single cell multiomic data by binarizing gene expression values.Nature communications · 2025Article
- A CRISPR/Cas9-based enhancement of high-throughput single-cell transcriptomics.Nature communications · 2025Article
- Exploring cell-to-cell variability and functional insights through differentially variable gene analysis.NPJ systems biology and applications · 2025Article
- Human Single-Cell RNA-Sequencing Data Supports the Hypothesis of X Chromosome Insensitivity but Is Ineffective in Testing the Dosage Compensation Model.Molecular biology and evolution · 2025Article
- Gene function revealed at the moment of stochastic gene silencingCommunications biology · 2025Article
- scCCTR: An iterative selection-based semi-supervised clustering model for single-cell RNA-seq data.Computational and structural biotechnology journal · 2025Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the number of cells measured in single-cell RNA sequencing (scRNA-seq) datasets increasing exponentially and concurrent increased sparsity due to more zero counts being measured for many genes, we demonstrate here that downstream analyses on binary-based gene expression give similar results as count-based analyses. Moreover, a binary representation scales up to ~ 50-fold more cells that can be analyzed using the same computational resources. We also highlight the possibilities provided by binarized scRNA-seq data. Development of specialized tools for bit-aware implementations of downstream analytical tasks will enable a more fine-grained resolution of biological heterogeneity.
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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.