ArticleCell genomics2024
SIMS: A deep-learning label transfer tool for single-cell RNA sequencing analysis.
Article in Cell genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mapping the Cerebral Organoid Landscape: A Systematic Review of Preclinical 3D Models in Neuroscience.Advanced healthcare materials · 2026Pooled it
- celltypeEnrich: a consensus-based scRNA-seq cluster annotation tool.bioRxiv : the preprint server for biology · 2026Article
- HIPPIE: a generative model for electrophysiological analysis across species, technologies, and modalities.Nature communications · 2026Article
- ProtoCloud: A prototypical self-explaining model for single-cell analysis.Cell genomics · 2026Article
- NeMO Analytics: a compendium of transcriptomic data for the exploration of neocortical development.Nature neuroscience · 2026Article
- CytoVerse: Single-Cell AI Foundation Models in the Browser.bioRxiv : the preprint server for biology · 2026Article
- Fate plasticity of interneuron specification.iScience · 2025Article
- Toward automated and explainable high-throughput perturbation analysis in single cells.Patterns (New York, N.Y.) · 2025Article
- Consensus representation of multiple cell-cell graphs from gene signaling pathways for cell type annotation.BMC biology · 2025Article
- Intestinal organoids in inflammatory bowel disease: advances, applications, and future directions.Frontiers in cell and developmental biology · 2025Review
- Mapping Cell Identity from scRNA-seq: A primer on computational methods.Computational and structural biotechnology journal · 2025Review
- QuickVol: A lightweight browser tool for immersive visualizations of volumetric data.iScience · 2024Article
- BrainCellR: A precise cell type nomenclature pipeline for comparative analysis across brain single-cell datasets.Computational and structural biotechnology journal · 2024Article
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Abstract
Cell atlases serve as vital references for automating cell labeling in new samples, yet existing classification algorithms struggle with accuracy. Here we introduce SIMS (scalable, interpretable machine learning for single cell), a low-code data-efficient pipeline for single-cell RNA classification. We benchmark SIMS against datasets from different tissues and species. We demonstrate SIMS's efficacy in classifying cells in the brain, achieving high accuracy even with small training sets (<3,500 cells) and across different samples. SIMS accurately predicts neuronal subtypes in the developing brain, shedding light on genetic changes during neuronal differentiation and postmitotic fate refinement. Finally, we apply SIMS to single-cell RNA datasets of cortical organoids to predict cell identities and uncover genetic variations between cell lines. SIMS identifies cell-line differences and misannotated cell lineages in human cortical organoids derived from different pluripotent stem cell lines. Altogether, we show that SIMS is a versatile and robust tool for cell-type classification from single-cell datasets.
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