ArticleBiostatistics (Oxford, England)2022
A probabilistic gene expression barcode for annotation of cell types from single-cell RNA-seq data.
Article in Biostatistics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Article
- CeLLTra: aligning cell names with gene expression via a pathway-informed transformer.Bioinformatics (Oxford, England) · 2026Article
- Single cell RNAseq signatures refined with combiroc enhance identification of NK cells in blood and solid tissues.Scientific reports · 2025Article
- IL-23 Promotes γδT Cell Activity in Dry Eye Disease Progression.Investigative ophthalmology & visual science · 2025Article
- Article
- SIMS: A deep-learning label transfer tool for single-cell RNA sequencing analysis.Cell genomics · 2024Article
- Unraveling Neuronal Identities Using SIMS: A Deep Learning Label Transfer Tool for Single-Cell RNA Sequencing Analysis.bioRxiv : the preprint server for biology · 2023Article
- Predictive and robust gene selection for spatial transcriptomics.Nature communications · 2023Article
- Immune cell type signature discovery and random forest classification for analysis of single cell gene expression datasets.Frontiers in immunology · 2023Article
- Identifying tumor cells at the single-cell level using machine learning.Genome biology · 2022Article
- Single cell gene regulatory networks in plants: Opportunities for enhancing climate change stress resilience.Plant, cell & environment · 2021Review
- Complete deconvolution of DNA methylation signals from complex tissues: a geometric approach.Bioinformatics (Oxford, England) · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Single-cell RNA sequencing (scRNA-seq) quantifies gene expression for individual cells in a sample, which allows distinct cell-type populations to be identified and characterized. An important step in many scRNA-seq analysis pipelines is the annotation of cells into known cell types. While this can be achieved using experimental techniques, such as fluorescence-activated cell sorting, these approaches are impractical for large numbers of cells. This motivates the development of data-driven cell-type annotation methods. We find limitations with current approaches due to the reliance on known marker genes or from overfitting because of systematic differences, or batch effects, between studies. Here, we present a statistical approach that leverages public data sets to combine information across thousands of genes, uses a latent variable model to define cell-type-specific barcodes and account for batch effect variation, and probabilistically annotates cell-type identity from a reference of known cell types. The barcoding approach also provides a new way to discover marker genes. Using a range of data sets, including those generated to represent imperfect real-world reference data, we demonstrate that our approach substantially outperforms current reference-based methods, particularly when predicting across studies.
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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.