ArticlePloS one2022
Machine learning for cell type classification from single nucleus RNA sequencing data.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Machine learning and computational approaches to model therapeutic response and resistance in diffuse midline glioma.Journal of neuro-oncology · 2026Review
- Partial domain adaptation enables cross domain cell type annotation between scRNA-seq and snRNA-seq.PLoS computational biology · 2026Article
- AI-Driven Innovations for Quality Control and Standardization: Future Strategies in Adipose-Derived Stem Cell Manufacturing.International journal of molecular sciences · 2026Review
- Inference of marker genes of subtle cell state changes via iLR: iterative logistic regression.Bioinformatics (Oxford, England) · 2026Article
- Computational tracking of cell origins using CellSexID from single-cell transcriptomes.Cell reports methods · 2025Article
- Automated classification of cellular expression in multiplexed imaging data with Nimbus.Nature methods · 2025Article
- Adaptive individualized gene pair signatures distinguishing melanoma and predicting response to immune checkpoint blockade.iScience · 2025Article
- AnnoGCD: a generalized category discovery framework for automatic cell type annotation.NAR genomics and bioinformatics · 2024Article
- Predictive biomarkers for embryotoxicity: a machine learning approach to mitigating multicollinearity in RNA-Seq.Archives of toxicology · 2024Article
- Identification of kidney cell types in scRNA-seq and snRNA-seq data using machine learning algorithms.Heliyon · 2024Article
- scGAA: a general gated axial-attention model for accurate cell-type annotation of single-cell RNA-seq data.Scientific reports · 2024Article
- AITeQ: a machine learning framework for Alzheimer's prediction using a distinctive five-gene signature.Briefings in bioinformatics · 2024Article
- Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets.Cell reports. Medicine · 2024Article
- Discovery of optimal cell type classification marker genes from single cell RNA sequencing data.BMC methods · 2024Article
- Using LLMs and Explainable ML to Analyze Biomarkers at Single-Cell Level for Improved Understanding of Diseases.Biomolecules · 2023Article
- Tree-Based Machine Learning Models with Optuna in Predicting Impedance Values for Circuit Analysis.Micromachines · 2023Article
- Adversarial training improves model interpretability in single-cell RNA-seq analysis.Bioinformatics advances · 2023Article
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Authors and funding
7 authors.
Funding
Abstract
With the advent of single cell/nucleus RNA sequencing (sc/snRNA-seq), the field of cell phenotyping is now a data-driven exercise providing statistical evidence to support cell type/state categorization. However, the task of classifying cells into specific, well-defined categories with the empirical data provided by sc/snRNA-seq remains nontrivial due to the difficulty in determining specific differences between related cell types with close transcriptional similarities, resulting in challenges with matching cell types identified in separate experiments. To investigate possible approaches to overcome these obstacles, we explored the use of supervised machine learning methods-logistic regression, support vector machines, random forests, neural networks, and light gradient boosting machine (LightGBM)-as approaches to classify cell types using snRNA-seq datasets from human brain middle temporal gyrus (MTG) and human kidney. Classification accuracy was evaluated using an F-beta score weighted in favor of precision to account for technical artifacts of gene expression dropout. We examined the impact of hyperparameter optimization and feature selection methods on F-beta score performance. We found that the best performing model for granular cell type classification in both datasets is a multinomial logistic regression classifier and that an effective feature selection step was the most influential factor in optimizing the performance of the machine learning pipelines.
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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.