ScLinear predicts protein abundance at single-cell resolution

Single-cell multi-omics have transformed biomedical research and present exciting machine learning opportunities. We present scLinear, a linear regression-based approach that predicts single-cell protein abundance based on RNA expression. ScLinear is vastly more efficient than state-of-the-art metho...

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Bibliographic Details
Published in:Communications biology 2024-03, Vol.7 (1), p.267-7, Article 267
Main Authors: Hanhart, Daniel, Gossi, Federico, Rapsomaniki, Maria Anna, Kruithof-de Julio, Marianna, Chouvardas, Panagiotis
Format: Article
Language:English
Subjects:
631/114/1305
631/337/2019
631/553/794
Algorithms
Biology
Biomedical and Life Sciences
Biomedical Research
Dance
Datasets
Deep learning
Gene expression
Gene Expression Profiling
Learning algorithms
Life Sciences
Linear Models
Machine Learning
Medical research
Proteins
Quality control
Regression analysis
Single-cell protein
Transcriptomics
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Summary:Single-cell multi-omics have transformed biomedical research and present exciting machine learning opportunities. We present scLinear, a linear regression-based approach that predicts single-cell protein abundance based on RNA expression. ScLinear is vastly more efficient than state-of-the-art methodologies, without compromising its accuracy. ScLinear is interpretable and accurately generalizes in unseen single-cell and spatial transcriptomics data. Importantly, we offer a critical view in using complex algorithms ignoring simpler, faster, and more efficient approaches. scLinear is a simple linear regression model that outperforms complex machine/deep learning approaches in predicting protein abundance at single-cell resolution.
ISSN:2399-3642
2399-3642
DOI:10.1038/s42003-024-05958-4