A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing
Genome Research, vol. 31, pp. 1767–1780
Abstract
Single-cell genomics is rapidly advancing our knowledge of the diversity of cell phenotypes, including both cell types and cell states. Driven by single-cell/-nucleus RNA sequencing (scRNA-seq), comprehensive cell atlas projects characterizing a wide range of organisms and tissues are currently underway. As a result, it is critical that the transcriptional phenotypes discovered are defined and disseminated in a consistent and concise manner. Molecular biomarkers have historically played an important role in biological research, from defining immune cell types by surface protein expression to defining diseases by their molecular drivers. Here, we describe a machine learning-based marker gene selection algorithm, NS-Forest version 2.0, which leverages the nonlinear attributes of random forest feature selection and a binary expression scoring approach to discover the minimal marker gene expression combinations that optimally capture the cell type identity represented in complete scRNA-seq transcriptional profiles. The marker genes selected provide an expression barcode that serves as both a useful tool for downstream biological investigation and the necessary and sufficient characteristics for semantic cell type definition. The use of NS-Forest to identify marker genes for human brain middle temporal gyrus cell types reveals the importance of cell signaling and noncoding RNAs in neuronal cell type identity.
Authors 10
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Affiliation as printed
J. Craig Venter Institute, La Jolla, California 92037, USA
1J. Craig Venter Institute, La Jolla, California 92037, USA
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Affiliation as printed
J. Craig Venter Institute, La Jolla, California 92037, USA
1J. Craig Venter Institute, La Jolla, California 92037, USA
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Affiliation as printed
J. Craig Venter Institute, La Jolla, California 92037, USA
1J. Craig Venter Institute, La Jolla, California 92037, USA
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Affiliation as printed
J. Craig Venter Institute, La Jolla, California 92037, USA
1J. Craig Venter Institute, La Jolla, California 92037, USA
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Allen Institute for Brain Science
Affiliation as printed
Allen Institute for Brain Science, Seattle, Washington 98109, USA
2Allen Institute for Brain Science, Seattle, Washington 98109, USA
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Allen Institute for Brain Science
Affiliation as printed
Allen Institute for Brain Science, Seattle, Washington 98109, USA
2Allen Institute for Brain Science, Seattle, Washington 98109, USA
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Allen Institute for Brain Science
Affiliation as printed
Allen Institute for Brain Science, Seattle, Washington 98109, USA
2Allen Institute for Brain Science, Seattle, Washington 98109, USA
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Boudewijn P. F. Lelieveldt Aachen Department of Radiology, Leiden University Medical Center 3Department of Radiology
Leiden University Medical Center · Delft University of Technology
Affiliation as printed
Department of Intelligent Systems, Delft University of Technology, 2628 Delft, The Netherlands
Department of Radiology, Leiden University Medical Center, 2300 Leiden, The Netherlands
3Department of Radiology, Leiden University Medical Center, 2300 Leiden, The Netherlands
4Department of Intelligent Systems, Delft University of Technology, 2628 Delft, The Netherlands
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Allen Institute for Brain Science
Affiliation as printed
Allen Institute for Brain Science, Seattle, Washington 98109, USA
2Allen Institute for Brain Science, Seattle, Washington 98109, USA
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J. Craig Venter Institute · La Jolla Institute for Immunology · University of California San Diego
Affiliation as printed
J. Craig Venter Institute, La Jolla, California 92037, USA
La Jolla Institute for Immunology, La Jolla, California 92037, USA
University of California San Diego, La Jolla, California 92093, USA
1J. Craig Venter Institute, La Jolla, California 92037, USA
5University of California San Diego, La Jolla, California 92093, USA
6La Jolla Institute for Immunology, La Jolla, California 92037, USA
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