NS-Forest: A machine learning method for the objective identification of minimum marker gene combinations for cell type determination from single cell RNA sequencing

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract Single cell genomics is rapidly advancing our knowledge of cell phenotypic types and states. Driven by single cell/nucleus RNA sequencing (scRNA-seq) data, comprehensive atlas projects covering a wide range of organisms and tissues are currently underway. As a result, it is critical that the cell 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 molecular drivers. Here we describe a machine learning-based marker gene selection algorithm, NS-Forest version 2.0, which leverages the non-linear attributes of random forest feature selection and a binary expression scoring approach to discover the minimal marker gene expression combinations that precisely captures the cell type identity represented in the complete scRNA-seq transcriptional profiles. The marker genes selected provide a barcode of the necessary and sufficient characteristics for semantic cell type definition and serve as useful tools for downstream biological investigation. The use of NS-Forest to identify marker genes for human brain middle temporal gyrus cell types reveals the importance of cell signaling and non-coding RNAs in neuronal cell type identity.

Authors 9

1. Brian D. Aevermann

   J. Craig Venter Institute

   Affiliation as printed

   J. Craig Venter Institute, La Jolla, CA, USA

2. Yun Zhang

   J. Craig Venter Institute

   Affiliation as printed

   J. Craig Venter Institute, La Jolla, CA, USA

3. Mark A. Novotny

   J. Craig Venter Institute

   Affiliation as printed

   J. Craig Venter Institute, La Jolla, CA, USA

4. Trygve E. Bakken

   Allen Institute for Brain Science

   Affiliation as printed

   Allen Institute for Brain Science, Seattle, WA, USA

5. Jeremy A. Miller

   Allen Institute for Brain Science

   Affiliation as printed

   Allen Institute for Brain Science, Seattle, WA, USA

6. Rebecca D. Hodge

   Allen Institute for Brain Science

   Affiliation as printed

   Allen Institute for Brain Science, Seattle, WA, USA

7. Boudewijn P. F. Lelieveldt Aachen Department of Radiology, Leiden University Medical Center

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Department of Intelligent Systems, Delft University of Technology, Delft, the Netherlands

   Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands

8. Ed S. Lein

   Allen Institute for Brain Science

   Affiliation as printed

   Allen Institute for Brain Science, Seattle, WA, USA

9. Richard H. Scheuermann corresponding

   J. Craig Venter Institute · La Jolla Institute for Immunology · Delft University of Technology

   Affiliation as printed

   Department of Intelligent Systems, Delft University of Technology, Delft, the Netherlands

   J. Craig Venter Institute, La Jolla, CA, USA

   La Jolla Institute for Immunology, La Jolla, CA, USA