TY - JOUR
T1 - A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing
AU - Aevermann, Brian
AU - Zhang, Yun
AU - Novotny, Mark
AU - Keshk, Mohamed
AU - Bakken, Trygve
AU - Miller, Jeremy
AU - Hodge, Rebecca
AU - Lelieveldt, Boudewijn
AU - Lein, Ed
AU - Scheuermann, Richard H.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85115382686&partnerID=8YFLogxK
U2 - 10.1101/gr.275569.121
DO - 10.1101/gr.275569.121
M3 - Article
C2 - 34088715
AN - SCOPUS:85115382686
SN - 1088-9051
VL - 31
SP - 1767
EP - 1780
JO - Genome Research
JF - Genome Research
IS - 10
ER -