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Identification of gene expression signatures across different types of neural stem cells with the Monte‐Carlo feature selection method
Author(s) -
Chen Lei,
Li JiaRui,
Zhang YuHang,
Feng KaiYan,
Wang ShaoPeng,
Zhang YunHua,
Huang Tao,
Kong Xiangyin,
Cai YuDong
Publication year - 2018
Publication title -
journal of cellular biochemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.028
H-Index - 165
eISSN - 1097-4644
pISSN - 0730-2312
DOI - 10.1002/jcb.26507
Subject(s) - neural stem cell , progenitor cell , biology , stem cell , lineage (genetic) , gene , computational biology , microbiology and biotechnology , neurosphere , cellular differentiation , adult stem cell , genetics
Adult neural stem cells (NSCs) are a group of multi‐potent, self‐renewing progenitor cells that contribute to the generation of new neurons and oligodendrocytes. Three subtypes of NSCs can be isolated based on the stages of the NSC lineage, including quiescent neural stem cells (qNSCs), activated neural stem cells (aNSCs) and neural progenitor cells (NPCs). Although it is widely accepted that these three groups of NSCs play different roles in the development of the nervous system, their molecular signatures are poorly understood. In this study, we applied the Monte‐Carlo Feature Selection (MCFS) method to identify the gene expression signatures, which can yield a Matthews correlation coefficient (MCC) value of 0.918 with a support vector machine evaluated by ten‐fold cross‐validation. In addition, some classification rules yielded by the MCFS program for distinguishing above three subtypes were reported. Our results not only demonstrate a high classification capacity and subtype‐specific gene expression patterns but also quantitatively reflect the pattern of the gene expression levels across the NSC lineage, providing insight into deciphering the molecular basis of NSC differentiation.