A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data

BMC Genomics

Research

Hui Peng Li¹  Shyam Prabhakar¹  See Kiong Ng²  Lawrence Jin Kiat Wee³  Yongli Hu³  Takeshi Hase⁴  Hiroaki Kitano⁴  Samik Ghosh⁴ 

[1] Computational and Systems Biology, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Genome, #02-01, 138672, Singapore, Singapore;Institute for Infocomm Research, A*STAR, 1 Fusionopolis Way, #21-01 Connexis (South Tower), Singapore, Singapore;Institute for Infocomm Research, A*STAR, 1 Fusionopolis Way, #21-01 Connexis (South Tower), Singapore, Singapore;The Systems Biology Institute, Singapore Node hosted at the Institute for Infocomm Research, A*STAR, Singapore, Singapore;The Systems Biology Institute, Falcon Building 5 F, 5-6-9 Shirokanedai, Minato, 108-0071, Tokyo, Japan;

关键词: Single-cell RNA-seq;    Machine learning;    Network reconstruction;    Systems biology;   

DOI  :  10.1186/s12864-016-3317-7

来源: Springer

【 摘 要 】

BackgroundThe ability to sequence the transcriptomes of single cells using single-cell RNA-seq sequencing technologies presents a shift in the scientific paradigm where scientists, now, are able to concurrently investigate the complex biology of a heterogeneous population of cells, one at a time. However, till date, there has not been a suitable computational methodology for the analysis of such intricate deluge of data, in particular techniques which will aid the identification of the unique transcriptomic profiles difference between the different cellular subtypes. In this paper, we describe the novel methodology for the analysis of single-cell RNA-seq data, obtained from neocortical cells and neural progenitor cells, using machine learning algorithms (Support Vector machine (SVM) and Random Forest (RF)).ResultsThirty-eight key transcripts were identified, using the SVM-based recursive feature elimination (SVM-RFE) method of feature selection, to best differentiate developing neocortical cells from neural progenitor cells in the SVM and RF classifiers built. Also, these genes possessed a higher discriminative power (enhanced prediction accuracy) as compared commonly used statistical techniques or geneset-based approaches. Further downstream network reconstruction analysis was carried out to unravel hidden general regulatory networks where novel interactions could be further validated in web-lab experimentation and be useful candidates to be targeted for the treatment of neuronal developmental diseases.ConclusionThis novel approach reported for is able to identify transcripts, with reported neuronal involvement, which optimally differentiate neocortical cells and neural progenitor cells. It is believed to be extensible and applicable to other single-cell RNA-seq expression profiles like that of the study of the cancer progression and treatment within a highly heterogeneous tumour.

【 授权许可】

CC BY   
© The Author(s). 2016

【 图 表 】

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