A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data
Authors: Hui Peng Li
Hiroaki Kitano
Lawrence Jin Kiat Wee
Shyam Prabhakar
Takeshi Hase
Yongli Hu
Samik Ghosh
See Kiong Ng
Journal: BMC Genomics (2016)
Discipline: MOLECULAR BIOLOGY & GENETICS
Keywords: Machine learning; Network reconstruction; Single-cell RNA-seq; Systems biology.
Affiliations: 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, Tokyo, 108-0071, Japan., Computational and Systems Biology, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Genome, #02-01, Singapore, 138672, 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.

Abstract:  Background: The 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)). Results: Thirty-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. Conclusion: This 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. 

Retracted: No

PubPeer comments:
 I think this may be my fault for not making the reason for the citation clear (on the [sRAP Bioconductor page] ), but you should only cite the BD-Func paper if you used BD-Func in sRAP. Otherwise, I think the fold-change paper should be sufficient. To be honest, I don t currently support sRAP (and I don t actually recommend a complete pipeline strategy for gene expression analysis, as explained [here] ). I am trying to work on having data to justify the template system [here] , but I am not actually recommend others use those exact file (it is a more of a recommendation for each individual to have their own template, with code that can be modified for each project. **In terms of your paper, I believe you only need citation [33], not citation [32]** (ii) Selection of differentially expressed (DE) genes using the R package Simplified RNA-Seq Analysis Pipeline (sRAP) [32, 33]. 32. Warden CD, Kanaya N, Chen S, Yuan YC. BD-Func: a streamlined algorithm for predicting activation and inhibition of pathways. PeerJ. 2013;1:e159. doi:10.7717 peerj.159. PubMed PMID: 24058887, PubMed Central PMCID: PMC3775632. **33. Warden CD, Yuan Y-C, Wu X. Optimal calculation of RNA-Seq fold-change values. Int J Comput Bioinformatics In Silico Model. 2013;2(6):285u201392.** ,