Gene regulatory effects inference for cell fate determination based on single-cell resolution data

Xiao Tai Huang, Leanne L.H. Chan, Zhongying ZHAO, Hong Yan

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

Abstract

Cell differentiation is a complicated biological process, involving lots of genes, which decide cell fate in early embryo development. These genes govern cell type formation (tissue formation). However, regulatory mechanism among these genes are not clear. In this paper, we infer gene regulatory effects for tissue formation to reveal gene regulatory mechanism based on state-of-the-art single-cell resolution data. Specifically, we disdose the gene regulatory mechanism about pha-4 gene, a gene highly related to pharynx formation, in Caenorhabditis elegans. We quantify pha-4 gene expression at cellular level to supervise the process of pharynx tissue development. Two types of data, wild-type data and mutant data, are exploited in our experiment. Wild-type data is pha-4 gene expression data under normal development condition, while mutant data represents the gene expression data under abnormal development condition which means perturbation (gene knockdown) of some other genes. By comparing these two types of data based on two statistical hypothesis tests pipelines, gene regulatory effects have been investigated. In total, we have inferred gene knockdown effects of 579 genes. 74 of them possess either activating or inhibiting effect on pha-4 gene. Finally, gene regulatory networks of pharynx tissue formation in C. elegans have been constructed based on these inferred gene effect results.

Original languageEnglish
Title of host publicationProceedings of 2015 International Conference on Machine Learning and Cybernetics, ICMLC 2015
PublisherIEEE Computer Society
Pages283-288
Number of pages6
ISBN (Electronic)9781467372213
DOIs
Publication statusPublished - 30 Nov 2015
Event14th International Conference on Machine Learning and Cybernetics, ICMLC 2015 - Guangzhou, China
Duration: 12 Jul 201515 Jul 2015

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
Volume1
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Conference

Conference14th International Conference on Machine Learning and Cybernetics, ICMLC 2015
Country/TerritoryChina
CityGuangzhou
Period12/07/1515/07/15

Scopus Subject Areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Human-Computer Interaction

User-Defined Keywords

  • Cell fate
  • Cell lineage tracing
  • Gene expression
  • Gene regulatory network
  • Single-cell resolution data

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