title: Inferring gene regulatory networks by integrating ChIP-seq/chip and transcriptome data via LASSO-type regularization methods.
abstract: Inferring gene regulatory networks from gene expression data at whole genome level is still an arduous challenge, especially in higher organisms where the number of genes is large but the number of experimental samples is small. It is reported that the accuracy of current methods at genome scale significantly drops from Escherichia coli to Saccharomyces cerevisiae due to the increase in number of genes. This limits the applicability of current methods to more complex genomes, like human and mouse. Least absolute shrinkage and selection operator (LASSO) is widely used for gene regulatory network inference from gene expression profiles. However, the accuracy of LASSO on large genomes is not satisfactory. In this study, we apply two extended models of LASSO, L0 and L1/2 regularization models to infer gene regulatory network from both high-throughput gene expression data and transcription factor binding data in mouse embryonic stem cells (mESCs). We find that both the L0 and L1/2 regularization models significantly outperform LASSO in network inference. Incorporating interactions between transcription factors and their targets remarkably improved the prediction accuracy. Current study demonstrates the efficiency and applicability of these two models for gene regulatory network inference from integrative omics data in large genomes. The applications of the two models will facilitate biologists to study the gene regulation of higher model organisms in a genome-wide scale.
authors.affiliation: Department of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Shenzhen Institute of Research & Innovation, The University of Hong Kong, Shenzhen, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Qin
authors.affiliation: Department of Mathematics, Zhejiang University, Hangzhou, China; Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region, China.
authors.forename: Yaohua
authors.initials: Y
authors.lastname: Hu
authors.affiliation: Department of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Shenzhen Institute of Research & Innovation, The University of Hong Kong, Shenzhen, China.
authors.forename: Feng
authors.initials: F
authors.lastname: Xu
authors.affiliation: Department of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Shenzhen Institute of Research & Innovation, The University of Hong Kong, Shenzhen, China.
authors.forename: Hari Krishna
authors.initials: HK
authors.lastname: Yalamanchili
authors.affiliation: Department of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Shenzhen Institute of Research & Innovation, The University of Hong Kong, Shenzhen, China; Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China. Electronic address: junwen@hku.hk.
authors.forename: Junwen
authors.initials: J
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D047369:Chromatin Immunoprecipitation / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D016678:Genome; D008957:Models, Genetic
keywords: ChIP-seq/chip; Gene regulatory networks; Integrative omics data; LASSO-type regularization methods; Transcriptome