title: Identifying local co-regulation relationships in gene expression data.
abstract: Identifying interesting relationships between pairs of genes, presented over some of experimental conditions in gene expression data set, is useful for discovering novel functional gene interactions. In this paper, we introduce a new method for id entifying L ocal C o-regulation R elationships (IdLCR). These local relationships describe the behaviors of pairwise genes, which are either up- or down-regulated throughout the identified condition subset. IdLCR firstly detects the pairwise gene-gene relationships taking functional forms and the condition subsets by using a regression spline model. Then it measures the relationships using a penalized Pearson correlation and ranks the responding gene pairs by their scores. By this way, those relationships without clearly biological interpretations can be filtered out and the local co-regulation relationships can be obtained. In the simulation data sets, ten different functional relationships are embedded. Applying IdLCR to these data sets, the results show its ability to identify functional relationships and the condition subsets. For micro-array and RNA-seq gene expression data, IdLCR can identify novel biological relationships which are different from those uncovered by IFGR and MINE.
authors.affiliation: College of Mathematics and Information Science, Henan Normal University, Xinxiang 453007, China. Electronic address: peiyonggang@htu.edu.cn.
authors.forename: Yonggang
authors.initials: Y
authors.lastname: Pei
authors.affiliation: College of Mathematics and Information Science, Henan Normal University, Xinxiang 453007, China. Electronic address: qinghuigaocb@163.com.
authors.forename: Qinghui
authors.initials: Q
authors.lastname: Gao
authors.affiliation: College of Mathematics and Information Science, Henan Normal University, Xinxiang 453007, China. Electronic address: juntaolimail@126.com.
authors.forename: Juntao
authors.initials: J
authors.lastname: Li
authors.affiliation: College of Life Science, Henan Normal University, Xinxiang 453007, China. Electronic address: zhaoxt0411@126.com.
authors.forename: Xiting
authors.initials: X
authors.lastname: Zhao
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004843:Epistasis, Genetic / Q000235:genetics*; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000235:genetics / Q000502:physiology*; D005796:Genes / Q000502:physiology*; D008957:Models, Genetic; D012044:Regression Analysis
keywords: B-spline; Correlation