Abstract
BackgroundIntegrated analysis that uses multiple sample gene expression data measured under the same stress can detect stress response genes more accurately than analysis of individual sample data. However, the integrated analysis is challenging since experimental conditions (strength of stress and the number of time points) are heterogeneous across multiple samples.ResultsHTRgene is a computational method to perform the integrated analysis of multiple heterogeneous time-series data measured under the same stress condition. The goal of HTRgene is to identify “response order preserving DEGs” that are defined as genes not only which are differentially expressed but also whose response order is preserved across multiple samples. The utility of HTRgene was demonstrated using 28 and 24 time-series sample gene expression data measured under cold and heat stress in Arabidopsis. HTRgene analysis successfully reproduced known biological mechanisms of cold and heat stress in Arabidopsis. Also, HTRgene showed higher accuracy in detecting the documented stress response genes than existing tools.ConclusionsHTRgene, a method to find the ordering of response time of genes that are commonly observed among multiple time-series samples, successfully integrated multiple heterogeneous time-series gene expression datasets. It can be applied to many research problems related to the integration of time series data analysis.
Highlights
Over the past two decades, the rapid development of molecular measurement technologies, such as microarray [1] and RNA sequencing (RNA-Seq) [2], have improved scalability and accuracy and reduced time and cost in measuring expression levels of all genes in a cell, whichAnalysis of detecting differentially expressed genes (DEGs) has been widely performed [3] to identify stressAhn et al BMC Bioinformatics 2019, 20(Suppl 16):588 response signaling genes from transcriptome data that are measured under stress condition
“Response order preserving DEGs” are defined as genes which are differentially expressed and whose response order is preserved across multiple samples
HTRgene analysis of Heterogeneous time-series dataset of cold and heat stresses HTRgene analysis was performed for heat and cold stress time-series data in Arabidopsis
Summary
Over the past two decades, the rapid development of molecular measurement technologies, such as microarray [1] and RNA sequencing (RNA-Seq) [2], have improved scalability and accuracy and reduced time and cost in measuring expression levels of all genes in a cell, whichAnalysis of detecting differentially expressed genes (DEGs) has been widely performed [3] to identify stressAhn et al BMC Bioinformatics 2019, 20(Suppl 16):588 response signaling genes from transcriptome data that are measured under stress condition. Kreps [3] and Matsui [4] reported 2086 and 996 DEGs for cold stress in Arabidopsis, respectively, and only 232 DEGs, about 16% of the union of two DEG sets, were commonly determined. This result shows the requirement of a robust analysis of gene expression datasets. Integrated analysis that uses multiple sample gene expression data measured under the same stress can detect stress response genes more accurately than analysis of individual sample data. The integrated analysis is challenging since experimental conditions (strength of stress and the number of time points) are heterogeneous across multiple samples
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