An Ordinary Differential Equation Model for Gene Regulation with RNA-Seq Data

• Activity Number: 340
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Lerong Li*+ and Momiao Xiong
• Companies: University of Texas at Houston and University of Texas Health Science Center at Houston
• Keywords: ODE ; Functional Data Analysis ; Dynamic Model ; RNA_seq

Abstract

Variation in gene expression holds a key to unraveling mechanism of gene regulation. However, the gene expression measured by micro-array provides limited information to reveal the features of gene regulation. The rapidly developed RNA-seq for expression profiling offers comprehensive picture of transcriptome and has made a number of significant qualitative and quantitative improvements on gene expression analysis and provides multiple layers of resolutions. To unravel the features of gene transcription, we use a second order differential equation (ODE) with time-varying coefficients to model the dynamics of transcription process and functional principal component analysis to estimate the parameters. The proposed model is applied to Kidney Renal Clear Cell Carcinoma (KIRC) RNA-seq data with 72 matched pair of KIRC and normal samples from TCGA dataset. We showed that the ODE model has significant biological interpretation. Using a single gene we can reach as high as 90% classification accuracy. Using ODE model, we also identified the genes that showed significantly differential dynamic behaviors to respond environmental perturbation between tumor and normal samples.