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2015 ; 2015
(ä): 916352
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Dynamic Model for RNA-seq Data Analysis
#MMPMID26346000
Li L
; Xiong M
Biomed Res Int
2015[]; 2015
(ä): 916352
PMID26346000
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By measuring messenger RNA levels for all genes in a sample, RNA-seq provides an
attractive option to characterize the global changes in transcription. RNA-seq is
becoming the widely used platform for gene expression profiling. However, real
transcription signals in the RNA-seq data are confounded with measurement and
sequencing errors and other random biological/technical variation. To extract
biologically useful transcription process from the RNA-seq data, we propose to
use the second ODE for modeling the RNA-seq data. We use differential principal
analysis to develop statistical methods for estimation of location-varying
coefficients of the ODE. We validate the accuracy of the ODE model to fit the
RNA-seq data by prediction analysis and 5-fold cross validation. To further
evaluate the performance of the ODE model for RNA-seq data analysis, we used the
location-varying coefficients of the second ODE as features to classify the
normal and tumor cells. We demonstrate that even using the ODE model for single
gene we can achieve high classification accuracy. We also conduct response
analysis to investigate how the transcription process responds to the
perturbation of the external signals and identify dozens of genes that are
related to cancer.
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