Dynamic Model for RNA-seq Data Analysis

TL;DR

This paper introduces a dynamic model using ordinary differential equations to analyze RNA-seq data, improving accuracy in capturing transcription processes and classifying normal and tumor cells.

Contribution

A novel ODE-based approach for RNA-seq data analysis with location-varying coefficients and classification capabilities.

Findings

The ODE model accurately fits RNA-seq data as validated by prediction analysis and cross-validation.

Using the ODE model for single genes achieves high classification accuracy between normal and tumor cells.

Response analysis identifies dozens of cancer-related genes affected by external signal perturbations.

Abstract

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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