Dataset Information

SMARTS: reconstructing disease response networks from multiple individuals using time series gene expression data.

ABSTRACT:

Motivation

Current methods for reconstructing dynamic regulatory networks are focused on modeling a single response network using model organisms or cell lines. Unlike these models or cell lines, humans differ in their background expression profiles due to age, genetics and life factors. In addition, there are often differences in start and end times for time series human data and in the rate of progress based on the specific individual. Thus, new methods are required to integrate time series data from multiple individuals when modeling and constructing disease response networks.

Results

We developed Scalable Models for the Analysis of Regulation from Time Series (SMARTS), a method integrating static and time series data from multiple individuals to reconstruct condition-specific response networks in an unsupervised way. Using probabilistic graphical models, SMARTS iterates between reconstructing different regulatory networks and assigning individuals to these networks, taking into account varying individual start times and response rates. These models can be used to group different sets of patients and to identify transcription factors that differentiate the observed responses between these groups. We applied SMARTS to analyze human response to influenza and mouse brain development. In both cases, it was able to greatly improve baseline groupings while identifying key relevant TFs that differ between the groups. Several of these groupings and TFs are known to regulate the relevant processes while others represent novel hypotheses regarding immune response and development.

Availability and implementation

Software and Supplementary information are available at http://sb.cs.cmu.edu/smarts/.

Contact

zivbj@cs.cmu.edu

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Wise A

PROVIDER: S-EPMC4393515 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

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Publications

SMARTS: reconstructing disease response networks from multiple individuals using time series gene expression data.

Wise Aaron A Bar-Joseph Ziv Z

Bioinformatics (Oxford, England) 20141204 8

<h4>Motivation</h4>Current methods for reconstructing dynamic regulatory networks are focused on modeling a single response network using model organisms or cell lines. Unlike these models or cell lines, humans differ in their background expression profiles due to age, genetics and life factors. In addition, there are often differences in start and end times for time series human data and in the rate of progress based on the specific individual. Thus, new methods are required to integrate time s ...[more]

PMID: 25480376

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Project description:BackgroundDeciphering the relationship between clinical responses and gene expression profiles may shed light on the mechanisms underlying diseases. Most existing literature has focused on exploring such relationship from cross-sectional gene expression data. It is likely that the dynamic nature of time-series gene expression data is more informative in predicting clinical response and revealing the physiological process of disease development. However, it remains challenging to extract useful dynamic information from time-series gene expression data.ResultsWe propose a statistical framework built on considering co-expression network changes across time from time series gene expression data. It first detects change point for co-expression networks and then employs a Bayesian multiple kernel learning method to predict exposure response. There are two main novelties in our method: the use of change point detection to characterize the co-expression network dynamics, and the use of kernel function to measure the similarity between subjects. Our algorithm allows exposure response prediction using dynamic network information across a collection of informative gene sets. Through parameter estimations, our model has clear biological interpretations. The performance of our method on the simulated data under different scenarios demonstrates that the proposed algorithm has better explanatory power and classification accuracy than commonly used machine learning algorithms. The application of our method to time series gene expression profiles measured in peripheral blood from a group of subjects with respiratory viral exposure shows that our method can predict exposure response at early stage (within 24 h) and the informative gene sets are enriched for pathways related to respiratory and influenza virus infection.ConclusionsThe biological hypothesis in this paper is that the dynamic changes of the biological system are related to the clinical response. Our results suggest that when the relationship between the clinical response and a single gene or a gene set is not significant, we may benefit from studying the relationships among genes in gene sets that may lead to novel biological insights.

Dataset Information

SMARTS: reconstructing disease response networks from multiple individuals using time series gene expression data.

Motivation

Results

Availability and implementation

Contact

Supplementary information

Publications

SMARTS: reconstructing disease response networks from multiple individuals using time series gene expression data.

Similar Datasets

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