Analyzing time series gene expression data
TLDR
This review is intended to serve as both, a point of reference for experimental biologists looking for practical solutions for analyzing their data, and a starting point for computer scientists interested in working on the computational problems related to time series expression analysis.Abstract:
Motivation: Time series expression experiments are an increasingly popular method for studying a wide range of biological systems. However, when analyzing these experiments researchers face many new computational challenges. Algorithms that are specifically designed for time series experiments are required so that we can take advantage of their unique features (such as the ability to infer causality from the temporal response pattern) and address the unique problems they raise (e.g. handling the different non-uniform sampling rates).
Results: We present a comprehensive review of the current research in time series expression data analysis. We divide the computational challenges into four analysis levels: experimental design, data analysis, pattern recognition and networks. For each of these levels, we discuss computational and biological problems at that level and point out some of the methods that have been proposed to deal with these issues. Many open problems in all these levels are discussed. This review is intended to serve as both, a point of reference for experimental biologists looking for practical solutions for analyzing their data, and a starting point for computer scientists interested in working on the computational problems related to time series expression analysis.read more
Citations
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Journal ArticleDOI
Independent Component Analysis (ICA) based-clustering of temporal RNA-seq data.
Moysés Nascimento,Fabyano Fonseca e Silva,Thelma Sáfadi,Ana Carolina Campana Nascimento,Talles Eduardo Maciel Ferreira,Laís Mayara Azevedo Barroso,Camila Ferreira Azevedo,Simone Eliza Faccione Guimarães,Nick V. L. Serão +8 more
TL;DR: Overall, the proposed two-step clustering method (ICAclust) performed well compared to K-means, a traditional method used for cluster analysis of temporal gene expression data.
Journal ArticleDOI
Bayesian Inference of Genetic Regulatory Networks from Time Series Microarray Data Using Dynamic Bayesian Networks
TL;DR: A dynamic Bayesian networks (DBNs) modeling and a full Bayesian learning scheme are proposed that directly models the continuous expression levels and also is associated with parameters that indicate the degree as well as the type of regulations.
Journal ArticleDOI
A parallel genetic algorithm for single class pattern classification and its application for gene expression profiling in Streptomyces coelicolor
Cuong To,Jiri Vohradsky +1 more
TL;DR: A single class classification method based on a parallel genetic algorithm which is a supervised computer learning method exploiting prior knowledge of gene function to identify unknown genes of similar function from expression data that avoids several problems associated with unsupervised clustering methods.
Journal ArticleDOI
Inferring differentiation pathways from gene expression
TL;DR: A novel model for gene expression profiles and an unsupervised learning method to estimate developmental similarity and infer differentiation pathways are proposed and demonstrated for the first time how the incorporation of structural knowledge about the dependence structure helps to reveal differentiation pathways and potentially relevant functional gene modules from microarray datasets.
Journal ArticleDOI
A hidden Markov model-based approach for identifying timing differences in gene expression under different experimental factors
Takashi Yoneya,Hiroshi Mamitsuka +1 more
TL;DR: The results of the experiment indicate that the method worked favourably to identify the timing ordering under different experimental factors, such as that gene expression under heat shock tended to start earlier than that under oxidative stress.
References
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