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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Book ChapterDOI
Curve fitting for short time series data from high throughput experiments with correction for biological variation
TL;DR: This paper proposes a regression model that can estimate corresponding parameters that can be used to correct measurement errors or deviations caused by biological variation in terms of a time shift, different reaction speed and different reaction intensity for replicates.
Proceedings ArticleDOI
Missing Value Estimation for Time Series Microarray Data Using Linear Dynamical Systems Modeling
C. Phong,Rahul Singh +1 more
TL;DR: This work proposes modeling gene expression profiles as simple linear and Gaussian dynamical systems and applies the Kalman filter to estimate missing values and demonstrates the efficiency of this approach by evaluating its performance in estimating artificially introduced missing values in two different time series data sets.
Dissertation
Time-Series Transcriptomic Analysis of a Systematically Perturbed Arabidopsis thaliana Liquid Culture System: A Systems Biology Perspective
TL;DR: A statistical analysis strategy is developed that enables at each time point of a timeseries the identification of genes whose expression changes in statistically significant amount due to the applied stress in Arabidopsis thaliana liquid culture system.
Journal ArticleDOI
Learning gene regulatory networks from gene expression data using weighted consensus
TL;DR: A linear programming-based consensus method for the inference of gene regulatory networks that assigns a weight to each method based on its credibility, suggesting that assigning weights to different individual methods rather than giving them equal weights improves the accuracy.
Dissertation
Analyse de processus stochastiques pour la génomique : étude du modèle MTD et inférence de réseaux bayésiens dynamiques
TL;DR: In this article, the authors present two approaches for the reconstruction of reseaux genetiques using reseaux bayesiens dynamiques (DBN) in order to estimate the topology of genes.
References
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Audrey P. Gasch,Paul T. Spellman,Camilla M. Kao,Orna Carmel-Harel,Michael B. Eisen,Gisela Storz,David Botstein,Patrick O. Brown +7 more
TL;DR: Analysis of genomic expression patterns in the yeast Saccharomyces cerevisiae implicated the transcription factors Yap1p, as well as Msn2p and Msn4p, in mediating specific features of the transcriptional response, while the identification of novel sequence elements provided clues to novel regulators.
Journal ArticleDOI
Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays.
Uri Alon,Naama Barkai,Daniel A. Notterman,Kurt C. Gish,S. Ybarra,David H. Mack,A. J. Levine,A. J. Levine +7 more
TL;DR: In this paper, a two-way clustering algorithm was applied to both the genes and the tissues, revealing broad coherent patterns that suggest a high degree of organization underlying gene expression in these tissues.