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
Bayesian Models for the Multi-sample Time-Course Microarray Experiments
TL;DR: The proposed procedure deals successfully with various technical difficulties which arise in microarray time-course experiments such as a small number of observations, non-uniform sampling intervals and presence of missing data or repeated measurements.
Proceedings ArticleDOI
An Integrated Time Series Gene Expression Data Analysis Pipeline with a Fuzzy Clustering method to assess Expression Patterns
TL;DR: An integrated time series gene expression analysis pipeline that detects differentially expressed genes, cluster co-expressed genes, unveil hidden gene expression patterns, identify over represented biological function categories and infer gene regulatory networks is developed.
Journal ArticleDOI
A Composite Mode Differential Gene Regulatory Architecture based on Temporal Expression Profiles
TL;DR: An algorithm called RIFT is proposed which helps to monitor the temporal differential regulatory pattern of a Differentially Expressed (DE) target gene either by a TF gene or a group of TF genes from a large time series (TS) data.
Book ChapterDOI
Evaluation of Time Series Microarray Data for Dynamic Gene Regulatory Network Inference
TL;DR: This work used dynamic artificial gene regulatory networks to evaluate the adequacy of time course microarray data to support the inference process and evaluated the effect of different ways that genes can be triggered on the performance of an inference algorithm.
Dissertation
Alignment of time course microarray data with hidden Markov models.
TL;DR: The aim of this project is to construct a methodology to align the data from different vineyards in order to obtain a common representation of the gene expression over the development cycle of the grape berries for each gene.
References
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Journal ArticleDOI
Cluster analysis and display of genome-wide expression patterns
TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
Journal ArticleDOI
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.
Todd R. Golub,Todd R. Golub,Donna K. Slonim,Pablo Tamayo,Christine Huard,Michelle Gaasenbeek,Jill P. Mesirov,Hilary A. Coller,Mignon L. Loh,James R. Downing,Michael A. Caligiuri,Clara D. Bloomfield,Eric S. Lander +12 more
TL;DR: A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case and suggests a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
Journal ArticleDOI
Comprehensive Identification of Cell Cycle–regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization
Paul T. Spellman,Gavin Sherlock,Gavin Sherlock,Michael Q. Zhang,Vishwanath R. Iyer,Kirk R. Anders,Michael B. Eisen,Patrick O. Brown,Patrick O. Brown,David Botstein,Bruce Futcher +10 more
TL;DR: A comprehensive catalog of yeast genes whose transcript levels vary periodically within the cell cycle is created, and it is found that the mRNA levels of more than half of these 800 genes respond to one or both of these cyclins.
Journal ArticleDOI
Genomic expression programs in the response of yeast cells to environmental changes.
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.