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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Proceedings ArticleDOI
Discovering Gene Clusters via Integrated Analysis on Time-Series and Group-Comparative Microarray Datasets
TL;DR: The proposed gene clustering method, TGmix, was applied to microarray datasets for rat's wound healing experiment, and the genes discovered in the same cluster conform to the analysis goal with related biological functions.
Journal ArticleDOI
Time‐series gene expression patterns and their characteristics of Beauveria bassiana in the process of infecting pest insects
Guochao Zhang,Jifeng Zhang,Zhuo Yao,Yong Shi,Chenxi Xu,Lvyi Shao,Lepu Jiang,Maoye Li,Yue Tong,Yujun Wang +9 more
TL;DR: A novel pattern analysis (PA) method for analyzing time‐series data and applied to a transcriptomic data set of B. bassiana infecting Galleria mellonella had three novel discoveries, including overall downregulation of gene expression, the more critical first 24 h, and enrichment of regulatory functions of downregulated genes.
Book ChapterDOI
SlopeMiner: An Improved Method for Mining Subtle Signals in Time Course Microarray Data
TL;DR: The algorithm calculates the slope for the slow transition between the expression levels of data, matching the sequence of expression level for each gene against temporal patterns having one transition between two expression levels.
Dissertation
Automated microscopy and high throughput image analysis in Arabidopsis and Drosophila
TL;DR: (Automated Microscopy and High Throughput Image Analysis in Arabidopsis and Drosophila.
Machine learning methods for microarray data analysis
TL;DR: This dissertation has been submitted in partial fulfillment of requirements for an advanced degree at the University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library.
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.