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
Improved inference of gene regulatory networks through integrated Bayesian clustering and dynamic modeling of time-course expression data.
TL;DR: This paper presents an algorithm that uses integrated, probabilistic clustering to ease the problems of under-determination and correlated variables within a fully Bayesian framework and fits using variational Bayesian methods.
Proceedings ArticleDOI
NATSA: A Near-Data Processing Accelerator for Time Series Analysis
Iván López Fernández,Ricardo Quislant,Eladio Gutierrez,Oscar Plata,Christina Giannoula,Mohammed Alser,Juan Gómez-Luna,Onur Mutlu +7 more
TL;DR: NATSA as mentioned in this paper is the first near-data processing accelerator for time series analysis, which exploits modern 3D-stacked High Bandwidth Memory (HBM) to enable efficient and fast specialized matrix profile computation near memory.
Journal ArticleDOI
Application of multivariate curve resolution to the analysis of yeast genome-wide screens
TL;DR: Results obtained by application of Multivariate Curve Resolution allowed a good recovery of the evolving gene expression profiles and the identification of metabolic pathways and individual genes involved in these gene expression changes.
Journal ArticleDOI
Inferring the perturbation time from biological time course data
TL;DR: In this article, a Bayesian method was proposed to infer the perturbation time given time course data from a wild-type and perturbed system using a nonparametric approach based on Gaussian Process Regression.
Journal ArticleDOI
CbGRiTS: cerebellar gene regulation in time and space.
Thomas J. Ha,Douglas J. Swanson,Matt Larouche,Randy Glenn,Dave Weeden,Peter G. Zhang,Kristin M. Hamre,Michael A. Langston,Charles A. Phillips,Mingzhou Song,Zhengyu Ouyang,Elissa J. Chesler,Suman Duvvurru,Roumyana Yordanova,Yan Cui,Kate Campbell,Greg Ricker,Carey R. Phillips,Ramin Homayouni,Dan Goldowitz +19 more
TL;DR: A new transcriptome database called, Cerebellar Gene Regulation in Time and Space (CbGRiTS), populated with transcriptome data across embryonic and postnatal development from two standard mouse strains, C57BL/6J and DBA/2J, several recombinant inbred lines and cerebellar mutant strains is introduced.
References
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