TTCA: an R package for the identification of differentially expressed genes in time course microarray data

Albrecht, Marco; Stichel, Damian; Müller, Bettina; Merkle, Ruth; Sticht, Carsten; Gretz, Norbert; Klingmüller, Ursula; Breuhahn, Kai; Matthäus, Franziska

doi:10.1186/s12859-016-1440-8

Cited by 19 publications

(14 citation statements)

References 53 publications

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“…The first example of this application is the comparison of non-small lung cancer cell (NSCLC) line H1975, with and without EGF treatment [ 33 ]. Gene expression matrices were divided into two groups (with and without EGF treatment).…”

Section: Resultsmentioning

confidence: 99%

Tensor decomposition-based unsupervised feature extraction applied to matrix products for multi-view data processing

Taguchi

2017

PLoS ONE

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In the current era of big data, the amount of data available is continuously increasing. Both the number and types of samples, or features, are on the rise. The mixing of distinct features often makes interpretation more difficult. However, separate analysis of individual types requires subsequent integration. A tensor is a useful framework to deal with distinct types of features in an integrated manner without mixing them. On the other hand, tensor data is not easy to obtain since it requires the measurements of huge numbers of combinations of distinct features; if there are m kinds of features, each of which has N dimensions, the number of measurements needed are as many as Nm, which is often too large to measure. In this paper, I propose a new method where a tensor is generated from individual features without combinatorial measurements, and the generated tensor was decomposed back to matrices, by which unsupervised feature extraction was performed. In order to demonstrate the usefulness of the proposed strategy, it was applied to synthetic data, as well as three omics datasets. It outperformed other matrix-based methodologies.

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Section: Resultsmentioning

confidence: 99%

Tensor decomposition-based unsupervised feature extraction applied to matrix products for multi-view data processing

Taguchi

2017

PLoS ONE

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“…To obtain insight into transcriptome dynamics, we identified genes that were differentially expressed over time. To this end, we used Transcript Time Course Analysis (TTCA) 27 which is designed to analyze time-series microarray data from perturbation experiments to discriminate the early and late changes in gene expression. Specifically, TTCA is intended for experimental designs with sparse and irregularly sampled time course gene expression data sets 27 .…”

Section: Resultsmentioning

confidence: 99%

“…To this end, we used Transcript Time Course Analysis (TTCA) 27 which is designed to analyze time-series microarray data from perturbation experiments to discriminate the early and late changes in gene expression. Specifically, TTCA is intended for experimental designs with sparse and irregularly sampled time course gene expression data sets 27 . TTCA calculates the integral scores quantifying the absolute expression changes in different time intervals, considering the inherent ordering and spacing information provided by the time points.…”

Section: Resultsmentioning

confidence: 99%

Heat-responsive and time-resolved transcriptome and metabolome analyses of Escherichia coli uncover thermo-tolerant mechanisms

Kim

Suh

et al. 2020

Sci Rep

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Current understanding of heat shock response has been complicated by the fact that heat stress is inevitably accompanied by changes in specific growth rates and growth stages. In this study, a chemostat culture was successfully performed to avoid the physico-chemical and biological changes that accompany heatshock, which provided a unique opportunity to investigate the full range of cellular responses to thermal stress, ranging from temporary adjustment to phenotypic adaptation at multi-omics levels. Heat-responsive and time-resolved changes in the transcriptome and metabolome of a widely used E. coli strain BL21(DE3) were explored in which the temperature was upshifted from 37 to 42 °C. Omics profiles were categorized into early (2 and 10 min), middle (0.5, 1, and 2 h), and late (4, 8, and 40 h) stages of heat stress, each of which reflected the initiation, adaptation, and phenotypic plasticity steps of the stress response. The continued heat stress modulated global gene expression by controlling the expression levels of sigma factors in different time frames, including unexpected downregulation of the second heatshock sigma factor gene (rpoE) upon the heat stress. Trehalose, cadaverine, and enterobactin showed increased production to deal with the heat-induced oxidative stress. Genes highly expressed at the late stage were experimentally validated to provide thermotolerance. Intriguingly, a cryptic capsular gene cluster showed considerably high expression level only at the late stage, and its expression was essential for cell growth at high temperature. Granule-forming and elongated cells were observed at the late stage, which was morphological plasticity occurred as a result of acclimation to the continued heat stress. Whole process of thermal adaptation along with the genetic and metabolic changes at fine temporal resolution will contribute to far-reaching comprehension of the heat shock response. Further, the identified thermotolerant genes will be useful to rationally engineer thermotolerant microorganisms.

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“…Examples include Significance Analysis of Microarray [4], Extraction of Differential Gene Expression (EDGE) [1, 5], Linear Models for Microarray Data (limma) [6], and Microarray Significant profiles [7]. EDGE uses a spline approach and is one of the first methods to specifically address identification of differentially expressed genes across time [8]. In contrast, the limma method has a more general purpose and is easily understood and implemented [7]; therefore, it has gained extreme popularity and become the gold standard to detect differentially expressed genes under different scenarios (e.g., two-group or multiple-group comparison) for microarray data.…”

Section: Introductionmentioning

confidence: 99%

“…Although the PGEE method and the glmmLasso method can carry out feature selection for longitudinal expression data and also eliminate or alleviate the inefficiency caused by separate analysis at each time point, these methods cannot handle extremely large numbers of genes [2, 14], which are often encountered in longitudinal gene expression profiles. For a selective review of methods capable of carrying out feature selection for longitudinal omics data, see Albrecht et al [8].…”

Section: Introductionmentioning

confidence: 99%

Feature Selection for Longitudinal Data by Using Sign Averages to Summarize Gene Expression Values over Time

Tian

Wang

2019

BioMed Research International

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With the rapid evolution of high-throughput technologies, time series/longitudinal high-throughput experiments have become possible and affordable. However, the development of statistical methods dealing with gene expression profiles across time points has not kept up with the explosion of such data. The feature selection process is of critical importance for longitudinal microarray data. In this study, we proposed aggregating a gene’s expression values across time into a single value using the sign average method, thereby degrading a longitudinal feature selection process into a classic one. Regularized logistic regression models with pseudogenes (i.e., the sign average of genes across time as predictors) were then optimized by either the coordinate descent method or the threshold gradient descent regularization method. By applying the proposed methods to simulated data and a traumatic injury dataset, we have demonstrated that the proposed methods, especially for the combination of sign average and threshold gradient descent regularization, outperform other competitive algorithms. To conclude, the proposed methods are highly recommended for studies with the objective of carrying out feature selection for longitudinal gene expression data.

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TTCA: an R package for the identification of differentially expressed genes in time course microarray data

Cited by 19 publications

References 53 publications

Tensor decomposition-based unsupervised feature extraction applied to matrix products for multi-view data processing

Tensor decomposition-based unsupervised feature extraction applied to matrix products for multi-view data processing

Heat-responsive and time-resolved transcriptome and metabolome analyses of Escherichia coli uncover thermo-tolerant mechanisms

Feature Selection for Longitudinal Data by Using Sign Averages to Summarize Gene Expression Values over Time

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