Comparison of Commonly Used Methods for Testing Interaction Effect in Time-Course Microarray Experiments

Tran, Nhu Quynh; Koçak, Mehmet; Mendeş, Mehmet

doi:10.5336/biostatic.2016-53627

Cited by 2 publications

(1 citation statement)

References 19 publications

(37 reference statements)

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“…More advanced techniques for identifying differential profiles are commonly used in time course gene expression studies. For instance, maSigPro uses a two stage method to filter genes based on their expression profile [ 14 ], BATS uses a Bayesian approach to rank genes of interest [ 15 ], which can offer increased accuracy for time courses with more data points [ 16 ], and the EDGE software can identify time dependent changes [ 17 ]. While these methods can rapidly identify differences between experimental groups their univariate nature provides a specific, rather than comprehensive, view of the data [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

MetaboClust: Using interactive time-series cluster analysis to relate metabolomic data with perturbed pathways

et al. 2018

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MotivationModern analytical techniques such as LC-MS, GC-MS and NMR are increasingly being used to study the underlying dynamics of biological systems by tracking changes in metabolite levels over time. Such techniques are capable of providing information on large numbers of metabolites simultaneously, a feature that is exploited in non-targeted studies. However, since the dynamics of specific metabolites are unlikely to be known a priori this presents an initial subjective challenge as to where the focus of the investigation should be. Whilst a number of feed-forward software tools are available for manipulation of metabolomic data, no tool centralizes on clustering and focus is typically directed by a workflow that is chosen in advance.ResultsWe present an interactive approach to time-course analyses and a complementary implementation in a software package, MetaboClust. This is presented through the analysis of two LC-MS time-course case studies on plants (Medicago truncatula and Alopecurus myosuroides). We demonstrate a dynamic, user-centric workflow to clustering with intrinsic visual feedback at all stages of analysis. The software is used to apply data correction, generate the time-profiles, perform exploratory statistical analysis and assign tentative metabolite identifications. Clustering is used to group metabolites in an unbiased manner, allowing pathway analysis to score metabolic pathways, based on their overlap with clusters showing interesting trends.

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Section: Introductionmentioning

confidence: 99%

MetaboClust: Using interactive time-series cluster analysis to relate metabolomic data with perturbed pathways

et al. 2018

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Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles

et al. 2021

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Background Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient’s response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary. Results We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS. Conclusions The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases.

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Comparison of Commonly Used Methods for Testing Interaction Effect in Time-Course Microarray Experiments

Cited by 2 publications

References 19 publications

MetaboClust: Using interactive time-series cluster analysis to relate metabolomic data with perturbed pathways

MetaboClust: Using interactive time-series cluster analysis to relate metabolomic data with perturbed pathways

Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles

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