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Analysis of Time-Course Microarray Data: Comparison of Common Tools Publisher Pubmed



Moradzadeh K1, 2 ; Moein S1 ; Nickaeen N3 ; Gheisari Y1, 4
Authors
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Authors Affiliations
  1. 1. Department of Genetics and Molecular Biology, Isfahan University of Medical Sciences, Isfahan, Iran
  2. 2. Student Research Committee, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
  3. 3. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
  4. 4. Regenerative Medicine Lab, Isfahan Kidney Diseases Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

Source: Genomics Published:2019


Abstract

High-throughput time-series data have a special value for studying the dynamism of biological systems. However, the interpretation of such complex data can be challenging. The aim of this study was to compare common algorithms recently developed for the detection of differentially expressed genes in time-course microarray data. Using different measures such as sensitivity, specificity, predictive values, and related signaling pathways, we found that limma, timecourse, and gprege have reasonably good performance for the analysis of datasets in which only test group is followed over time. However, limma has the additional advantage of being able to report significance cut off, making it a more practical tool. In addition, limma and TTCA can be satisfactorily used for datasets with time-series data for all experimental groups. These findings may assist investigators to select appropriate tools for the detection of differentially expressed genes as an initial step in the interpretation of time-course big data. © 2018
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