Analysis of time-course microarray data: Comparison of common tools

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.

Olfactory receptors contribute to progression of kidney fibrosis

“…1b ). The DEGs were determined using the linear models for microarray data (LIMMA) 30 Package of R software 31 , which according to our recent study 32 is the most reliable tool for time-course analysis of microarray data. A Comparison of the UUO and sham samples determined 2583 DEGs with an adjusted p value < 0.05.…”

“…Unfortunately, due to cost and difficulty, such experiments are less considered by investigators and most of the gene expression studies are performed statically 50 . On the other hand, appropriate analysis of time-series data is of great importance and should be performed by suitable mathematical approaches 32 . Although both studies by Wu et al were designed temporally, single time-point analysis prevented them from finding significant changes in the expression of OR genes over the course of the disease.…”

Systems and classical biology approaches unraveled role of olfactory receptors in progression of kidney fibrosis

“…This result was also validated by the hierarchical clustering (Figure 1b). The DEGs were determined using the LIMMA Package of R software, which according to our recent study, is the most reliable tool for the time-course analysis of microarray data (Moradzadeh, Moein, Nickaeen, & Gheisari, 2019). A Comparison of the UUO and sham samples determined 2583 DEGs with an adjusted p-value < 0 .05.…”

Big data to knowledge: common pitfalls in transcriptomics data analysis and representation

“…For instance, the time points were compared one by one instead of inspecting the trajectories as a whole. Indeed, in spite of the availability of specialized tools for time-series big data analysis [6,7], such techniques were employed only in 3 studies. In addition, we explored the distribution of studies based on their model organism.…”