Abstract
From time course gene expression data, we may identify genes that modulate in a certain pattern across time. Such patterns are advantageous to investigate the transcriptomic response to a certain condition. Especially, it is of interest to compare two or more conditions to detect gene expression patterns that significantly differ between them. Time course analysis can become difficult using traditional differentially expressed gene (DEG) analysis methods since they are based on pair-wise sample comparison instead of a series of time points. Most importantly, the related tools are mostly available as local Software, requiring technical expertise. Here, we present TimesVector-web, which is an easy to use web service for analysing time course gene expression data with multiple conditions. The web-service was developed to (1) alleviate the burden for analyzing multi-class time course data and (2) provide downstream analysis on the results for biological interpretation including TF, miRNA target, gene ontology and pathway analysis. TimesVector-web was validated using three case studies that use both microarray and RNA-seq time course data and showed that the results captured important biological findings from the original studies.
Highlights
Times-course analysis of gene expression data can be advantageous for revealing modulating gene expression patterns of certain biological mechanism across time
If time course data is generated for multiple conditions, finding gene expression patterns that significantly differ between the conditions are the main interest of the analysis [4,5,6]
In case it is difficult to find a good K, we provide a K-test function in means to help users to select a good K, which will be discussed in section “Finding an optimal K for clustering” (Section 2.3) in more detail
Summary
Times-course analysis of gene expression data can be advantageous for revealing modulating gene expression patterns of certain biological mechanism across time. It is a common practice to search for significantly differentially expressed genes (DEGs) between two conditions, for example, a cohort of normal versus cancer patients. Such analysis is tailored to observe the transcriptomic difference within a single snapshot of the current gene expression status in a pairwise manner. When adopting the traditional DEG analysis, we are opted to compare a combination of time point pairs Such approach requires post analysis of the result, since the results of each DEG pair itself is not sufficient for interpretation. If time course data is generated for multiple conditions, finding gene expression patterns that significantly differ (or similar) between the conditions are the main interest of the analysis [4,5,6]
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