The analysis of time series expression data is widely employed for investigating biological mechanisms. Microarrays are often used to generate time series for several different experimental conditions. These time series then need to be compared to each other. For a successful comparison it is necessary to perform a time series alignment because the experiments can differ in the number of time points, as well as in the time points themselves. In this work we propose a novel visual analytics approach for the analysis of multiple time series experiments in parallel. Our time series alignment analysis tool TIALA allows one to align multiple time series experiments and to visually explore the aligned expression profiles. A two- and three-dimensional visualization strategy was implemented that is especially designed to enhance the display of multiple aligned time series expression profiles. TIALA is available as a part of the microarray data analysis software Mayday. Mayday itself is open source software distributed under the terms of the GNU General Public License. It is available from http://www.microarray-analysis.org.
We apply our approach to time series showing abiotic stress responses of Arabidopsis thaliana and to data sets from two replicates of the antibiotics producing bacterium Streptomyces coelicolor.