MATSE: Information Visualisation of Microarray Time-course Data

Project Description

Microarray Time-series Explorer (MaTSE) is a software application developed to improve the analysis of microarray data by allowing the user to explore their data using a unique visual interface.  Throughout the project we focused on engaging potential end-users in academia and industry toward developing functionality relevant to their needs and producing software that is of a standard that would attract the interest of potential investors or licensors.  In conjunction with this we worked toward amassing commercial know-how across the project team and building a commercialisation plan that can be used to exploit the software and further guide development.


Consultation with our collaborating biologists has allowed us to define three main unique selling points for the MaTSE software. These are: The ability to easily discover patterns in data not possible with other visualisation tools: The primary USP of MaTSE is that it allows users to explore their data in a way that allows them to find patterns of correlated gene activity occur over shorter intervals of time in the data. These patterns often relate to biological phenomena of genuine interest to biologists and they cannot be found using more traditional analysis techniques. MaTSE also allows users to find more dominant trends in recorded gene activity and allows users to cross reference their findings with stored gene groupings. Rapid feedback and easy interpretation of results: The MaTSE software displays microarray data without any type of complex algorithmic preprocessing such as that applied by traditional analysis techniques such as clustering, principle component analysis or self organising maps. While traditional techniques attempt to summarise the data by displaying an abstraction of that data, the only processing of the data involved in MaTSE is the calculation of straight-forward mean and change values in the scatter-plot. This makes the display closer to the actual recorded data and allows users to gain a better understanding of their data during analysis without having to take time to comprehend algorithms which might have applied to the data.The ability to store and recall patterns: The unique scatter-plot layout in MaTSE is such that user selections are measurable (based on activity, change in activity and mean activity) and are meaningful when recalled. These selections, and combinations of selections, are recorded so they can be stored, annotated and shared with other users.