12 ANALYTICAL AND LABORATORY EQUIPMENT
potential of the important research being conducted in this area.
Qlucore started as a collaborative research project at Lund University, Sweden, supported by researchers at
Below Gene expression experiments help to measure the activity (the expression) of tens of thousands of genes at once.
the Departments of Mathematics and Clinical Genetics, in order to address the vast amount of high-dimensional data generated with microarray gene expression analysis. As a result, it was recognised that an interactive
scientific software tool was needed to conceptualise the ideas evolving from the research collaboration.
Te basic concept behind the software is to provide a tool that
can take full advantage of the most powerful pattern recogniser that exists - the human brain. Te result is a core software engine that visualises the data in 3D and will aid the user in identifying hidden structures and patterns. Over the past two years the major efforts have been to optimise the early ideas and to develop a core software engine that is extremely fast, allowing the user to interactively and in real time instantly explore and analyse high-dimensional data sets with the use of a normal PC.
Qlucore was founded in early 2007 and the first product released was the Qlucore Gene Expression Explorer 1.0. Te latest version of this software, Version1.1, represents a major step forward with the advanced statistics support. All user action is at most two mouse clicks away. Te company’s early customers are mainly from the Life-science and Biotech industries, but solutions for other industries are currently under development.
One of the key methods used by Qlucore Gene Expression Explorer to visualise data is dynamic principal component analysis (PCA), an innovative way of combining PCA analysis with immediate user interaction.
Dynamic PCA is PCA analysis combined with instant user response, a combination which provides an optimal way for users to visualise and analyse a large dataset by presenting a comprehensive view of the data set at the same time, since the user is given full freedom to explore all possible versions of the presented view.
PCA analysis works by projecting high dimensional data down to lower dimensions. Te specific projections of the high-dimensional data are chosen in order to maintain as much variance as possible in the projected data set. With Qlucore Gene Expression Explorer, data is projected and plotted on the two dimensional computer screen and then rotated manually or automatically and examined by the naked eye.
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