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10 ANALYTICAL AND LABORATORY EQUIPMENT


“In our studies, we are dealing with very large amounts of data, sometimes between 10 and 100 million data points, which we tend to view as graphics. With earlier applications, these graphics would take a long time to appear, but with the latest data analysis tools, these 3D images are presented instantly.”


Dr Ann-Sofie Albrekt, Lund University, Sweden


for the researchers themselves to examine this enormous quantity of data, to test different hypothesis, and to explore alternative scenarios within seconds, since important findings can now be displayed in an easy-to-interpret graphical form.


Data visualisation During the last decade, research into molecular biology has helped to identify a large number of disease- associated genes, and is therefore helping researchers to unpick the fundamental biology of major illnesses. Gene expression profiling, for example, is now regularly being used for the study of many serious diseases.


Gene expression experiments help to measure the activity (the expression) of tens of thousands of genes at once, in order to create a global picture of cellular function. Tese findings can then be used to distinguish between cells that are actively dividing, for example, or to show how the cells react to a particular treatment. As part of this process, researchers often must consider sub-groups (such as patients who are in remission versus patients who have suffered a relapse), whilst also examining the different types of cell abnormalities related to clinical conditions such as diabetes and cancer.


Difficulties can arise, however, as a result of the vast amount of data that is created by experiments like these. Tis ‘data overload’ can present a serious problem for researchers, since it is essential to capture, explore, and analyse this kind of data effectively in order to obtain meaningful results.


To address this issue, a new generation of data visualisation tools has been designed to take full advantage of the most powerful pattern recogniser that exists: the human brain. Indeed, powerful software engines are already being used to help researchers to visualise their data in 3D, so that they can identify hidden structures and patterns more easily, and therefore identify any interesting and/ or significant results easily, by


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themselves, without having to rely on specialist bioinformaticians and biostatisticians.


Identifying patterns Data visualisation works by projecting high dimensional data down to lower dimensions, which can then be plotted in 3D on a computer screen, and then rotated manually or automatically and examined by the naked eye. With the benefit of instant user feedback on all of these actions, scientists studying diseases like diabetes and leukaemia can now easily analyse their findings in real-time, directly on their computer screen, in an easy- to-interpret graphical form.


Scientists are already making use of this exciting new technology in a real-world setting. For example, a large EU-funded research project is attempting to develop and optimise in vitro test strategies that could reduce or replace animal testing for sensitisation studies.


Te project, known as Sens-it-iv, combines both private and public research institutions, as well as several industrial and societal interest organisations. Dr Ann-Sofie Albrekt is currently advanced data analysis software for her work in this important area, under the supervision of Professor Carl Borrebaeck, a sub-coordinator of Sens-it-iv.


“In our studies, we are dealing with very large amounts of data, sometimes between 10 and 100 million data points, which we tend to view as graphics. With earlier applications, these graphics would take a long time to appear, but with the latest data analysis tools, these 3D images are presented instantly,” Dr Albrekt says. “As a result, we can be much more creative with our theories, as we can easily test any number of hypotheses in rapid succession, and see the results at a glance.”


When used during research in this way, the ability to visualise data in 3D represents a very powerful tool


for scientists, since the human brain is very good at detecting structures and patterns. Te idea behind this approach is that highly complex data will be easier to understand and comprehend by giving it a graphic form. As such, this approach to information visualisation offers a way to transform raw data into a comprehensible graphical format, so that scientists can make decisions based on information that they can identify and understand easily.


New imaging functions contained within the latest data analysis applications are currently allowing scientists to analyse very large data sets by using a combination of different visualisation techniques, such as Heatmaps and Principal Component Analysis (PCA). With visualisation tools like these, it is possible to investigate large and complex data sets without being a statistics expert, since visualising information reduces the time required to take in data, make sense of it, and draw conclusions from it.


Te process begins by reducing high dimension data down to lower dimensions so that it can be plotted in 3D. Principal Component Analysis (PCA) is often used for this purpose, as it uses a mathematical procedure to transform a number of possibly correlated variables into a number of uncorrelated variables (called principal components).


Key breakthrough One of the key breakthroughs in the latest generation of bioinformatics software is the introduction of dynamic PCA, an innovative way of combining PCA analysis with immediate user interaction. Tis unique feature allows scientists to manipulate different PCA-plots – interactively and in real time – directly on the computer screen, and at the same time work with all annotations and other links in a fully integrated way. With this approach, researchers are given full freedom to explore all possible versions of the presented view, and are therefore able to visualise, analyse, and explore a large dataset easily.


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