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Qlucore Omics Explorer enables scientists to analyse very large data sets using combination of statistical methods and visualization techniques. This approach allows researchers to study the data and look for patterns and structures, without having to be a statistics or computer expert.

With instant user feedback on all actions, scientists can use Omics Explorer to analyze their findings in real-time, directly on their computer screen, which means that multiple datasets can be analyszd very quickly. In addition, the program seamlessly links to Gene Expression Omnibus (GEO) data. This allows scientists to benefit from already published material and to compare and enhance their own results.

The software works by projecting high-dimensional data down to lower dimensions, which can then be plotted in 3D on a computer screen, rotated manually or automatically, and examined by the naked eye. The use of different colors makes the analysis even easier. Typical sources of data are experiments related to gene expression, microRNA, DNA methylation, proteomics, or other sources for biomarker discovery.

One of the key methods used to visualize data is dynamic PCA, which combines traditional PCA analysis with immediate user interaction. Qlucore’s software updates the plots interactively and in real time, and works with all annotations and other links in a fully integrated way.

Researchers conducting studies typically begin their analysis by opening up a dataset with Omics Explorer, and then look at the inherent structures that form, without performing any filtering. Any outliers or natural groups that may form can be seen.

At this stage, if the data is "noisy", scientists can filter by variance in order to reduce the noise levels. Usually, if natural classes were seen during their initial inspection, they will begin to form even tighter clusters when variables with no or very little variation are removed.

Most researchers then will then perform a comparison (such as a t- or F-test based on ANOVA) to identify variables that can explain the different classes. Once that process has been completed, the researchers can export a variable (gene) list and plots. It is also possible to continue to study the subgroup of variables further to examine what other variables are correlated with the ones that have been identified thus far, and how these variables are expressed in different groups.

For example, if a biologist wants to investigate similarities between healthy samples compared with patient samples, it is very useful to begin with the healthy samples, look at the structure forming in the principle component analysis, lock the principal components, and then to add the patient sample group.

Because the structure will change, many applications will completely re-draw the findings. Qlucore Omics Explorer gives scientists the option of locking the principle components, which makes the data much easier to interpret.

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