Qlucore announces aid to better visualisations of large data sets

New traffic-light Qlucore Projection Score indicates the usefulness of a Principal Component Analysis (PCA) representation

Historically, scientists and researchers have been faced with a problem when looking at visualisations of large amounts of data, of whether the patterns they are seeing are statistically valid, or random. Qlucore Projection Score is a unique functionality that will be available in the new version of Qlucore Omics Explorer 3.0. Projection Score will provide the user with information on how accurately the visual representation is actually portraying data.

The patent-pending Qlucore Projection Score technique is the brain child of Qlucore co-founder Magnus Fontes. It allows detailed comparison of representations obtained by PCA corresponding to different variable subsets, e.g., those obtained by variance filtering of a large data set. The goal of exploratory visualisation is to find a representation from which interpretable and potentially interesting information can be extracted, that is, one that contains structures and patterns that are likely to be non-random. By following the evolution of the projection score in real time during variance filtering, the user can easily find the variable subset (and thus implicitly the variance cut-off) giving the most informative representation.

Magnus Fontes, the co-founder of Qlucore and developer of the Projection Score concept comments:

“Qlucore is proud to be at the forefront of visualisation technology for scientific research. The Projection Score technique is one which I have been working on for a considerable time and it will be very valuable in aiding research scientists to validate their data visualisation work. The technique has been welcomed by my peers and I am delighted that it is now available on a commercial basis.”