Qlucore Projection Score aids better visualization of large data sets

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 visualizations of large amounts of data, of whether the patterns they are seeing are statistically valid, or random. Qlucore Projection Score is a unique functionality within Qlucore Omics Explorer that provides scientists and researchers with information on how accurately the visual representation is actually portraying data. The 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 visualization 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.