Qlucore Projection Score indicates the usefulness of a PCA representation by following the evolution of the projection score in real time during variance filtering
The Qlucore Projection Score allows detailed comparison of representations obtained by Principal Component Analysis (PCA) corresponding to different variable subsets, such as those obtained by variance filtering of a large data set.
By following the evolution of the projection score in real time during variance filtering, the user can easily find the variable subset giving the most informative representation.
In Qlucore Omics Explorer, the projection score is coloured according to the displayed value. Red indicates a low projection score, yellow indicates a medium-high score and green corresponds to a high projection score.
In practice, almost all real data sets contain some non-random structure, and therefore it is very uncommon to get a projection score close to zero.
The colours, and thus the boundaries between what is considered to be a "good" or a "bad" projection score, are based on Qlucore's experience applying the projection score to many different data sets, and should be interpreted mainly as rough guidelines suggesting the quality of the representations.
The projection score is a widely versatile technique that is applicable for a broad family of different statistical analyses. The statistical and technical details have been published by Magnus Fontes and Charlotte Soneson in the prestigous scientific journal BMC Bioinformatics in 2011.