Each PCA interpretation analysis produces a set of high and low principal component extreme genes (PCEGs). One such analysis can be compared to another by forming a PCEG confusion matrix showing the correspondence of every principal component from one data set (e.g. the original data) vs. every principal component of a noise added dataset. Each cell of the confusion matrix represents the fraction of genes that intersect between the corresponding PCEG sets (the count of intersecting PCEG genes divided by the count union of PCEG genes). A single "PCEG overlap" score is calculated as the average of all row maximums and all column maximums. If two analysis correspond well the PCEG confusion matrix will have a strong diagonal component with diagonal cell values near 1, and thus will get a PCEG overlap score near 1. Scores greater than zero indicate that there is partial overlap, possibly off-diagonal correspondence, between genes in respective PCEG sets. We observe that some fraction of the principal component gene sets are recovered in spite of additional noise or in spite of selecting different column subsets.

The following directory contains one PCEG comparison confusion matrix per experiment run. The figures themselves do not have titles, but their directory name and file name indicate the experiment settings used to generate each dataset. E.g.:

noise-added-0.01/noise-0.050-run-3-intersect.png

indicates this is a noise added experiment at

balanced-subsets-0.001/subset-15-run-5-intersect.png

indicates this is a class-balanced column subset experiment at