Interpreting multi-variate models with setPCA

TL;DR

This paper introduces setPCA, a method that enhances the interpretability of PCA in omics data by integrating background knowledge through a GUI that overlays known sets onto loadings plots.

Contribution

The paper presents a novel algorithm for integrating known sets with PCA loadings and provides a user-friendly GUI for improved interpretability of multi-variate models.

Findings

Algorithm effectively overlays known sets onto PCA plots.

GUI facilitates easier interpretation of multi-variate models.

Method is freely available for academic use.

Abstract

Principal Component Analysis (PCA) and other multi-variate models are often used in the analysis of "omics" data. These models contain much information which is currently neither easily accessible nor interpretable. Here we present an algorithmic method which has been developed to integrate this information with existing databases of background knowledge, stored in the form of known sets (for instance genesets or pathways). To make this accessible we have produced a Graphical User Interface (GUI) in Matlab which allows the overlay of known set information onto the loadings plot and thus improves the interpretability of the multi-variate model. For each known set the optimal convex hull, covering a subset of elements from the known set, is found through a search algorithm and displayed. In this paper we discuss two main topics; the details of the search algorithm for the optimal convex hull for this problem and the GUI interface which is freely available for download for academic use.