Interpreting multi-variate models with setPCA

• Main Authors: Aouni, Nordine, Linders, Luc, Robinson, David, Vandelaer, Len, Wiezorek, Jessica, Gupta, Geetesh, Cavill, Rachel
• Format: Journal Article
• Language: English
• Published: 17-11-2021
• Subjects: Computer Science - Learning; Quantitative Biology - Genomics
• DOI: 10.48550/arxiv.2111.09138

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.