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A key challenge in modern data analysis is understanding connections betweencomplex and differing modalities of data. For example, two of the mainapproaches to the study of breast cancer are histopathology (analyzing visualcharacteristics of tumors) and genetics. While histopathology is the goldstandard for diagnostics and there have been many recent breakthroughs ingenetics, there is little overlap between these two fields. We aim to bridgethis gap by developing methods based on Angle-based Joint and IndividualVariation Explained (AJIVE) to directly explore similarities and differencesbetween these two modalities. Our approach exploits Convolutional NeuralNetworks (CNNs) as a powerful, automatic method for image feature extraction toaddress some of the challenges presented by statistical analysis ofhistopathology image data. CNNs raise issues of interpretability that weaddress by developing novel methods to explore visual modes of variationcaptured by statistical algorithms (e.g. PCA or AJIVE) applied to CNN features.Our results provide many interpretable connections and contrasts betweenhistopathology and genetics.

Joint and individual analysis of breast cancer histologic images and genomic covariates