Multi-modality fusion using canonical correlation analysis methods: Application in breast cancer survival prediction from histology and genomics

TL;DR

This paper explores advanced multi-modality data fusion techniques using canonical correlation analysis and its penalized variants, demonstrating improved breast cancer survival prediction from histology and genomics data.

Contribution

It introduces novel penalized CCA methods with deflation schemes for high-dimensional data fusion and applies them to improve cancer survival prediction.

Findings

Penalized CCA outperforms traditional CCA in high-dimensional settings.

The proposed model significantly reduces prediction error on simulated data.

In real data, the model surpasses PCA in survival prediction accuracy.

Abstract

The availability of multi-modality datasets provides a unique opportunity to characterize the same object of interest using multiple viewpoints more comprehensively. In this work, we investigate the use of canonical correlation analysis (CCA) and penalized variants of CCA (pCCA) for the fusion of two modalities. We study a simple graphical model for the generation of two-modality data. We analytically show that, with known model parameters, posterior mean estimators that jointly use both modalities outperform arbitrary linear mixing of single modality posterior estimators in latent variable prediction. Penalized extensions of CCA (pCCA) that incorporate domain knowledge can discover correlations with high-dimensional, low-sample data, whereas traditional CCA is inapplicable. To facilitate the generation of multi-dimensional embeddings with pCCA, we propose two matrix deflation schemes that enforce desirable properties exhibited by CCA. We propose a two-stage prediction pipeline using pCCA embeddings generated with deflation for latent variable prediction by combining all the above. On simulated data, our proposed model drastically reduces the mean-squared error in latent variable prediction. When applied to publicly available histopathology data and RNA-sequencing data from The Cancer Genome Atlas (TCGA) breast cancer patients, our model can outperform principal components analysis (PCA) embeddings of the same dimension in survival prediction.