Integration of spatial distribution in imaging-genomics

Vaishnavi Subramanian, Weizhao Tang, Benjamin Chidester, Jian Ma, Minh N. Do

Abstract:

To better understand diseases such as cancer, it is crucial for computational inference to quantify the spatial distribution of various cell types within a tumor. To this end, we used Ripley’s K-statistic, which captures the spatial distribution patterns at different scales of both individual point sets and interactions between multiple point sets. We propose to improve the expressivity of histopathology image features by incorporating this descriptor to capture potential cellular interactions, especially interactions between lymphocytes and epithelial cells. We demonstrate the utility of the Ripley’s K-statistic by analyzing digital slides from 710 TCGA breast invasive carcinoma (BRCA) patients. In
particular, we consider its use in the context of imaging-genetics to understand correlations between gene expression and image features using canonical correlation analysis (CCA). Our analysis shows that including these spatial features leads to more significant associations between
image features and gene expression.