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Integration of Spatial Distribution in Imaging-Genetics

  • Vaishnavi SubramanianEmail author
  • Weizhao Tang
  • Benjamin Chidester
  • Jian Ma
  • Minh N. Do
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

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.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Vaishnavi Subramanian
    • 1
    Email author
  • Weizhao Tang
    • 1
  • Benjamin Chidester
    • 2
  • Jian Ma
    • 2
  • Minh N. Do
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  2. 2.School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA

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