  Multiple data views measured on the same set of participants is becoming more
common and has the potential to deepen our understanding of many complex
diseases by analyzing these different views simultaneously. Equally important,
many of these complex diseases show evidence of subgroup heterogeneity (e.g.,
by sex or race). HIP (Heterogeneity in Integration and Prediction) is among the
first methods proposed to integrate multiple data views while also accounting
for subgroup heterogeneity to identify common and subgroup-specific markers of
a particular disease. However, HIP is applicable to continuous outcomes and
requires programming expertise by the user. Here we propose extensions to HIP
that accommodate multi-class, Poisson, and Zero-Inflated Poisson outcomes while
retaining the benefits of HIP. Additionally, we introduce an R Shiny
application, accessible on shinyapps.io at
https://multi-viewlearn.shinyapps.io/HIP_ShinyApp/, that provides an interface
with the Python implementation of HIP to allow more researchers to use the
method anywhere and on any device. We applied HIP to identify genes and
proteins common and specific to males and females that are associated with
exacerbation frequency. Although some of the identified genes and proteins show
evidence of a relationship with chronic obstructive pulmonary disease (COPD) in
existing literature, others may be candidates for future research investigating
their relationship with COPD. We demonstrate the use of the Shiny application
with a publicly available data. An R-package for HIP would be made available at
https://github.com/lasandrall/HIP.
