# Extensions of Heterogeneity in Integration and Prediction (HIP) with R Shiny Application

arXiv: https://arxiv.org/abs/2310.08426 · HTML: https://mlsanity.com/paper/2310.08426/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 50 references that are in the map.
- First posted on arXiv 12 Oct 2023.

## Builds on (most cited first, 6 of 6)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Sparse Linear Discriminant Analysis for Multi-view Structured Data](https://mlsanity.com/paper/1911.05643/) (2019)
- [Sparse Generalized Eigenvalue Problem with Application to Canonical Correlation Analysis for Integrative Analysis of Methylation and Gene Expression Data](https://mlsanity.com/paper/1611.01066/) (2016)
- [Bayesian Integrative Analysis and Prediction with Application to Atherosclerosis Cardiovascular Disease](https://mlsanity.com/paper/2005.11586/) (2020)
- [Are Latent Factor Regression and Sparse Regression Adequate?](https://mlsanity.com/paper/2203.01219/) (2022)
- [Accounting for data heterogeneity in integrative analysis and prediction methods: An application to Chronic Obstructive Pulmonary Disease](https://mlsanity.com/paper/2111.06962/) (2021)

## Abstract (arXiv)

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.