Extensions of Heterogeneity in Integration and Prediction (HIP) With R Shiny Application
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| Formato: | Artículo Open Access |
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Wiley
2025
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| _version_ | 1867020023077273600 |
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| author | Jessica Butts Leif Verace Christine Wendt Russell Bowler Craig P. Hersh Qi Long Lynn Eberly Sandra E. Safo |
| author_facet | Jessica Butts Leif Verace Christine Wendt Russell Bowler Craig P. Hersh Qi Long Lynn Eberly Sandra E. Safo Jessica Butts Leif Verace Christine Wendt Russell Bowler Craig P. Hersh Qi Long Lynn Eberly Sandra E. Safo |
| collection | Wiley Open Access |
| contents | Extensions of Heterogeneity in Integration and Prediction (HIP) With R Shiny Application Jessica Butts Leif Verace Christine Wendt Russell Bowler Craig P. Hersh Qi Long Lynn Eberly Sandra E. Safo Statistics in Medicine ABSTRACTMultiple data views measured on the same set of participants are becoming more common and have 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 publicly available data. An R‐package for HIP is available at https://github.com/lasandrall/HIP. 10.1002/sim.70036 http://creativecommons.org/licenses/by-nc/4.0/ |
| doi_str_mv | 10.1002/sim.70036 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_70036 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by-nc/4.0/ |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Extensions of Heterogeneity in Integration and Prediction (HIP) With R Shiny Application Jessica Butts Leif Verace Christine Wendt Russell Bowler Craig P. Hersh Qi Long Lynn Eberly Sandra E. Safo Statistics in Medicine Extensions of Heterogeneity in Integration and Prediction (HIP) With R Shiny Application Jessica Butts Leif Verace Christine Wendt Russell Bowler Craig P. Hersh Qi Long Lynn Eberly Sandra E. Safo Statistics in Medicine ABSTRACTMultiple data views measured on the same set of participants are becoming more common and have 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 publicly available data. An R‐package for HIP is available at https://github.com/lasandrall/HIP. 10.1002/sim.70036 http://creativecommons.org/licenses/by-nc/4.0/ |
| title | Extensions of Heterogeneity in Integration and Prediction (HIP) With R Shiny Application |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.70036 |