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ordinalgmifs: An R Package for Ordinal Regression in High-dimensional Data Settings
Kellie J Archer1, Jiayi Hou2, Qing Zhou1
1Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.
Cancer Informatics
|January 10, 2015
Summary
This study introduces the ordinalgmifs R package for analyzing high-throughput genomic data. It enables ordinal response modeling with more predictors than samples, crucial for cancer research.
Area of Science:
- Genomics
- Bioinformatics
- Statistical Modeling
Background:
- High-throughput genomic assays classify tissue samples (normal < pre-malignant < malignant).
- Molecular features may monotonically associate with cancer stage, indicating disease development mechanisms.
- Traditional ordinal models fail when predictors exceed samples (P > n).
Purpose of the Study:
- Introduce the ordinalgmifs R package for advanced ordinal response modeling.
- Provide a tool for analyzing genomic data where P > n.
- Facilitate the study of monotonic associations between molecular features and cancer progression.
Main Methods:
- Developed the ordinalgmifs R package implementing various ordinal response models.
- Designed for scenarios where the number of predictors (P) exceeds the sample size (n).
- Included R code examples for practical application.
Main Results:
- The ordinalgmifs package effectively fits ordinal models under P > n conditions.
- Demonstrates utility in high-throughput genomic data analysis for cancer classification.
- Addresses limitations of traditional methods in high-dimensional biological data.
Conclusions:
- The ordinalgmifs R package is a valuable tool for genomic research, particularly in cancer studies.
- Enables robust analysis of monotonic relationships in high-dimensional data.
- Facilitates a deeper understanding of molecular feature associations with disease progression.
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