Ordinalgmifs: An R Package for Ordinal Regression in High-dimensional Data Settings. (January 2014)

• Record Type: Journal Article
• Authors: Archer, Kellie J.; Hou, Jiayi; Zhou, Qing; Ferber, Kyle; Layne, John G.; Gentry, Amanda E.
• Is Part Of: Cancer informatics. Volume 13(2014)
• Publication Date: 2014-01
• Subjects: ordinal response -- high-dimensional features -- penalized models -- R; Bioinformatics -- Periodicals; Biology -- Data processing -- Periodicals; Cancer -- Periodicals; Cancer -- Research -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.4137/CIN.S20806
• Languages: English
• ISSNs: 1176-9351

Abstract

High-throughput genomic assays are performed using tissue samples with the goal of classifying the samples as normal < pre-malignant < malignant or by stage of cancer using a small set of molecular features. In such cases, molecular features monotonically associated with the ordinal response may be important to disease development; that is, an increase in the phenotypic level (stage of cancer) may be mechanistically linked through a monotonic association with gene expression or methylation levels. Though traditional ordinal response modeling methods exist, they assume independence among the predictor variables and require the number of samples ( n ) to exceed the number of covariates ( P ) included in the model. In this paper, we describe our ordinalgmifs R package, available from the Comprehensive R Archive Network, which can fit a variety of ordinal response models when the number of predictors ( P ) exceeds the sample size ( n ). R code illustrating usage is also provided.