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Ordinalgmifs: An R Package for Ordinal Regression in High-dimensional Data Settings.

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.

SUBMITTER: Archer KJ

PROVIDER: S-EPMC4266195 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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ordinalgmifs: An R Package for Ordinal Regression in High-dimensional Data Settings.

Archer Kellie J KJ Hou Jiayi J Zhou Qing Q Ferber Kyle K Layne John G JG Gentry Amanda E AE

Cancer informatics 20141210

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 level ...[more]

PMID: 25574124

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