Overview

OOMPA

OOMPA Standalone Packages

By "standalone" we mean a project that contains only one package, usually with the same name, usually with an accompanying manuscript. Each of these standalones is part of the broader OOMPA project.

NameNeedle

Using Needleman-Wunsch to Match Sample Names. The Needleman-Wunsch global alignment algorithm can be used to find approximate matches between sample names in different data sets. See J Wang and colleagues (2010).

SIBER

Systematic Identification of Bimodally Expressed Genes Using RNAseq Data, Provides models to identify bimodally expressed genes from RNAseq data based on the Bimodality Index. SIBERG models the RNAseq data in the finite mixture modeling framework and incorporates mechanisms for dealing with RNAseq normalization. Three types of mixture models are implemented, namely, the mixture of log normal, negative binomial, or generalized Poisson distribution. See Tong and colleagues (2013).

integIRTy

Integrating Multiple Modalities of High Throughput Assays Using Item Response Theory. Provides a systematic framework for integrating multiple modalities of assays profiled on the same set of samples. The goal is to identify genes that are altered in cancer either marginally or consistently across different assays. The heterogeneity among different platforms and different samples are automatically adjusted so that the overall alteration magnitude can be accurately inferred. See Tong and Coombes (2012).

UMPIRE

The Ultimate Microrray Prediction, Reality and Inference Engine (UMPIRE) is a package to facilitate the simulation of realistic microarray data sets with links to associated outcomes. See Zhang and Coombes (2012). Version 2.0 adds the ability to simulate realistic mixed-typed clinical data.

NewmanOmics

Extending the Newman Studentized Range Statistic to Transcriptomics. Extends the classical Newman studentized range statistic in various ways that can be applied to genome-scale transcriptomic or other expression data. See Tally and colleagues (2026).

DeepCNV

Exploiting Normal Contamination to Infer Copy Number from Deep Sequencing The DeepCNV package provides a systematic Bayesian framework for inferring copy number from deep sequencing data of one or a few genes. Under Development.