OOMPA Core Packages
- Class Unions, Matrix Operations, and Color Schemes for OOMPA. Provides class unions that must be preloaded in order for the basic tools in the OOMPA project to be defined and loaded. It also includes vectorized operations for row-by-row means, variances, and t-tests. Finally, it provides new color schemes.
- Data to Illustrate OOMPA Algorithms. This is a data-only package to provide example data for other packages that are part of the OOMPA suite of packages.
- Basic Functions for Pre-Processing Microarrays. Provides classes to pre-process microarray gene expression data as part of the OOMPA collection of packages.
- Classes and Methods for "Class Comparison" Problems on Microarrays. Defines the classes used for "class comparison" problems in the OOMPA project. Class comparison includes tests for differential expression; see Simon's book for details on typical problem types.
- Classes and Methods for "Class Discovery" with Microarrays or Proteomics. Defines the classes used for "class discovery" problems in the OOMPA project. Class discovery primarily consists of unsupervised clustering methods with attempts to assess their statistical significance.
- The Tail-Rank Statistic. Implements the tail-rank statistic for selecting biomarkers from a microarray data set, an efficient nonparametric test focused on the distributional tails. See the Tolstoy paper.
- Qualitative Palettes with Many Colors. Tools for creating, viewing, and assessing qualitative palettes with many (20-30 or more) colors. See Coombes and colleagues (2019).
- Bayesian Analysis of Different Rates in Different Groups. Test for different proportions (rates) in different groups using a Bayesian model in which all rate parameters follow a beta distribution and are selected from a common hyperdistribution. Includes tools to fit an arbitrary mixture of beta distributions. Under Development.
- Partial LeAst Squares for Multiomic Analysis. Contains tools for supervised analyes of incomplete, overlapping multi-omics datasets. Under Development.
By "singletons" we mean projects that contain only one package, usually with the same name, usually with an accompanying manuscript. Each of these singletons is part of the broader OOMPA project.
- 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).
- 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).
- 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).
- 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.
- 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. Under Development.