Multi-assay 'omics experiments on a set of samples are increasingly commonplace in biomedical research. MultiAssayExperiment implements data structures and methods for representing, manipulating, and integrating multi-assay experiments via efficient construction, subsetting, and extraction operations. These methods are implemented matching Bioconductor user experience by straightforward extending concept and design of single-assay classes such as SummarizedExperiment.

The software formalises a framework for classification in R. There are four stages; Data transformation, feature selection, classifier training, and prediction.

The missRows package implements the MI-MFA method to deal with missing individuals ('biological units') in multi-omics data integration. The MI-MFA method generates multiple imputed datasets from a Multiple Factor Analysis model, then the yield ...

Implementation of the BRGE's (Bioinformatic Research Group in Epidemiology from Center for Research in Environmental Epidemiology) MultiDataSet and ResultSet. MultiDataSet is designed for integrating multi omics data sets and ResultSet is a container ...

omicsPrint provides functionality for cross omic genetic fingerprinting, for example, to verify sample relationships between multiple omics data types, i.e. genomic, transcriptomic and epigenetic (DNA methylation).