MRexperiment-class: Class "MRexperiment" - a modified eSet object for the data...
In metagenomeSeq: Statistical analysis for sparse high-throughput sequencing

Description Objects from the Class Extends Methods Note Examples

This is the main class for metagenomeSeq.

Objects should be created with calls to newMRexperiment.

Class eSet (package 'Biobase'), directly. Class VersionedBiobase (package 'Biobase'), by class "eSet", distance 2. Class Versioned (package 'Biobase'), by class "eSet", distance 3.

Class-specific methods.

[: Subset operation, taking two arguments and indexing the sample and variable. Returns an MRexperiment object, including relevant metadata. Setting drop=TRUE generates an error. Subsetting the data, the experiment summary slot is repopulated and pData is repopulated after calling factor (removing levels not present).

Note: This is a summary for reference. For an explanation of the actual usage, see the vignette.

MRexperiments are the main class in use by metagenomeSeq. The class extends eSet and provides additional slots which are populated during the analysis pipeline.

MRexperiment dataset are created with calls to newMRexperiment. MRexperiment datasets contain raw count matrices (integers) accessible through MRcounts. Similarly, normalized count matrices can be accessed (following normalization) through MRcounts by calling norm=TRUE. Following an analysis, a matrix of posterior probabilities for counts is accessible through posteriorProbs.

The normalization factors used in analysis can be recovered by normFactors, as can the library sizes of samples (depths of coverage), libSize.

Similarly to other RNASeq bioconductor packages available, the rows of the matrix correspond to a feature (be it OTU, species, gene, etc.) and each column an experimental sample. Pertinent clinical information and potential confounding factors are stored in the phenoData slot (accessed via pData).

To populate the various slots in an MRexperiment several functions are run. 1) cumNormStat calculates the proper percentile to calculate normalization factors. The cumNormStat slot is populated. 2) cumNorm calculates the actual normalization factors using p = cumNormStat.

Other functions will place subsequent matrices (normalized counts (cumNormMat), posterior probabilities (posteriorProbs))

As mentioned above, MRexperiment is derived from the virtual class,eSet and thereby has a phenoData slot which allows for sample annotation. In the phenoData data frame factors are stored. The normalization factors and library size information is stored in a slot called expSummary that is an annotated data frame and is repopulated for subsetted data.