filtrate {pbcmc} | R Documentation

## Virtual functions for MolecularPermutationClassifier hierarchy

### Description

The following functions establish an organized framework for MolecularPermutationClassifier subclasses data processing. In this context, the later are supposed to be implemented with respective responsibilities. In particular, once the class is created the user has to:

filtrate:

  Removes, from the exprs matrix, subjects not required by the classification algorithm.

classify:

  Generates subject classification according to subclass implementations (PAM50, etc.).

permute:

  Obtains subject classification based on the null correlation distribution by means permutation simulation.

subtype:

  Obtaind the new classification using permutation results.

subjectReport:

  A friendly report for physician treatment decision support.

databaseReport:

  A pdf with all subjectReports, if a database is available.

### Usage

    filtrate(object, verbose = getOption("verbose", default = FALSE))

    classify(object, ..., verbose = getOption("verbose", default = FALSE))

    permutate(object, nPerm = 10000L, pCutoff = 0.01, where = "fdr",
        keep = FALSE, ..., seed = 1234567890, BPPARAM = bpparam(),
        verbose = getOption("verbose", default = TRUE))

    subtypes(object, pCutoff = 0.01, ..., where = c("fdr", "pvalue")[1])

    subjectReport(object, subject)

    databaseReport(object, fileName, ..., verbose = getOption("verbose", default =
        TRUE))

### Arguments

`object` | MolecularPermutationClassifier child class object
`verbose` | should the user feedback be displayed? By default value is "verbose" global option parameter, if present, or FALSE otherwise.
`...` | additional parameters for future implementations.
`nPerm` | integer with number of permutations. Default: 1e4L.
`pCutoff` | numeric with p-value or fdr cutoff used, i.e., variable<pCutoff. Default: 0.01.
`where` | character with significant value used. Default value is "fdr".
`keep` | should null distribution simulation values be kept?. Default: FALSE
`seed` | integer to use as random seed. Default: 1234567890.
`BPPARAM` | an optional BiocParallelParam instance determining the parallel back-end to be used during evaluation, or a list of BiocParallelParam instances, to be applied in sequence for nested calls to bplapply. Default=bpparam().
`subject` | integer to select the appropriate subject to report.
`fileName` | character with the name of the pdf report file to save.

### Value

A MolecularPermutationClassifier child according to the actual object class.

### Author(s)

Cristobal Fresno cfresno@bdmg.com.ar, German A. Gonzalez ggonzalez@bdmg.com.ar, Andrea S. Llera allera@leloir.org.ar and Elmer Andres Fernandez efernandez@bdmg.com.ar

### See Also

`PAM50` for a complete example.

Other MolecularPermutationClassifier PAM50: `PAM50-class`, `loadBCDataset`

### Examples

    ##Using pam50centroids package example data
    data(pam50centroids)
    pam50centroids
    pam50centroids<-filtrate(pam50centroids, verbose=TRUE)
    pam50centroids<-classify(pam50centroids, std="none", verbose=TRUE)
    ##Let's run a quick example with 100 permutations. It is recommended at
    ##least 10.000
    pam50centroids<-permutate(pam50centroids, nPerm=100, pCutoff=0.01,
    corCutoff=0.1, verbose=TRUE)
    pam50centroids

[Package pbcmc version 1.8.0 Index]