MolecularPermutationClassifier-class: Class 'MolecularPermutationClassifier' S4 implementation in R

In pbcmc: Permutation-Based Confidence for Molecular Classification

Description

Virtual class to represent gene-based molecular signature classification by means of permutation test.

Slots

parameters

named list with at least the following fields:

$nPerm

integer with number of permutations. Default: 1e4L

$where

character with significant value used. Default value is "fdr".

$pCutoff

numeric with p-value or fdr cutoff used, i.e., variable<pCutoff. Default: 0.01

$keep

should null distribution simulation values be kept?. Default: FALSE

exprs

matrix with gene exprs profile, where genes are in rows and subjects as columns, a.k.a., M matrix.

annotation

data.frame with individual annotations (genes, etc). Minimal compulsory fields are:

$probe

same characters as in row.names(M).

$EntrezGene.ID

integer with NCBI Entrez Data Base.

$NCBI.gene.symbol

character with gene mnemonic, a.k.a. gene symbol.

targets

data.frame with additional subject data (optional).

classification

named list with at least the following fields:

$class

factor with all possible class levels.

permutation

named list with at least the following fields:

$pvalues

numeric matrix with subjects in row and classes in columns.

$fdr

numeric matrix with False Discovery Rate correction of p-values by row.

Superclasses

None declared.

Subclasses

PAM50

Peruo et al. (2000 and 2010) breast cancer subtypes, i. e., Luminal A, Luminal B, Basal, Her2 or Normal-like subtypes as implemented in genefu library (Haibe-Kains et al. 2014).

Functions

MolecularPermutationClassifier S4 class includes the following functions:

• Integrity check:

  validity

  will check appropriate annotation data.frame minimal required columns, all named parameters and if exprs and annotation dimension matches.

  prototype

  just for an empty class with default values: nPerm=1e4L, where="fdr", pCutoff=0.01, corCutoff=0.1 and keep=FALSE

  .

• Generics:

  show,print

  basic class display wrappers.

  summary

  classifier statistics.

• Constructors (as this class is virtual see subclass' 'documentation).

  setAs

  MAList to PAM50

  as.PAM50

  wrapper for PAM50 setAs from MAList.

  loadBCDataset

  wrapper to load BreastCancerXX data (Class, exprs, annotation, clinical data).

• Getters for the corresponding slots (parameters, exprs, annotation, targets, classification and permutation).

• Setters for the corresponding slots (parameters<-, annotation<- and targets<-).

• Particular (virtual) functions:

  filtrate

  remove from the exprs matrix subjects not required by the classification algorithm.

  classify

  generate subject classification according to subclasses implementation (PAM50, etc.).

  permutate

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

  subtypes

  obtain 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.

Author(s)

Cristobal Fresno [e-mail], German A. Gonzalez [e-mail], Andrea S. Llera [e-mail] and Elmer Andres Fernandez [e-mail]

References

1. Haibe-Kains B, Schroeder M, Bontempi G, Sotiriou C and Quackenbush J, 2014, genefu: Relevant Functions for Gene Expression Analysis, Especially in Breast Cancer. R package version 1.16.0, www.pmgenomics.ca/bhklab/

2. Perou CM, Sorlie T, Eisen MB, et al., 2000, Molecular portraits of human breast tumors. Nature 406:747-752

3. Perou CM, Parker JS, Prat A, Ellis MJ, Bernard PB., 2010, Clinical implementation of the intrinsic subtypes of breast cancer, The Lancet Oncology 11(8):718-719

See Also

PAM50 for a complete example, loadBCDataset to load BreastCancerXX dataset, filtrate, classify and permutate to get corresponding Breast Cancer subtype. Getters/Setters for this class are parameters, exprs, annotation, targets, classification and permutation.

Other MolecularPermutationClassifier: parameters, show

pbcmc documentation built on Nov. 1, 2018, 2:09 a.m.