PAM50Permutate: 'permutate' subject gene-expression for PAM50 confidence

In pbcmc: Permutation-Based Confidence for Molecular Classification

Description

Calculate the null Spearman's ρ distribution of each subtype by means of gene label permutation, in order to evaluate if the observed values could be obtained by random change.

Usage

## S4 method for signature 'PAM50'
permutate(object, nPerm = 10000, pCutoff = 0.01,  
    where = "fdr", keep = FALSE, corCutoff = 0.1, seed = 1234567890, 
    BPPARAM = bpparam(), verbose = getOption("verbose", default = TRUE))

Arguments

object	a MolecularPermutationClassifier subclass object.
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
corCutoff	numeric with correlation difference between classes cutoff used, i.e., |ρ(profile, class_A)-ρ(profile, class_B)|>corCutoff. Default 0.1
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().
verbose	should the user feedback be displayed? By default value is "verbose" global option parameter, if present, or FALSE otherwise.

Value

a PAM50 object with the following updated slots:

@permutation

$pvalues numeric matrix with subtype pvalues obtained as the number of times the permuted correlation is greater or equal the observed correlation divided the number of permutations. $fdr subtype adjusted pvalues for each subject with False Discovery Rate. $correlations list with subject matrix correlation of each permutation simulation. $subtype data.frame with classification results obtained by subtype function.

@parameters

$nPerm, $pCutoff, $where and $keep updated accordingly.

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.

Other PAM50: as, classify,PAM50-method, filtrate,PAM50-method, pam50centroids, subjectReport,PAM50-method, subtypes,PAM50-method

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

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