pbcmc (version 1.0.0)

permutate,PAM50-method: permutate subject gene-expression for PAM50 confidence

Description

Calculate the null Spearman's $\rho$ 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

"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

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., $|\rho(profile, class_A)-\rho(profile, class_B)|>corCutoff$. 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.

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