permutate,PAM50-method {pbcmc} | R Documentation

## `permutate` subject gene-expression for PAM50 confidence

### 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 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

### 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

[Package pbcmc version 1.8.0 Index]