iC10: A copy number and expression-based classfier for breast...
In iC10: A Copy Number and Expression-Based Classifier for Breast Tumours

Description Usage Arguments Details Value Author(s) References See Also Examples

iC10 implements the classifier described in the paper 'Genome-driven integrated classification of breast cancer validated in over 7,500 samples' (Ali HR et al., Genome Biology 2014). It uses copy number and/or expression form breast cancer data, trains a pamr classifier (Tibshirani et al.) with the features available and predicts the iC10 group.

1	iC10(x, seed=25435)

`x`	An object with class `iC10features`: A list with elements 'train.CN', 'train.Exp', 'train.iC10', 'CN', 'Exp', 'map.cn', 'map.exp'
`seed`	seed to initialize random number generator. It is passed to `set.seed()`. See details.

This function trains a pamr classifier and predicts the set of samples. The shrinkage parameter is obtained with crossvalidation, therefore different runs can give different results (unless a seed is specified).

An object of class iC10. A list with the following elements:

`class`	Prediction classes for the samples
`posterior`	Probablitites for each sample to belong to each of the 10 groups
`centroids`	Shrunken Centroids for each of the 10 groups.
`fitted`	Normalized features for the samples classified.
`map.cn`	Annotation data for the copy number features
`map.exp`	Annotation data for the expression features

Oscar M. Rueda

Ali HR et al. Genome-driven integrated classification of breast cancer validated in over 7,500 samples. Genome Biology 2014; 15:431. Curtis et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 2012; 486:346-352. Tibshirani et al. Diagnosis of multiple cancer types by shrunken centroids of gene expression. PNAS 2002; 99(10):6567-6572.

See pamr.train, pamr.cv and pamr.predict in package pamr.

require(iC10TrainingData)
data(train.CN)
data(train.Exp)
features <- matchFeatures(Exp=train.Exp, Exp.by.feat="probe")
features <- normalizeFeatures(features, "scale")
res <- iC10(features)