matchFeatures {iC10}

Matching features from the classifier to the test data.

Description

This function matches available copy number and/or expression data features to the training signatures; using either genomic position or HUGO gene name for copy number features and either Illumina probe names or HUGO gene name for expression features.

Usage

matchFeatures(CN = NULL, Exp = NULL,
CN.by.feat = c("gene", "probe"),
Exp.by.feat = c("gene", "probe"),
ref="hg19")

Arguments

CN

Data must be log2 copy number ratios. Two formats are allowed: - a matrix where each row represents a gene and each column a sample. In this case CN.by.feat must be "gene" and the rownames must be the hgnc gene names. - a data.frame with segmented data. The following columns must exist: 'ID' for the sample name, 'chromosome_name' for the chromosome (must be numeric), 'loc.start' for the start position of the region, 'loc.end' for the end position of the region, 'seg.mean' for the log2ratio of the segment. If NULL, copy number is not used in the classifier.

Exp

Matrix with the expression data to classify. Each row must be a gene or an Illumina probe, and each column must correspond to a sample. Rownames must be either Illumina probes, in which case Exp.by.feat must be "probe"; or hgnc gene names, in which case Exp.by.feat must be "gene". If NULL, expression is not used in the classifier.

CN.by.feat

Either "probe" or "gene", Default is "probe".

Exp.by.feat

Either "probe" or "gene", Default is "gene".

ref

Either "hg18", "hg19" or "hg38". It is used to match the copy number probes if CN.by.feat is "probe"

Details

One of CN or Exp must be not NULL. If matching is done by gene, hgnc gene name is used to match the rownames of the features. A list of synonym gene names is used (see Map.All). For copy number features matched by probe, the maximum log ratio in absolute value inside the limits of the feature is used. If there is no copy number in that region, the value of the probe before it is used.

Value

A list with the following elements is returned:

CN

copy number data to classify

train.CN

copy number training data

Exp

expression data to classify

train.Exp

expression training data

train.iC10

iC10 assignments for the training data

map.cn

annotation data for the copy number features

map.exp

annotation data for the expression features

Note

Note that the training set will be different, depending on the features matched. Genomic annotation for the training dataset has been obtained from Mark Dunning's lluminaHumanv3.db package.

Author(s)

Oscar M Rueda

References

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.

See Also

normalizeFeatures, iC10

Examples

require(iC10TrainingData)
data(train.CN)
data(train.Exp)
features <- matchFeatures(Exp=train.Exp,Exp.by.feat="probe", ref="hg18")
str(features)