Outlier profile Analysis

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Description

Returns a matrix with 0, -1 and 1 entries that describe outlier profiles in samples. The rows reperesent genes and the columns represent samples. -1 implies that the gene is a down-regulated outlier, 1 indicates an up-regulate outlier and 0 means that the gene is not an outlier in a sample.

Usage

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opa(exprs.matrix, ...)

## S4 method for signature 'matrix'
opa(exprs.matrix, group, upper.quantile = 0.95,
  lower.quantile = 0.05)

## S4 method for signature 'ExpressionSet'
opa(exprs.matrix, group, upper.quantile = 0.95,
  lower.quantile = 0.05)

Arguments

exprs.matrix

Gene expression data. Can be either a matrix or an object of type ExpressionSet.

...

Numeric. To supply values for upper.quantile and lower.quantile arguments if default values are going to be override.

group

A vector of factors representing the groups to which each sample belong. This can be either a vector of 0s and 1s, or normal and cases.

upper.quantile

Numeric. The cut-off for upper quantile when determining outliers. Default to 0.95

lower.quantile

Numeric. The cut-off for lower quantile when determining outliers. Default to 0.05

Value

opa returns an object of type OPPARList. The outlier profiles are stored in profileMatrix and can be accessed using $. It it also possible to retrieve parameters used to run the outlier profile analysis, such as upper.quantile, lower.quantile via the $ operator.

Methods (by class)

  • matrix: opa(exprs.matrix, group, lower.quantile = 0.05, upper.quantile = 0.95)

  • ExpressionSet: opa(eset, group, lower.quantile = 0.05, upper.quantile = 0.95)

See Also

Wang, C., Taciroglu, A., Maetschke, S. R., Nelson, C. C., Ragan, M. A., & Davis, M. J. (2012). mCOPA: analysis of heterogeneous features in cancer expression data. Journal of Clinical Bioinformatics, 2, 22. http://doi.org/10.1186/2043-9113-2-22

Examples

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# loading bcm object from GSE46141 dataset
data(GSE46141)
library(Biobase)
# defining the group variable. local breast tumors are the controls
# and the rest of the samples are the diseased samples
group <- sapply(pData(bcm)$source_name_ch1, function(x){ ifelse(x == "breast",0,1)})
group <- factor(group)
# running opa with default values (i.e upper.quantile = 0.95, lower.quantile = 0.05)
# the result is an object of type OPPARList
opa(bcm,group = group)