opa {oppar} | R Documentation

## Outlier profile Analysis

### 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

    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

    # 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)

[Package oppar version 1.10.0 Index]