copaIntE {OGSA} | R Documentation

## copaIntE

### Description

Counts outliers by Tibshirani-Hastie method by calling outCount after setting up list or by rank outlier method by calling outRank

### Usage

    copaIntE(expressionSet, tails, thres = 0.05, method='Tibshirani',
    corr=FALSE, offsets=NULL)

### Arguments

`expressionSet` | object containing Set of matrices of molecular data and phenotype data (1 for case, 0 for control)
`tails` | Vector equal to number of matrices with values left or right for where to find outliers
`thres` | alpha value
`method` | Tibshirani , Rank
`corr` | Whether to correct for normal outliers
`offsets` | A vector equal to the number of matrices which sets the minimum value relative to normal to call outlier (corrected rank only)

### Value

A vector with outlier counts by gene

### References

Ochs, M. F., Farrar, J. E., Considine, M., Wei, Y., Meshinchi, S., & Arceci, R. J. (n.d.). Outlier Analysis and Top Scoring Pair for Integrated Data Analysis and Biomarker Discovery. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1-1. doi:10.1109/tcbb.2013.153

### Examples

    data(ExampleData)

     library(Biobase)
    # building the Annotated Data Frame
     phenoData <- AnnotatedDataFrame(
         data.frame(
            type = factor(x = pheno, labels = c("Control", "Case")),
             row.names = colnames(expr)
         )
     )
    # build environment
     inputData <- list2env(list(exprs = expr, meth = meth, cnv = cnv))

    # build expressionSet - other information can be added here
     expressionSet <- ExpressionSet(inputData, phenoData)

    # set up values for expr-meth-cnv in that order
     tailLRL <- c('left', 'right', 'left')

    tibLRL <- copaIntE(expressionSet, tails=tailLRL)

[Package OGSA version 1.16.0 Index]