copaIntE

Description

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

Usage

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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

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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)