OGSA (version 1.2.0)

outCallTibE: outCallTibE

Description

Counts outliers by the Tibshirani and Hastie method and generates a list object with all outliers noted

Usage

outCallTibE (expressionSet, tail='right', corr=FALSE, names=NULL)

Arguments

expressionSet

ExpressionSet object containing sets of data and phenotype information

tail

Vector equal to number of matrices with values 'left' or 'right' for where to find outliers

corr

whether to correct for normal outliers ONLY for compatibility, since method does not allow determining specific changes in cases, it will just print message if corr = TRUE

names

Vector equal to number of matrices to name molecular type of data (e.g., 'CNV').

Value

A list with all specific outlier calls for each molecular type in each case sample

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

D. Ghosh. (2010). Discrete Nonparametric Algorithms for Outlier Detection with Genomic Data. J. Biopharmaceutical Statistics, 20(2), 193-208.

Examples

data(ExampleData)
data('KEGG_BC_GS')

 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 for the tails in the order that they are exported, for example:
tailLRL <- c('left', 'right', 'left')


outTibLRL <- outCallTibE(expressionSet, names=c('Expr', 'Meth', 'CNV'), tail=tailLRL)