OGSA (version 1.2.0)

outCallRankE: outCallRankE

Description

Counts outliers by the Ghosh method and generates list objects with all outliers noted

Usage

outCallRankE (expressionSet, thres= 0.05, tail='right', corr=FALSE,
offsets=NULL, names=NULL)

Arguments

expressionSet

object containing Set of matrices of molecular data and phenotype data (1 for case, 0 for control)

thres

Alpha value

tail

A vector equal to the number of matrices with values left or right for where to find outliers

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)

names

A vector equal to the 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)

 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')

outRankLRL <- outCallRankE(expressionSet, names=c('Expr', 'Meth', 'CNV'),
                                                          tail=tailLRL)