copaIntE {OGSA} | R Documentation |
Counts outliers by Tibshirani-Hastie method by calling outCount after setting up list or by rank outlier method by calling outRank
copaIntE(expressionSet, tails, thres = 0.05, method='Tibshirani', corr=FALSE, offsets=NULL)
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) |
A vector with outlier counts by gene
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
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)