outCount {OGSA} | R Documentation |
Counts outliers by the Tibshirani and Hastie method. Adds the ability to subtract for outliers in the normals using corr = TRUE
outCount (data, phenotype, tail='right', corr=FALSE)
data |
A matrix of nGene by nSample |
phenotype |
A vector of 0s and 1s of length nSample, where 1 = case, 0 = control |
tail |
Indicates whether outliers are up (right) or down (left) outliers |
corr |
Whether to correct for normal outliers |
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) # Set up Phenotype phenotype <- pheno names(phenotype) <- colnames(cnv) #set up datalist dataSet <- list(expr,meth,cnv) # set up values for expr-meth-cnv in that order tailLRL <- c('left', 'right', 'left') outTibLRL <- outCallTib(dataSet, phenotype=pheno, names=c('Expr', 'Meth', 'CNV'), tail=tailLRL)