copaStat {OGSA} | R Documentation |
Calculates outlier statistics by the Tibshirani-Hastie method
copaStat (data, phenotype, tail='right', perms=100, permType='array')
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 |
perms |
The number of permutations |
permType |
By all on array or by gene, if by gene increase perms significantly and plan on lots of time; in theory array should be fine as genes are rescaled |
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 values for expr-meth-cnv in that order tailLRL <- c('left', 'right', 'left') #setup dataList dataSet <- list(expr, meth, cnv) data <- dataSet[[1]] tibL <- copaStat(data, phenotype, tail='right', perms=100, permType='array')