outCallTib {OGSA} | R Documentation |
Counts outliers by the Tibshirani and Hastie method and generates a list object with all outliers noted
outCallTib (dataSet, phenotype, tail='right', corr=FALSE, names=NULL)
dataSet |
Set of matrices of molecular data |
phenotype |
A vector of 0s and 1s of length nSample, where 1 = case, 0 = control |
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'). |
A list with all specific outlier calls for each molecular type in each case sample
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.
data(ExampleData) data('KEGG_BC_GS') # Set up dataSet dataSet <- list(expr, meth, cnv) # Set up Phenotype phenotype <- pheno names(phenotype) <- colnames(cnv) # set up values for expr-meth-cnv in that order tailLRL <- c('left', 'right', 'left') outTibLRL <- outCallTib(dataSet, phenotype, names=c('Expr', 'Meth', 'CNV'), tail=tailLRL)