outCallRank {OGSA} | R Documentation |
Counts outliers by the Ghosh method and generates list objects with all outliers noted
outCallRank (dataSet, phenotype, thres= 0.05, tail='right', corr=FALSE, offsets=NULL, names=NULL)
dataSet |
Set of matrices of molecular data |
phenotype |
A vector of 0s and 1s of length nSample, where 1 = case, 0 = 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) |
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) #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') outRankLRL <- outCallRank(dataSet, phenotype, names=c('Expr', 'Meth', 'CNV'), tail=tailLRL)