outCallRankE {OGSA} | R Documentation |
Counts outliers by the Ghosh method and generates list objects with all outliers noted
outCallRankE (expressionSet, thres= 0.05, tail='right', corr=FALSE, offsets=NULL, names=NULL)
expressionSet |
object containing Set of matrices of molecular data and phenotype data (1 for case, 0 for 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) 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 for the tails in the order that they are exported, # for example: tailLRL <- c('left', 'right', 'left') outRankLRL <- outCallRankE(expressionSet, names=c('Expr', 'Meth', 'CNV'), tail=tailLRL)