outCallTibE {OGSA} | R Documentation |
Counts outliers by the Tibshirani and Hastie method and generates a list object with all outliers noted
outCallTibE (expressionSet, tail='right', corr=FALSE, names=NULL)
expressionSet |
ExpressionSet object containing sets of data and phenotype information |
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') 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') outTibLRL <- outCallTibE(expressionSet, names=c('Expr', 'Meth', 'CNV'), tail=tailLRL)