normPred {crmn} | R Documentation |
Predict the normalized data using a previously fitted normalization model.
normPred(normObj, newdata, factors, lg=TRUE, ...)
normObj |
the result from normFit |
newdata |
an ExpressionSet or a matrix (in which case the
standards must be passed on via ... ),
possibly the same as used to
fit the normalization model in order to get the fitted data. |
factors |
column names in the pheno data slot describing the biological factors. Or a design matrix. |
lg |
logical indicating that the data should be log transformed |
... |
passed on to standardsPred , standardsFit ,
standards , analytes |
Apply fitted normalization parameters to new data to get normalized data. Current can not only handle matrices as input for methods 'RI' and 'one'.
the normalized data
Henning Redestig henning@psc.riken.jp
normFit
data(mix) nfit <- normFit(mix, "crmn", factor="type", ncomp=3) normedData <- normPred(nfit, mix, "type") slplot(pca(t(log2(exprs(normedData)))), scol=as.integer(mix$type)) ## same thing Y <- exprs(mix) G <- model.matrix(~-1+mix$type) isIS <- fData(mix)$tag == 'IS' nfit <- normFit(Y, "crmn", factors=G, ncomp=3, standards=isIS) normedData <- normPred(nfit, Y, G, standards=isIS) slplot(pca(t(log2(normedData))), scol=as.integer(mix$type))