normFit {crmn} | R Documentation |
Fit the parameters for normalization of a metabolomics data set.
normFit(object, method, one="Succinate_d4", factors, lg=TRUE, fitfunc=lm, ...)
object |
an ExpressionSet or a matrix (with samples as
columns) in which case the standards must be passed on via ... |
method |
chosen normalization method |
one |
single internal standard to use for normalization |
factors |
column names in the pheno data slot describing the biological factors. Or a design matrix directly. |
lg |
logical indicating that the data should be log transformed |
fitfunc |
the function that creates the model fit for normalization, must use the same interfaces as lm . |
... |
passed on to standardsFit , standards , analytes |
Normalization is first done by fitting a model and then applying
that model either to new data or the same data using
normPred
. Five different methods are implemented.
a normalization model
Henning Redestig henning@psc.riken.jp
Sysi-Aho, M.; Katajamaa, M.; Yetukuri, L. & Oresic, M. Normalization method for metabolomics data using optimal selection of multiple internal standards. BMC Bioinformatics, 2007, 8, 93
Redestig, H.; Fukushima, A.; Stenlund, H.; Moritz, T.; Arita, M.; Saito, K. & Kusano, M. Compensation for systematic cross-contribution improves normalization of mass spectrometry based metabolomics data Anal Chem, 2009, 81, 7974-7980
normPred
, standards
, model.matrix
data(mix) nfit <- normFit(mix, "crmn", factors="type", ncomp=3) slplot(sFit(nfit)$fit$pc, scol=as.integer(mix$runorder)) ## same thing Y <- exprs(mix) G <- model.matrix(~-1+mix$type) isIS <- fData(mix)$tag == 'IS' nfit <- normFit(Y, "crmn", factors=G, ncomp=3, standards=isIS) slplot(sFit(nfit)$fit$pc, scol=as.integer(mix$runorder))