Description Usage Arguments Details Value Author(s) References See Also Examples

Fit the parameters for normalization of a metabolomics data set.

1 2 3 4 5 6 7 8 9 10 |

`object` |
an |

`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 |

`formula` |
if fitfunc has formula interface or not |

`...` |
passed on to |

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.

- t1
divide by row-means of the

*L_2*scaled internal standards- one
divide by value of a single, user defined, internal standard

- totL2
divide by the square of sums of the full dataset

- nomis
See Sysi-Aho et al.

- crmn
See Redestig et al.

a normalization model

Henning Redestig

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`

1 2 3 4 5 6 7 8 9 | ```
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))
``` |

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