Description Usage Arguments Details Value Author(s) References Examples
Normalise a metabolomic data matrix using internal, external standards and other quality control metabolites
1 2 3 4 5 | NormQcmets(featuredata, factors = NULL, factormat = NULL, method = c("is",
"nomis", "ccmn", "ruv2", "ruvrand", "ruvrandclust"), isvec = NULL,
ncomp = NULL, k = NULL, plotk = TRUE, lambda = NULL, qcmets = NULL,
maxIter = 200, nUpdate = 100, lambdaUpdate = TRUE, p = 2,
saveoutput = FALSE, outputname = NULL, ...)
|
featuredata |
featuredata A data frame in the featuredata format. This is a dataframe with metabolites in columns and samples in rows. Unique sample names should be provided as row names. |
factors |
For the ccmn method. A vector or a dataframe containing biological factors. |
factormat |
For the ruv2 method. A design matrix for the linear model, consisting of biological factors of interest. |
method |
A character string indicating the required normalization
method. Must be one of " |
isvec |
A vector of internal standards to be used with the method
" |
ncomp |
Number of PCA components to be used for the " |
k |
Number of factors of unwanted variation to be included in the
" |
plotk |
For the " |
lambda |
The regularization parameter for the " |
qcmets |
A vector indicating which metabolites should be used as the
internal, external standards or other quality control metabolites
in the " |
maxIter |
For the " |
nUpdate |
For the " |
lambdaUpdate |
For the " |
p |
For the " |
saveoutput |
A logical indicating whether the normalised data matrix should be saved as a .csv file. |
outputname |
The name of the output file if the output has to be saved. |
... |
Other arguments to be passed onto |
These normalisation methods include "is
" which uses a
single standard, Cross-contribution Compensating
Multiple internal standard Normalisation, "ccmn
" (Redestig et
al., 2009); normalization using optimal selection of multiple internal
standards, "nomis
" (Sysi-aho et al. 2007), "ruv2
"
(De Livera et al. 2012a), and "ruvrand
", "ruvrandclust
"
(De Livera et al. 2015).
An overview of these normalisation methods are given by De Livera et al. (2015).
If the method is ‘ruv2’, the function will return an object of class
MArrayLM
, containing F statistics, t statistics,
corresponding confidence intervals, and adjusted and unadjusted p-values. See
LinearModelFit
. For all other methods, the result is an object of class
alldata
. Additionally, the list also
contains the removed unwanted variation component (UVcomp),and the
results from the optimization algorithm (opt) for the "ruvrandclust
" method
@seealso normFit
.
Alysha M De Livera, Gavriel Olshansky
De Livera, Alysha M De, M. Aho-Sysi, Laurent Jacob, J. Gagnon-Bartch, Sandra Castillo, J.A. Simpson, and Terence P. Speed. 2015. Statistical Methods for Handling Unwanted Variation in Metabolomics Data. Analytical Chemistry 87 (7). American Chemical Society: 3606-3615.
De Livera, A. M., Dias, D. A., De Souza, D., Rupasinghe, T., Pyke, J., Tull, D., Roessner, U., McConville, M., Speed, T. P. (2012a) Normalising and integrating metabolomics data. Analytical Chemistry 84(24): 1076-10776.
De Livera, A.M., Olshansky, M., Speed, T. P. (2013) Statistical analysis of metabolomics data. Methods in Molecular Biology In press.
Gagnon-Bartsch, Johann A., Speed, T. P. (2012) Using control genes to correct for unwanted variation in microarray data. Biostatistics 13(3): 539-552.
Redestig, H., Fukushima, A., Stenlund, H., Moritz, T., Arita, M., Saito, K., Kusano, M. (2009) Compensation for systematic cross-contribution improves normalization of mass spectrometry based metabolomics data. Analytical Chemistry 81(19): 7974-7980.
Sysi-Aho, M., Katajamaa, M., Yetukuri, L., Oresic, M. (2007) Normalization method for metabolomics data using optimal selection of multiple internal standards. BMC Bioinformatics 8(1); 93.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | ## Reading the data
data(mixdata)
featuredata <- mixdata$featuredata
sampledata<-mixdata$sampledata
metabolitedata<-mixdata$metabolitedata
isvec<-featuredata[,which(metabolitedata$type =="IS")[1]]
factors<-sampledata$type
qcmets<-which(metabolitedata$type =="IS")
## Normalise by an internal or an internal standard
norm_is <- NormQcmets(featuredata, method = "is", isvec=isvec)
PcaPlots(norm_is$featuredata, factors)
## Normalise by the NOMIS method
norm_nomis <- NormQcmets(featuredata, method = "nomis", qcmets = qcmets)
PcaPlots(norm_nomis$featuredata, factors)
## Normalise by the CCMN method
norm_ccmn <- NormQcmets(featuredata, factors, method = "ccmn", qcmets = qcmets, ncomp = 2)
PcaPlots(norm_ccmn$featuredata, factors)
## Normalise using RUV-random method
norm_ruvrand <- NormQcmets(featuredata, method = "ruvrand", qcmets = qcmets, k = 2)
PcaPlots(norm_ruvrand$featuredata, factors)
PcaPlots(norm_ruvrand$uvdata, sampledata$batch, main = "Unwanted batch variation")
## Normalise using RUV-random clustering method
##Not run
#norm_ruvrandclust <- NormQcmets(featuredata, method = "ruvrandclust", qcmets = qcmets, k = 2)
#PcaPlots(norm_ruvrandclust$featuredata, factors)
#PcaPlots(norm_ruvrandclust$uvdata, sampledata$batch, main = "Unwanted batch variation")
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