CRMN

Description

Normalize metabolomics data using CCMN and other methods

Details

A package implementing the 'Cross-contribution compensating multiple standard normalization' described in Redestig et al. (2009) Analytical Chemistry, https://doi.org/10.1021/ac901143w. Can be used to normalize metabolomics data. Do openVignette("crmn") to see the manual.

Author(s)

Henning Redestig

Accessor for the analytes

Description

Subset an data set to only contain the analytes.

Usage

analytes(object, standards=NULL, ...)

Arguments

Value

subsetted dataset

Author(s)

Henning Redestig

Examples

data(mix)
analytes(mix)
analytes(exprs(mix), fData(mix)$tag == 'IS')

Accessor for the analytes

Description

Subset an expression set to remove the internal standards

Usage

analytes_eset(object, where = "tag", what = "IS", ...)

Arguments

Value

ExpressionSet

Author(s)

Henning Redestig

Examples

data(mix)
analytes(mix)
fData(mix)$test <- fData(mix)$tag
analytes(mix, where="test")

Accessor for the analytes

Description

Subset an expression set to remove the internal standards

Usage

analytes_other(object, standards, ...)

Arguments

Value

ExpressionSet

Author(s)

Henning Redestig

Examples

data(mix)
analytes(exprs(mix), fData(mix)$tag == 'IS')

Drop unused levels

Description

Drop unused factor levels in a data frame.

Usage

dropunusedlevels(x)

Arguments

Author(s)

Henning Redestig

Examples

iris[1:10,]$Species
dropunusedlevels(iris[1:10,])$Species

Make X

Description

Construct a design matrix

Usage

makeX(object, factors, ...)

## S4 method for signature 'ANY,matrix'
makeX(object, factors, ...)

## S4 method for signature 'ExpressionSet,character'
makeX(object, factors, ...)

Arguments

Details

Make a design matrix from the pheno data slot of an expression set, taking care that factors and numerical are handled properly. No interactions are included and formula is the most simple possible, i.e. y~-1+term1+term2+.... Can also be given anything as object in which case factor must be a design matrix. It that case the same design matrix is returned.

Value

a design matrix

Author(s)

Henning Redestig

Examples

data(mix)
makeX(mix, "runorder")
runorder <- mix$runorder
makeX(mix, model.matrix(~-1+runorder))

Accessor for the method

Description

Get the method

Usage

method(object, ...)

method(object, ...)

Arguments

Value

the method (content differs between normlization methods)

Author(s)

Henning Redestig

Matrix safe accessor of expression slot

Description

Get the expression data from an ExpressionSet or just return the given matrix

Usage

mexprs(object)

mexprs(object)

## S4 method for signature 'ExpressionSet'
mexprs(object)

Arguments

Value

the expression data

Author(s)

Henning Redestig

Examples

data(mix)
head(mexprs(mix))
head(mexprs(exprs(mix)))

Accessor

Description

Matrix safe setter of expression slot

Usage

mexprs(object) <- value

## S4 replacement method for signature 'ExpressionSet,matrix'
mexprs(object) <- value

mexprs(object) <- value

Arguments

Details

Set the expression data in an ExpressionSet or just return the given matrix

Value

the expression data

Author(s)

Henning Redestig

Examples

data(mix)
test <- mix
mexprs(test) <- exprs(mix) * 0
head(mexprs(test))
test <- exprs(mix)
mexprs(test) <- test * 0
head(mexprs(test))

Dilution mixture dataset.

Description

Mixture dilution series

Usage

data(mix)

Details

Multi-component dilution series. GC-TOF/MS measurements by Miyako Kusano. Input concentrations are known and given in the original publication.

Author(s)

Henning Redestig

Examples

 data(mix)
 fData(mix)
 exprs(mix)
 pData(mix)

Accessor for the model

Description

Get the model

Usage

model(object, ...)

model(object, ...)

Arguments

Value

the model (content differs between normlization models)

Author(s)

Henning Redestig

Normalization model

Description

Common class representation for normalization models.

Author(s)

Henning Redestig

Fit a normalization model

Description

Fit the parameters for normalization of a metabolomics data set.

Usage

normFit(
  object,
  method,
  one = "Succinate_d4",
  factors = NULL,
  lg = TRUE,
  fitfunc = lm,
  formula = TRUE,
  ...
)

Arguments

Details

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.

Value

a normalization model

Author(s)

Henning Redestig

References

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

See Also

normPred, standards, model.matrix

Examples

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))

Predict for normalization

Description

Predict the normalized data using a previously fitted normalization model.

Usage

normPred(normObj, newdata, factors = NULL, lg = TRUE, predfunc = predict, ...)

Arguments

Details

Apply fitted normalization parameters to new data to get normalized data. Current can not only handle matrices as input for methods 'RI' and 'one'.

Value

the normalized data

Author(s)

Henning Redestig

See Also

normFit

Examples

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$type))
## same thing
Y <- exprs(mix)
G <- with(pData(mix), model.matrix(~-1+type))
isIS <- fData(mix)$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))

Normalize a metabolomics dataset

Description

Normalization methods for metabolomics data

Usage

normalize(object, method, segments = NULL, ...)

Arguments

Details

Wrapper function for normFit and normPred

Value

the normalized dataset

Author(s)

Henning Redestig

See Also

normFit, normPred

Examples

data(mix)
normalize(mix, "crmn", factor="type", ncomp=3)
#other methods
normalize(mix, "one")
normalize(mix, "avg")
normalize(mix, "nomis")
normalize(mix, "t1")
normalize(mix, "ri")
normalize(mix, "median")
normalize(mix, "totL2")
## can also do normalization with matrices
Y <- exprs(mix)
G <- with(pData(mix), model.matrix(~-1+type))
isIS <- with(fData(mix), tag == "IS")
normalize(Y, "crmn", factor=G, ncomp=3, standards=isIS)

Muffle the pca function

Description

PCA and Q2 issues warnings about biasedness and poorly estimated PCs. The first is non-informative and the poorly estimated PCs will show up as poor overfitting which leads to a choice of fewer PCs i.e. not a problem. This function is mean to muffle those warnings. Only used for version of pcaMethods before 1.26.0.

Usage

pcaMuffle(w)

Arguments

Value

nothing

Author(s)

Henning Redestig

Plot a statistics for CRMN normalization model

Description

Simple plot function for a CRMN normalization model.

Usage

## S3 method for class 'nFit'
plot(x, y = NULL, ...)

Arguments

Details

Shows Tz and the optimization (if computed) of the PCA model. The number of components used for normalization should not exceed the maximum indicated by Q2. The structure shown in the Tz plot indicate the analytical variance which is exactly independent of the experimental design. The corresponding loading plot shows how this structure is capture by the used ISs.

Value

nothing

Author(s)

Henning Redestig

See Also

slplot

Examples

data(mix)
nfit <- normFit(mix, "crmn", factors="type", ncomp=2)
plot(nfit)

Accessor for the standards model

Description

Get the sFit

Usage

sFit(object, ...)

sFit(object, ...)

Arguments

Value

the sFit is only defined for CRMN

Author(s)

Henning Redestig

Show method for nFit

Description

Show some basic information for an nFit model

Usage

## S4 method for signature 'nFit'
show(object)

Arguments

Value

prints some basic information

Author(s)

Henning Redestig

Examples

data(mix)
normFit(mix, "avg")

Show nfit

Description

Show method for nFit

Usage

show_nfit(object)

Arguments

Value

prints some basic information

Author(s)

Henning Redestig

Accessor for the Internal Standards

Description

Subset an data set to only contain the labeled internal standards.

Usage

standards(object, standards=NULL, ...)

Arguments

Value

subsetted dataset

Author(s)

Henning Redestig

Examples

data(mix)
standards(mix)
standards(exprs(mix), fData(mix)$tag == 'IS')

Standards model

Description

Fit a model which describes the variation of the labeled internal standards from the biological factors.

Usage

standardsFit(object, factors, ncomp = NULL, lg = TRUE, fitfunc = lm, ...)

Arguments

Details

There is often unwanted variation in among the labeled internal standards which is related to the experimental factors due to overlapping peaks etc. This function fits a model that describes that overlapping variation using a scaled and centered PCA / multiple linear regression model. Scaling is done outside the PCA model.

Value

a list containing the PCA/MLR model, the recommended number of components for that model, the standard deviations and mean values and Q2/R2 for the fit.

Author(s)

Henning Redestig

See Also

makeX, standardsPred

Examples

data(mix)
sfit <- standardsFit(mix, "type", ncomp=3)
slplot(sfit$fit$pc)
## same thing
Y <- exprs(mix)
G <- model.matrix(~-1+mix$type)
isIS <- fData(mix)$tag == 'IS'
sfit <- standardsFit(Y, G, standards=isIS, ncomp=3)

Predict effect for new data (or get fitted data)

Description

Predicted values for the standards

Usage

standardsPred(model, newdata, factors, lg = TRUE, ...)

Arguments

Details

There is often unwanted variation in among the labeled internal standards which is related to the experimental factors due to overlapping peaks etc. This predicts this effect given a model of the overlapping variance. The prediction is given by \hat{X}_{IS}=X_{IS}-X_{IS}B

Value

the corrected data

Author(s)

Henning Redestig

See Also

makeX, standardsFit

Examples

data(mix)
fullFit <- standardsFit(mix, "type", ncomp=3)
sfit <- standardsFit(mix[,-1], "type", ncomp=3)
pred <- standardsPred(sfit, mix[,1], "type")
cor(scores(sfit$fit$pc)[1,], scores(fullFit$fit$pc)[1,])
## could just as well have been done as
Y <- exprs(mix)
G <- model.matrix(~-1+mix$type)
isIS <- fData(mix)$tag == 'IS'
fullFit <- standardsFit(Y, G, ncomp=3, standards=isIS)
sfit    <- standardsFit(Y[,-1], G[-1,], ncomp=3,
                        standards=isIS)
pred <- standardsPred(sfit, Y[,1,drop=FALSE], G[1,,drop=FALSE], standards=isIS)
cor(scores(sfit$fit$pc)[1,], scores(fullFit$fit$pc)[1,])

Accessor for the Internal Standards

Description

Subset an data set to only contain the labeled internal standards.

Usage

standards_eset(object, where = "tag", what = "IS", ...)

Arguments

Value

subsetted dataset

Author(s)

Henning Redestig

Examples

data(mix)
standards(mix)
fData(mix)$test <- fData(mix)$tag
standards(mix, where="test")

Accessor for the Internal Standards

Description

Subset an data set to only contain the labeled internal standards.

Usage

standards_other(object, standards, ...)

Arguments

Value

subsetted dataset

Author(s)

Henning Redestig

Examples

data(mix)
standards(exprs(mix), fData(mix)$tag == 'IS')

Normalize by sample weight

Description

Normalize samples by their weight (as in grams fresh weight)

Usage

weightnorm(object, weight = "weight", lg = FALSE)

Arguments

Details

Normalize each sample by dividing by the loaded sample weight. The weight argument is takes from the pheno data (or given as numerical vector with one value per sample). Missing values are not tolerated.

Value

the normalized expression set

Author(s)

Henning Redestig

Examples

data(mix)
w <- runif(ncol(mix),1, 1.3)
weightnorm(mix, w)