als: alternating least squares multivariate curve resolution...

Description Usage Arguments Value Note References See Also Examples

Description

This is an implementation of alternating least squares multivariate curve resolution (MCR-ALS). Given a dataset in matrix form d1, the dataset is decomposed as d1=C %*% t(S) where the columns of C and S represent components contributing to the data in each of the 2-ways that the matrix is resolved. In forming the decomposition, the components in each way many be constrained with e.g., non-negativity, uni-modality, selectivity, normalization of S and closure of C. Note that if more than one dataset is to be analyzed simultaneously, then the matrix S is assumed to be the same for every dataset in the bilinear decomposition of each dataset into matrices C and S.

Usage

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als(CList, PsiList, S=matrix(), WList=list(),
thresh =.001, maxiter=100, forcemaxiter = FALSE,
optS1st=TRUE, x=1:nrow(CList[[1]]), x2=1:nrow(S),
baseline=FALSE, fixed=vector("list", length(PsiList)),
uniC=FALSE, uniS=FALSE, nonnegC = TRUE, nonnegS = TRUE,
normS=0, closureC=list())

Arguments

CList

list with the same length as PsiList where each element is a matrix of dimension m by comp and represents the matrix C for each dataset

PsiList

list of datasets, where each dataset is a matrix of dimension m by n

S

matrix with n rows and comp columns, often representing (mass) spectra

WList

An optional list with the same length as PsiList, where each element is a matrix of dimension m by n giving the weight of that datapoint; note that if closure or normalization constraints are applied, then both are applied after the application of weights.

thresh

numeric value that defaults to .001; if ((oldrss - rss) / oldrss) < thresh then the optimization stops, where oldrss is the residual sum of squares at iteration x-1 and rss is the residual sum of squares at iteration x

maxiter

The maximum number of iterations to perform (where an iteration is optimization of either AList and C)

forcemaxiter

Logical indicating whether maxiter iterations should be performed even if the residual difference drops below thresh.

optS1st

logical indicating whether the first constrained least squares regression should estimate S or CList.

x

optional vector of labels for the rows of C, which are used in the application of unimodality constraints.

x2

optional vector of labels for the rows of S, which are used in the application of unimodality constraints.

baseline

logical indicating whether a baseline component is present; if baseline=TRUE then this component is exempt from constraints unimodality or non-negativity

fixed

list with the same length as PsiList in which each element is a vector of the indices of the components to fix to zero in each dataset

nonnegS

logical indicating whether the components (columns) of the matrix S should be constrained to non-negative values

nonnegC

logical indicating whether the components (columns) of the matrix C should be constrained to non-negative values

uniC

logical indicating whether unimodality constraints should be applied to the columns of C

uniS

logical indicating whether unimodality constraints should be applied to the columns of S

normS

numeric indicating whether the spectra are normalized; if normS>0, the spectra are normalized. If normS==1 the maximum of the spectrum of each component is constrained to be equal to one; if normS > 0 && normS!=1 then the norm of the spectrum of each component is constrained to be equal to one.

closureC

list; if the length is zero, then no closure constraints are applied. If the length is not zero, it should be equal to the number of datasets in the analysis, and contain numeric vectors consisting of the desired value of the sum of each row of the concentration matrix.

Value

A list with components:

CList

A list with the same length as the number of datasets, containing the optimized matrix C at termination scaled by the optimized amplitudes for that dataset from AList.

S

The matrix S given as input.

rss

The residual sum of squares at termination.

resid

A list with the same length as the number of datasets, containing the residual matrix for each dataset

iter

The number of iterations performed before termination.

Note

This function was used to solve problems described in

van Stokkum IHM, Mullen KM, Mihaleva VV. Global analysis of multiple gas chromatography-mass spectrometry (GS/MS) data sets: A method for resolution of co-eluting components with comparison to MCR-ALS. Chemometrics and Intelligent Laboratory Systems 2009; 95(2): 150-163.

in conjunction with the package TIMP. For the code to reproduce the examples in this paper, see examples_chemo.zip included in the inst directory of the package source code. .

References

Garrido M, Rius FX, Larrechi MS. Multivariate curve resolution alternating least squares (MCR-ALS) applied to spectroscopic data from monitoring chemical reactions processes. Journal Analytical and Bioanalytical Chemistry 2008; 390:2059-2066.

Jonsson P, Johansson A, Gullberg J, Trygg J, A J, Grung B, Marklund S, Sjostrom M, Antti H, Moritz T. High-throughput data analysis for detecting and identifying differences between samples in GC/MS-based metabolomic analyses. Analytical Chemistry 2005; 77:5635-5642.

Tauler R. Multivariate curve resolution applied to second order data. Chemometrics and Intelligent Laboratory Systems 1995; 30:133-146.

Tauler R, Smilde A, Kowalski B. Selectivity, local rank, three-way data analysis and ambiguity in multivariate curve resolution. Journal of Chemometrics 1995; 9:31-58.

See Also

matchFactor,multiex,multiex1, plotS

Examples

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## load 2 matrix datasets into variables d1 and d2
## load starting values for elution profiles
## into variables Cstart1 and Cstart2
## load time labels as x, m/z values as x2
data(multiex)

## starting values for elution profiles
matplot(x,Cstart1,type="l")
matplot(x,Cstart2,type="l",add=TRUE)

## using MCR-ALS, improve estimates for mass spectra S and the two
## matrices of elution profiles
## apply unimodality constraints to the elution profile estimates
## note that the starting estimates for S just contain a dummy matrix

test0 <- als(CList=list(Cstart1,Cstart2),S=matrix(1,nrow=400,ncol=2),
PsiList=list(d1,d2), x=x, x2=x2, uniC=TRUE, normS=0)

## plot the estimated mass spectra 
plotS(test0$S,x2)

## the known mass spectra are contained in the variable S
## can compare the matching factor of each estimated spectrum to
## that in S
matchFactor(S[,1],test0$S[,1])
matchFactor(S[,2],test0$S[,2])
 
## plot the estimated elution profiles
## this shows the relative abundance of the 2nd component is low 
matplot(x,test0$CList[[1]],type="l")
matplot(x,test0$CList[[2]],type="l",add=TRUE)

Example output

Loading required package: nnls
Loading required package: Iso
Iso 0.0-17
Initial RSS 3.039967e+13 
Iteration (opt. S): 1, RSS: 1.330703e+12, RD: 0.9562264
Iteration (opt. C): 2, RSS: 153488187, RD: 0.9998847
Iteration (opt. S): 3, RSS: 102433454, RD: 0.3326297
Iteration (opt. C): 4, RSS: 102351694, RD: 0.0007981757
Initial RSS / Final RSS = 3.039967e+13 / 102351694 = 297011.9 
          [,1]
[1,] 0.9999994
          [,1]
[1,] 0.9999917

ALS documentation built on May 2, 2019, 6:14 a.m.

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