mdqc: MDQC: Mahalanobis Distance Quality Control

In mdqc: Mahalanobis Distance Quality Control for microarrays

Description

MDQC is a multivariate quality assessment method for microarrays based on quality control (QC) reports.

Usage

mdqc(x, method=c("nogroups", "apriori", "global", "cluster", "loading"),
     groups=NULL, k=NULL, pc=NULL,
     robust=c("S-estimator","MCD", "MVE"), nsamp=10*nrow(x))

Arguments

x	a numeric matrix or data frame containing the quality measures (columns) for each array (rows). The number of rows must exceed the number of columns.
method	The Mahalanobis Distances (MDs) can be computed on all the quality measures in the QC report (this is the default method given by method="nogroups"), on the first k principal components resulting from a principal component analysis (PCA) of the QC report ("global") or on subsets of quality measures in the QC report ("apriori": groups defined by the user, "cluster": groups resulting from a cluster analysis, or "loading": groups resulting from a cluster analysis in the space of the loadings of a PCA). While the first two methods compute a single MD for each array, the last three compute one MD within each created group of quality measures.
groups	A list to specify the groups of quality measures when the “apriori” method is chosen. E.g. groups = list(c(1,2), c(4,6)) puts column 1,2 as one group and 4,6 as a second.
k	An integer to specify the number of clusters (or groups) to be used in the cluster analysis when “cluster” or “loading” methods are chosen.
pc	An integer to specify the number of principal components analyzed from the PCA when “global” or “loading” methods are chosen.
robust	A robust multivariate location/spread estimator (choice of S-estimator, MCD or MVE). The default method uses S-estimators with a 25% breakdown point.
nsamp	The number of subsamples that the robust estimator should use. This defaults to 10 times the number of rows in the matrix.

Details

MDQC flags potentially low quality arrays based on the idea of outlier detection, that is, it flags those arrays whose quality attributes jointly depart from those of the bulk of the data.

This function computes a distance measure, the Mahalanobis Distance, to summarize the quality of each array. The use of this distance allows us to perform a multivariate analysis of the information in QC reports taking the correlation structure of the quality measures into account. In addition, by using robust estimators to identify the typical quality measures of good-quality arrays, the evaluation is not affected by the measures of outlying arrays.

MDQC can be based on all the quality measures simultaneously (using method="nogroups"), on subsets of them (using method="apriori", "cluster", or "loading"), or on a transformed space with a lower dimension (using method="global").

In the “apriori” approach the user forms groups of quality measures on the basis of an a priori interpretation of them and according to the quality aspect they represent. The “cluster” and the “loading” methods are two data-driven methods to form the groups. The former groups the quality measures using clustering analysis, and the latter uses the loadings of a principal component analysis to identify the quality measures that contain similar information and group them. It is important to note that the “apriori”, the “cluster”, and the “loading” methods create groups of the original quality measures of the report and compute one MD within each group. Finally, the “global” method computes a single MD based on the reduced space of the first k principal components from a robust PCA. The number k of PCs can be chosen using a scree plot.

More details on each method are given in Cohen Freue et al. (2007)

Value

An object of class ‘“mdqc”’ (with associated plot, print and summary methods) with components

ngroups	Number of groups in which the MDs have been computed
groups	column numbers corresponding to the quality measures in each group
mdqcValues	Mahalanobis Distance(s) for each array
x	dataset containing the numeric quality measures in the report
method	method used to group or transform the quality measures before computing the MD for each array
pc	number of principal components used in the robust PCA.
k	number of clusters used in the cluster analysis.

Note

We thank Christopher Croux for providing us a MATLAB code that we translated into R to compute the multivariate S-estimator

Author(s)

Justin Harrington [e-mail] and Gabriela V. Cohen Freue [e-mail].

References

Cohen Freue, G. V. and Hollander, Z. and Shen, E. and Zamar, R. H. and Balshaw, R. and Scherer, A. and McManus, B. and Keown, P. and McMaster, W. R. and Ng, R. T. (2007) ‘MDQC: A New Quality Assessment Method for Microarrays Based on Quality Control Reports’. Bioinformatics 23, 3162 – 3169.

Bolstad, B. M. and Collin, F. and Brettschneider, J. and Simpson, K. and Cope, L. and Irizarry R. A. and Speed T. P. (2005) ‘Quality assessment of Affymetrix GeneChip data.’ In Gentleman R. and Carey C. J. and Huber W. and Irizarry R. A. and Dudoit S. Bioinformatics and Computational Biology Solutions Using R and Bioconductor. New York: Springer.

Brettschneider, J. and Collin, F. and Bolstad, B. M. and Speed, T. P. (2007) ‘Quality assessment for short oligonucleotide arrays’. Forthcoming in Technometrics (with Discussion).

Ross, M. E. and Zhou, X. and Song, G. and Shurtleff, S. A. and Girtman, K. and Williams, W. K. and Liu, H. and Mahfouz, R. and Raimondi, S. C. and Lenny, N. and Patel, A. and Downing, J. R. (2003) ‘Classification of pediatric acute lymphoblastic leukemia by gene expression profiling.’ Blood 102, 2951–9.

See Also

prcomp.robust,pam, mahalanobis, allQC

Examples

data(allQC)

## Contains the QC report obtained using Bioconductor's simpleaffy package
## for a subset of arrays from a large acute lymphoblastic leukemia (ALL)
## study (Ross et al., 2004).
## This dataset has been also studied by Bolstad et al. (2005) and
## Brettschneider et al. (2007).
## For further information see allQC.


#### No Groups method
# Figure 2 in Cohen Freue et al. (2007):
# Results of MDQC based on all measures of the QC report.

mdout <- mdqc(allQC, method="nogroups")
plot(mdout)
print(mdout)
summary(mdout)

#### A-Priori grouping method
# Figure 3 in Cohen Freue et al. (2007):
# Results of MDQC using the apriori grouping method.

mdout <- mdqc(allQC, method="apriori", groups=list(1:5, 6:9, 10:11))
plot(mdout)



#### Global PCA method
# Figure 4 in Cohen Freue et al.(2007):
# Results of MDQC using the global PCA method.

mdout <- mdqc(allQC, method="global", pc=4)
plot(mdout)



#### Clustering grouping method
# Figure 4 in Supplementary Material of Cohen Freue et al. (2007):
# Results of MDQC using a cluster analysis to form
# 3 groups of quality measures.

mdout <- mdqc(allQC, method="cluster", k=3)
plot(mdout)



#### Loading grouping method
# Figure 4 in Supplementary Material of Cohen Freue et al. (2007):
# Results of MDQC using a cluster analysis on the first
# k=4 loading vectors from a robust PCA to form 3 groups of quality measures.

mdout <- mdqc(allQC, method="loading", k=3, pc=4)
plot(mdout)


### To get the raw MD distances
mdout$mdqcValues

mdqc documentation built on Nov. 8, 2020, 11 p.m.