Bioinformatics Vol. 17 no. 6 2001
Pages 566-568
© 2001 Oxford University Press
Applications Note |
SVDMAN—singular value decomposition analysis of microarray data
Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
Received on October 23, 2000
; revised on January 26, 2001
; accepted on March 7, 2001
Summary: We have developed two novel methods for Singular
Value Decomposition analysis (SVD) of microarray data. The first is a
threshold-based method for obtaining gene groups, and the second is a
method for obtaining a measure of confidence in SVD analysis. Gene
groups are obtained by identifying elements of the left singular
vectors, or gene coefficient vectors, that are greater in magnitude
than the threshold 
, where 
is
the number of genes, and 
is a weight factor whose default
value is 3. The groups are non-exclusive and may contain genes of
opposite (i.e. inversely correlated) regulatory response. The
confidence measure is obtained by systematically deleting assays from
the data set, interpolating the SVD of the reduced data set to
reconstruct the missing assay, and calculating the Pearson
correlation between the reconstructed assay and the original data.
This confidence measure is applicable when each experimental assay
corresponds to a value of parameter that can be interpolated, such as
time, dose or concentration. Algorithms for the grouping method and
the confidence measure are available in a software application called
SVD Microarray ANalysis (SVDMAN). In addition to calculating the SVD
for generic analysis, SVDMAN provides a new means for using
microarray data to develop hypotheses for gene associations and
provides a measure of confidence in the hypotheses, thus extending
current SVD research in the area of global gene expression
analysis.
Availability: ftp://bpublic.lanl.gov/compbio/software
Contact: brettin{at}lanl.gov
Supplementary information: http://home.lanl.gov/svdman
* To whom correspondence should be addressed.
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