SVDMAN--singular value decomposition analysis of microarray data
- PMID: 11395437
- DOI: 10.1093/bioinformatics/17.6.566
SVDMAN--singular value decomposition analysis of microarray data
Abstract
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 W N(-1/2), where N is the number of genes, and W 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.
Similar articles
-
Resampling methods for variance estimation of singular value decomposition analyses from microarray experiments.Funct Integr Genomics. 2002 Aug;2(3):92-7. doi: 10.1007/s10142-002-0047-5. Epub 2002 Feb 14. Funct Integr Genomics. 2002. PMID: 12185456
-
Identification of nutrient partitioning genes participating in rice grain filling by singular value decomposition (SVD) of genome expression data.BMC Genomics. 2003 Jul 10;4(1):26. doi: 10.1186/1471-2164-4-26. BMC Genomics. 2003. PMID: 12854976 Free PMC article.
-
DNA microarray data imputation and significance analysis of differential expression.Bioinformatics. 2005 Nov 15;21(22):4155-61. doi: 10.1093/bioinformatics/bti638. Epub 2005 Aug 23. Bioinformatics. 2005. PMID: 16118262
-
Basic microarray analysis: grouping and feature reduction.Trends Biotechnol. 2001 May;19(5):189-93. doi: 10.1016/s0167-7799(01)01599-2. Trends Biotechnol. 2001. PMID: 11301132 Review.
-
Clustering microarray data.Methods Enzymol. 2006;411:194-213. doi: 10.1016/S0076-6879(06)11010-1. Methods Enzymol. 2006. PMID: 16939791 Review.
Cited by
-
bioNMF: a versatile tool for non-negative matrix factorization in biology.BMC Bioinformatics. 2006 Jul 28;7:366. doi: 10.1186/1471-2105-7-366. BMC Bioinformatics. 2006. PMID: 16875499 Free PMC article.
-
Associating phenotypes with molecular events: recent statistical advances and challenges underpinning microarray experiments.Funct Integr Genomics. 2006 Jan;6(1):1-13. doi: 10.1007/s10142-005-0006-z. Epub 2005 Nov 15. Funct Integr Genomics. 2006. PMID: 16292543 Review.
-
Mining gene expression data by interpreting principal components.BMC Bioinformatics. 2006 Apr 7;7:194. doi: 10.1186/1471-2105-7-194. BMC Bioinformatics. 2006. PMID: 16600052 Free PMC article.
-
Probabilistic assembly of human protein interaction networks from label-free quantitative proteomics.Proc Natl Acad Sci U S A. 2008 Feb 5;105(5):1454-9. doi: 10.1073/pnas.0706983105. Epub 2008 Jan 24. Proc Natl Acad Sci U S A. 2008. PMID: 18218781 Free PMC article.
-
Disentangling information flow in the Ras-cAMP signaling network.Genome Res. 2006 Apr;16(4):520-6. doi: 10.1101/gr.4473506. Epub 2006 Mar 13. Genome Res. 2006. PMID: 16533914 Free PMC article.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources
Molecular Biology Databases