The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Generalized singular value decomposition for comparative analysis of genome-scale expression data sets of two different organisms
Open accessAbstract
We describe a comparative mathematical framework for two genome-scale expression data sets. This framework formulates expression as superposition of the effects of regulatory programs, biological processes, and experimental artifacts common to both data sets, as well as those that are exclusive to one data set or the other, by using generalized singular value decomposition. This framework enables comparative reconstruction and classification of the genes and arrays of both data sets. We illustrate this framework with a comparison of yeast and human cell-cycle expression data sets.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Generalized Single Value Decomposition (GSVD) is a unique type of differential co-expression analysis that relies on spectral decomposition to identify modules of co-regulated genes.”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [104]
Citations
Cites
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
Spellman, P. T., Sherlock, G. J., Zhang, M. Q., Iyer, V. R., Anders, K. R., Eisen, M. B., Brown, P. O., Botstein, D., & Futcher, B. (1998) · Molecular Biology of the Cell · doi:10.1091/mbc.9.12.3273 · Cited by 4,724
Self-citationSystematic determination of genetic network architecture
Tavazoie, S., Hughes, J. D., Campbell, M. J., Cho, R. J., & Church, G. M. (1999) · Nature Genetics · doi:10.1038/10343 · Cited by 2,509
Singular value decomposition for genome-wide expression data processing and modeling
Alter, O., Brown, P. O., & Botstein, D. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.18.10101 · Cited by 1,989
Self-citationWhitfield, M. L., Sherlock, G., Saldanha, A. J., Murray, J. I., Ball, C. A., Alexander, K. E., Matese, J. C., Perou, C. M., Hurt, M. M., Brown, P. O., et al. (2002) · Molecular Biology of the Cell · doi:10.1091/mbc.02-02-0030 · Cited by 1,527
Self-citationLarge-scale temporal gene expression mapping of central nervous system development
Wen, X., Fuhrman, S., Michaels, G. S., Carr, D. B., Smith, S. V., Barker, J. L., & Somogyi, R. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.1.334 · Cited by 645
Molecular characterisation of soft tissue tumours: a gene expression study
Nielsen, T. O., West, R. B., Linn, S. C., Alter, O., Knowling, M. A., OʼConnell, J. X., Zhu, S., Fero, M., Sherlock, G. J., Pollack, J. R., et al. (2002) · The Lancet · doi:10.1016/s0140-6736(02)08270-3 · Cited by 574
Self-citationTowards a Generalized Singular Value Decomposition
Paige, C. C., & Saunders, M. A. (1981) · SIAM Journal on Numerical Analysis · doi:10.1137/0718026 · Cited by 555
The Stanford Microarray Database
Sherlock, G. (2001) · Nucleic Acids Research · doi:10.1093/nar/29.1.152 · Cited by 528
Fundamental patterns underlying gene expression profiles: Simplicity from complexity
Holter, N. S., Mitra, M., Maritan, A., Cieplak, M., Banavar, J. R., & Fedoroff, N. V. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.150242097 · Cited by 475
Saccharomyces Genome Database (SGD) provides secondary gene annotation using the Gene Ontology (GO)
Dwight, S. S. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.69 · Cited by 375
Statistical analysis of array expression data as applied to the problem of tamoxifen resistance.
Hilsenbeck, S., Friedrichs, W., Schiff, R., O’Connell, P., Hansen, R., Osborne, C., & Fuqua, S. (1999) · Journal of the National Cancer Institute · doi:10.1093/jnci/91.5.453 · Cited by 205
Microarrays: Optical Technologies and Informatics
Bittner, M. L., Chen, Y., Dorsel, A. N., & Dougherty, E. R. (2001) · Cited by 84
Kar4p, a Karyogamy-Specific Component of the Yeast Pheromone Response Pathway
Kurihara, L. J., Stewart, B. G., Gammie, A. E., & Rose, M. D. (1996) · Molecular and Cellular Biology · doi:10.1128/mcb.16.8.3990 · Cited by 61
Flagged first, then by citations
Research works printed but not linked: 2
- Raychaudhuri S. Stuart J. M. & Altman R. B. (2000) in Proceedings of the Pacific Symposium on Biocomputing eds. Altman R. B. Lauderdale K. Dunker A. K. Hunter L. & Klein T. E. (World Scientific Singapore) pp. 455.
- Golub G. H. & Van Loan C. F. (1996) Matrix Computation (Johns Hopkins Univ. Press Baltimore).
Cited by
Sorted by citations
TopHat: discovering splice junctions with RNA-Seq
Trapnell, C., Pachter, L. S., & Salzberg, S. L. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp120 · Cited by 11,669
A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules
Stuart, J. M., Segal, E., Koller, D., & Kim, S. K. (2003) · Science · doi:10.1126/science.1087447 · Cited by 2,274
Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects
Lahat, D., Adali, T., & Jutten, C. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2460697 · Cited by 1,296
Gene co-expression analysis for functional classification and gene–disease predictions
van Dam, S., Võsa, U., van der Graaf, A., Franke, L., & de Magalhães, J. P. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbw139 · Cited by 995
From big data analysis to personalized medicine for all: challenges and opportunities
Alyass, A., Turcotte, M., & Meyre, D. (2015) · BMC Medical Genomics · doi:10.1186/s12920-015-0108-y · Cited by 575
Metadata
- Authors
- Alter, O., Brown, P. O., & Botstein, D.
- Affiliations
- Stanford University (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2003-03-11
- Volume
- 100
- Issue
- 6
- Pages
- 3351-3356
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC152296
- OpenAlex
- W2129256542
- Cited by
- 304
- References
- 14
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 2 · 2023: 6 · 2022: 5 · 2021: 8 · 2020: 15 · 2019: 13 · 2018: 9 · 2017: 9 · 2016: 16 · 2015: 10 · 2014: 29 · 2013: 13 · 2012: 8
- FWCI
- 7.58
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Human Genome Research Institute (5 K01 HG00038-01, K01HG000038)
- National Cancer Institute (CA85129, CA 77097, R01CA077097, U01CA085129)
- National Institute of General Medical Sciences (GM46406, R37GM046406, R01GM046406)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- generalized singular value decomposition, regulatory programs, biological processes, experimental artifacts, gene classification, yeast cell cycle, cross-species comparison
- MeSH terms
- Cell Cycle, Data Interpretation, Statistical, Genes, Fungal, Humans, Pheromones, RNA, Fungal, RNA, Messenger, Saccharomyces cerevisiae, Stress, Physiological, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Genomics, Databases, Genetic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
