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HO GSVD specifications

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Unique identifier OMICS_14916
Name HO GSVD
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Python
Computer skills Advanced
Stability Stable
Maintained Yes

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  • person_outline Orly Alter

Publication for HO GSVD

HO GSVD citations

 (5)
library_books

Identification of cancer prognosis associated functional modules using differential co expression networks

2017
Oncotarget
PMCID: 5762563
PMID: 29348878
DOI: 10.18632/oncotarget.22878

[…] candidate gene set. We then converted candidate gene IDs to the corresponding DAVID gene IDs and removed the non-mapped genes. The gene expression matrices of multiple cancers were decomposed by the HO-GSVD method for identifying the common modules across different cancers. We chose the vectors with top eigenvalues in the right basis matrix as candidates of co-expression genes. The co-expression […]

library_books

Integrative clustering of multi level ‘omic data based on non negative matrix factorization algorithm

2017
PLoS One
PMCID: 5411077
PMID: 28459819
DOI: 10.1371/journal.pone.0176278

[…] us local minimums using several initializations of W and Hi and then choosing the one for which objective function Q with the smallest value.The higher order generalized singular value decomposition (HO GSVD) [] and its variants [, ] have also been proposed for integrative matrix factorization. These methods are the extensions of singular value decomposition (SVD). SVD factorizes the matrix X into […]

library_books

Matrix factorization reveals aging specific co expression gene modules in the fat and muscle tissues in nonhuman primates

2016
Sci Rep
PMCID: 5050522
PMID: 27703186
DOI: 10.1038/srep34335

[…] ues, including PI3K/AKT signalling, ERK/MAPK signalling, and calcium signalling, might represent the potential targets in the treatment of skeletal disorder.Our ICEGM approach simplified the previous HO-GSVD mathematical framework. Previous HO-GSVD mathematical framework has been used to identify the either ‘common’ or ‘differential’ clusters in multiple datasets. While here, we put more attention […]

library_books

Multi tissue Analysis of Co expression Networks by Higher Order Generalized Singular Value Decomposition Identifies Functionally Coherent Transcriptional Modules

2014
PLoS Genet
PMCID: 3879165
PMID: 24391511
DOI: 10.1371/journal.pgen.1004006

[…] by its expression profile and represented by a data point in a dimensional space. The observations from all datasets are contained in a subspace of dimension , which thereafter is referred to as the HO GSVD subspace. Here, we aim at finding directions in the HO GSVD subspace that either capture the variability in gene expression that is common to all conditions (common factors) or that is specifi […]

library_books

GSVD Comparison of Patient Matched Normal and Tumor aCGH Profiles Reveals Global Copy Number Alterations Predicting Glioblastoma Multiforme Survival

2012
PLoS One
PMCID: 3264559
PMID: 22291905
DOI: 10.1371/journal.pone.0030098

[…] gulation , , and demonstrate that GSVD modeling of DNA microarray data can be used to correctly predict previously unknown cellular mechanisms.Recently, we mathematically defined a higher-order GSVD (HO GSVD) for more than two large-scale matrices with different row dimensions and the same column dimension . We proved that this novel HO GSVD extends to higher orders almost all of the mathematical […]


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HO GSVD institution(s)
Department of Electrical and Computer Engineering, University of Texas at Austin, TX, USA; Department of Management Science and Engineering, Stanford University, Stanford, CA, USA; Department of Computer Science, Cornell University, Ithaca, NY, USA; Scientific Computing and Imaging (SCI) Institute and Departments of Bioengineering and Human Genetics, University of Utah, Salt Lake City, UT, USA
HO GSVD funding source(s)
This work was supported by Office of Naval Research Grant N00014-02-1-0076, National Science Foundation Grant DMS-1016284, as well as the Utah Science Technology and Research (USTAR) Initiative, National Human Genome Research Institute R01 Grant HG-004302 and National Science Foundation CAREER Award DMS-0847173.

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