scholarly journals Extracting characteristic patterns from genome - wide expression data by non - negative matrix factorization

2004 ◽  
Author(s):  
Nini Rao ◽  
S.J. Shepherd
Keyword(s):  
Matrix Factorization ◽  
Expression Data ◽  
Genome Wide ◽  
Genome Wide Expression ◽  
Non Negative Matrix Factorization

scholarly journals Use of Genome-Wide Expression Data to Mine the “Gray Zone” of GWA Studies Leads to Novel Candidate Obesity Genes

PLoS Genetics ◽  
2010 ◽  
Vol 6 (6) ◽  
pp. e1000976 ◽  
Author(s):  
Jussi Naukkarinen ◽  
Ida Surakka ◽  
Kirsi H. Pietiläinen ◽  
Aila Rissanen ◽  
Veikko Salomaa ◽  
...  
Keyword(s):  
Expression Data ◽  
Gray Zone ◽  
Genome Wide ◽  
Obesity Genes ◽  
Gwa Studies ◽  
Genome Wide Expression

scholarly journals Stage prediction of embryonic stem cell differentiation from genome-wide expression data

2011 ◽  
Vol 27 (18) ◽  
pp. 2546-2553 ◽  
Author(s):  
Lan Zagar ◽  
Francesca Mulas ◽  
Silvia Garagna ◽  
Maurizio Zuccotti ◽  
Riccardo Bellazzi ◽  
...  
Keyword(s):  
Stem Cell ◽  
Cell Differentiation ◽  
Embryonic Stem Cell ◽  
Embryonic Stem ◽  
Stem Cell Differentiation ◽  
Embryonic Stem Cell Differentiation ◽  
Expression Data ◽  
Genome Wide ◽  
Genome Wide Expression

scholarly journals Non-Negative Matrix Factorization for the Analysis of Complex Gene Expression Data: Identification of Clinically Relevant Tumor Subtypes

2008 ◽  
Vol 6 ◽  
pp. CIN.S606 ◽  
Author(s):  
Attila Frigyesi ◽  
Mattias Höglund
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Matrix Factorization ◽  
Biological Significance ◽  
Data Sets ◽  
Expression Data ◽  
Microarray Expression Data ◽  
Tumor Subtypes ◽  
Gene Sets ◽  
Non Negative Matrix Factorization

Non-negative matrix factorization (NMF) is a relatively new approach to analyze gene expression data that models data by additive combinations of non-negative basis vectors (metagenes). The non-negativity constraint makes sense biologically as genes may either be expressed or not, but never show negative expression. We applied NMF to five different microarray data sets. We estimated the appropriate number metagens by comparing the residual error of NMF reconstruction of data to that of NMF reconstruction of permutated data, thus finding when a given solution contained more information than noise. This analysis also revealed that NMF could not factorize one of the data sets in a meaningful way. We used GO categories and pre defined gene sets to evaluate the biological significance of the obtained metagenes. By analyses of metagenes specific for the same GO-categories we could show that individual metagenes activated different aspects of the same biological processes. Several of the obtained metagenes correlated with tumor subtypes and tumors with characteristic chromosomal translocations, indicating that metagenes may correspond to specific disease entities. Hence, NMF extracts biological relevant structures of microarray expression data and may thus contribute to a deeper understanding of tumor behavior.

scholarly journals Viewing RNA-seq data on the entire human genome

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 596 ◽  
Author(s):  
Eric M. Weitz ◽  
Lorena Pantano ◽  
Jingzhi Zhu ◽  
Bennett Upton ◽  
Ben Busby
Keyword(s):  
Web Application ◽  
Source Code ◽  
Gene Expression Omnibus ◽  
Expression Data ◽  
Rna Seq ◽  
Sequence Read Archive ◽  
Data Pipeline ◽  
Genome Wide ◽  
Small Team ◽  
Genome Wide Expression

RNA-Seq Viewer is a web application that enables users to visualize genome-wide expression data from NCBI’s Sequence Read Archive (SRA) and Gene Expression Omnibus (GEO) databases. The application prototype was created by a small team during a three-day hackathon facilitated by NCBI at Brandeis University. The backend data pipeline was developed and deployed on a shared AWS EC2 instance. Source code is available at https://github.com/NCBI-Hackathons/rnaseqview.

scholarly journals Comparative proteomics and global genome-wide expression data implicate role of ARMC8 in lung cancer

2015 ◽  
Vol 16 (9) ◽  
pp. 3691-3696 ◽  
Author(s):  
Asif Amin ◽  
Shoiab Bukhari ◽  
Taseem A Mokhdomi ◽  
Naveed Anjum ◽  
Asrar H Wafai ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Comparative Proteomics ◽  
Expression Data ◽  
Genome Wide ◽  
Genome Wide Expression
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