scholarly journals Cascaded Factor Analysis and Wavelet Transform Method for Tumor Classification Using Gene Expression Data

2012 ◽  
Vol 4 (9) ◽  
pp. 73-79
Author(s):  
Jayakishan Meher ◽  
Ram Chandra Barik ◽  
Madhab Ranjan Panigrahi ◽  
Saroj Kumar Pradhan ◽  
Gananath Dash
Keyword(s):  
Gene Expression ◽  
Factor Analysis ◽  
Wavelet Transform ◽  
Gene Expression Data ◽  
Tumor Classification ◽  
Expression Data ◽  
Transform Method

scholarly journals Optimization Based Tumor Classification from Microarray Gene Expression Data

PLoS ONE ◽  
2011 ◽  
Vol 6 (2) ◽  
pp. e14579 ◽  
Author(s):  
Onur Dagliyan ◽  
Fadime Uney-Yuksektepe ◽  
I. Halil Kavakli ◽  
Metin Turkay
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Microarray Gene Expression Data ◽  
Tumor Classification ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Microarray Gene

scholarly journals Pathway based factor analysis of gene expression data produces highly heritable phenotypes that associate with age

2015 ◽  
Author(s):  
Andrew Anand Brown ◽  
Zhihao Ding ◽  
Ana Viñuela ◽  
Dan Glass ◽  
Leopold Parts ◽  
...  
Keyword(s):  
Gene Expression ◽  
Factor Analysis ◽  
Gene Expression Data ◽  
Biological Knowledge ◽  
Expression Data ◽  
Expression Levels ◽  
Biologically Relevant ◽  
Kegg Pathways ◽  
Analysis Methods ◽  
Gene Expression Levels

Statistical factor analysis methods have previously been used to remove noise components from high dimensional data prior to genetic association mapping, and in a guided fashion to summarise biologically relevant sources of variation. Here we show how the derived factors summarising pathway expression can be used to analyse the relationships between expression, heritability and ageing. We used skin gene expression data from 647 twins from the MuTHER Consortium and applied factor analysis to concisely summarise patterns of gene expression, both to remove broad confounding influences and to produce concise pathway-level phenotypes. We derived 930 "pathway phenotypes" which summarised patterns of variation across 186 KEGG pathways (five phenotypes per pathway). We identified 69 significant associations of age with phenotype from 57 distinct KEGG pathways at a stringent Bonferroni threshold (P<5.38E-5). These phenotypes are more heritable (h^2=0.32) than gene expression levels. On average, expression levels of 16% of genes within these pathways are associated with age. Several significant pathways relate to metabolising sugars and fatty acids, others with insulin signalling. We have demonstrated that factor analysis methods combined with biological knowledge can produce more reliable phenotypes with less stochastic noise than the individual gene expression levels, which increases our power to discover biologically relevant associations. These phenotypes could also be applied to discover associations with other environmental factors.

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