scholarly journals Integrated Analysis of Gene Expression and Copy Number Data on Gene Shaving Using Independent Component Analysis

2011 ◽  
Vol 8 (6) ◽  
pp. 1568-1579 ◽  
Author(s):  
Jinhua Sheng ◽  
Hong-Wen Deng ◽  
Vince D. Calhoun ◽  
Yu-Ping Wang
Keyword(s):  
Gene Expression ◽  
Independent Component Analysis ◽  
Copy Number ◽  
Component Analysis ◽  
Independent Component ◽  
Integrated Analysis ◽  
Copy Number Data ◽  
Gene Shaving

scholarly journals Gene Expression Data Classification Using Consensus Independent Component Analysis

2008 ◽  
Vol 6 (2) ◽  
pp. 74-82 ◽  
Author(s):  
Chun-Hou Zheng ◽  
De-Shuang Huang ◽  
Xiang-Zhen Kong ◽  
Xing-Ming Zhao
Keyword(s):  
Gene Expression ◽  
Independent Component Analysis ◽  
Gene Expression Data ◽  
Data Classification ◽  
Component Analysis ◽  
Independent Component ◽  
Expression Data

scholarly journals Independent component analysis of Alzheimer's DNA microarray gene expression data

2009 ◽  
Vol 4 (1) ◽  
pp. 5 ◽  
Author(s):  
Wei Kong ◽  
Xiaoyang Mou ◽  
Qingzhong Liu ◽  
Zhongxue Chen ◽  
Charles R Vanderburg ◽  
...  
Keyword(s):  
Gene Expression ◽  
Independent Component Analysis ◽  
Dna Microarray ◽  
Gene Expression Data ◽  
Component Analysis ◽  
Microarray Gene Expression Data ◽  
Independent Component ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Microarray Gene

Molecular diagnosis of human cancer type by gene expression profiles and independent component analysis

2005 ◽  
Vol 13 (12) ◽  
pp. 1303-1311 ◽  
Author(s):  
Xue Wu Zhang ◽  
Yee Leng Yap ◽  
Dong Wei ◽  
Feng Chen ◽  
Antoine Danchin
Keyword(s):  
Gene Expression ◽  
Independent Component Analysis ◽  
Molecular Diagnosis ◽  
Human Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Component Analysis ◽  
Independent Component ◽  
Cancer Type

Robust Preprocessing of Gene Expression Microarrays for Independent Component Analysis

2006 ◽  
pp. 714-721 ◽  
Author(s):  
Pedro Gómez Vilda ◽  
Francisco Díaz ◽  
Rafael Martínez ◽  
Raul Malutan ◽  
Victoria Rodellar ◽  
...  
Keyword(s):  
Gene Expression ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Gene Expression Microarrays ◽  
Expression Microarrays

scholarly journals A review of independent component analysis application to microarray gene expression data

BioTechniques ◽  
2008 ◽  
Vol 45 (5) ◽  
pp. 501-520 ◽  
Author(s):  
Wei Kong ◽  
Charles R. Vanderburg ◽  
Hiromi Gunshin ◽  
Jack T. Rogers ◽  
Xudong Huang
Keyword(s):  
Gene Expression ◽  
Independent Component Analysis ◽  
Gene Expression Data ◽  
Component Analysis ◽  
Microarray Gene Expression Data ◽  
Independent Component ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Analysis Application ◽  
Microarray Gene

scholarly journals Granular Transcriptomic Signatures Derived from Independent Component Analysis of Bulk Nervous Tissue for Studying Labile Brain Physiologies

2020 ◽  
Author(s):  
Zeid M Rusan ◽  
Michael P Cary ◽  
Roland J Bainton
Keyword(s):  
Gene Expression ◽  
Independent Component Analysis ◽  
Developmental Stages ◽  
Gene Expression Signature ◽  
Tissue Microarrays ◽  
Cell Types ◽  
Component Analysis ◽  
Independent Component ◽  
Cell Type ◽  
Gene Expression Signatures

AbstractMulticellular organisms employ concurrent gene regulatory programs to control development and physiology of cells and tissues. The Drosophila melanogaster model system has a remarkable history of revealing the genes and mechanisms underlying fundamental biology yet much remains unclear. In particular, brain xenobiotic protection and endobiotic regulatory systems that require transcriptional coordination across different cell types, operating in parallel with the primary nervous system and metabolic functions of each cell type, are still poorly understood. Here we use the unsupervised machine learning method independent component analysis (ICA) on majority fresh-frozen, bulk tissue microarrays to define biologically pertinent gene expression signatures which are sparse, i.e. each involving only a fraction of all fly genes. We optimize the gene expression signature definitions partly through repeated application of a stochastic ICA algorithm to a compendium of 3,346 microarrays from 221 experiments provided by the Drosophila research community. Our optimized ICA model of pan fly gene expression consists of 850 modules of co-regulated genes that map to tissue developmental stages, disease states, cell-autonomous pathways and presumably novel processes. Importantly, we show biologically relevant gene modules expressed at varying amplitudes in whole brain and isolated adult blood-brain barrier cell levels. Thus, whole tissue derived ICA transcriptional signatures that transcend single cell type boundaries provide a window into the transcriptional states of difficult to isolate cell ensembles maintaining delicate brain physiologies. We believe the fly ICA gene expression signatures set, by virtue of the success of ICA at inferring robust often low amplitude patterns across large datasets and the quality of the input samples, to be an important asset for analyzing compendium and newly generated microarray or RNA-seq expression datasets.

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