scholarly journals Evaluation of Different Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes

Microarrays ◽  
2013 ◽  
Vol 2 (2) ◽  
pp. 131-152 ◽  
Author(s):  
Daniel Johnstone ◽  
Carlos Riveros ◽  
Moones Heidari ◽  
Ross Graham ◽  
Debbie Trinder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Microarray Data ◽  
Gene Expression Microarray ◽  
Expression Microarray ◽  
Gene Expression Microarray Data

scholarly journals Transcriptomic responses to wounding: meta-analysis of gene expression microarray data

BMC Genomics ◽  
2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Piotr Andrzej Sass ◽  
Michał Dąbrowski ◽  
Agata Charzyńska ◽  
Paweł Sachadyn
Keyword(s):  
Gene Expression ◽  
Microarray Data ◽  
Meta Analysis ◽  
Gene Expression Microarray ◽  
Expression Microarray ◽  
Gene Expression Microarray Data ◽  
Transcriptomic Responses

scholarly journals Comparative functional genomic analysis of Alzheimer’s affected and naturally aging brains

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8682
Author(s):  
Yi-Shian Peng ◽  
Chia-Wei Tang ◽  
Yi-Yun Peng ◽  
Hung Chang ◽  
Chien-Lung Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Microarray Data ◽  
Gene List ◽  
Amyloid Β ◽  
Differentially Expressed ◽  
Gene Expression Microarray ◽  
Expression Microarray ◽  
Functional Genomic Analysis ◽  
Gene Expression Microarray Data

Background Alzheimer’s disease (AD) is a prevalent progressive neurodegenerative human disease whose cause remains unclear. Numerous initially highly hopeful anti-AD drugs based on the amyloid-β (Aβ) hypothesis of AD have failed recent late-phase tests. Natural aging (AG) is a high-risk factor for AD. Here, we aim to gain insights in AD that may lead to its novel therapeutic treatment through conducting meta-analyses of gene expression microarray data from AG and AD-affected brain. Methods Five sets of gene expression microarray data from different regions of AD (hereafter, ALZ when referring to data)-affected brain, and one set from AG, were analyzed by means of the application of the methods of differentially expressed genes and differentially co-expressed gene pairs for the identification of putatively disrupted biological pathways and associated abnormal molecular contents. Results Brain-region specificity among ALZ cases and AG-ALZ differences in gene expression and in KEGG pathway disruption were identified. Strong heterogeneity in AD signatures among the five brain regions was observed: HC/PC/SFG showed clear and pronounced AD signatures, MTG moderately so, and EC showed essentially none. There were stark differences between ALZ and AG. OXPHOS and Proteasome were the most disrupted pathways in HC/PC/SFG, while AG showed no OXPHOS disruption and relatively weak Proteasome disruption in AG. Metabolic related pathways including TCA cycle and Pyruvate metabolism were disrupted in ALZ but not in AG. Three pathogenic infection related pathways were disrupted in ALZ. Many cancer and signaling related pathways were shown to be disrupted AG but far less so in ALZ, and not at all in HC. We identified 54 “ALZ-only” differentially expressed genes, all down-regulated and which, when used to augment the gene list of the KEGG AD pathway, made it significantly more AD-specific.

Data Mining Methods for Microarray Data Analysis

2011 ◽  
pp. 283-287
Author(s):  
Lei Yu ◽  
Huan Liu
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Data Analysis ◽  
Microarray Data ◽  
Microarray Data Analysis ◽  
Future Research ◽  
Gene Expression Microarray ◽  
Expression Microarray ◽  
Class Prediction ◽  
Gene Expression Microarray Data

The advent of gene expression microarray technology enables the simultaneous measurement of expression levels for thousands or tens of thousands of genes in a single experiment (Schena, et al., 1995). Analysis of gene expression microarray data presents unprecedented opportunities and challenges for data mining in areas such as gene clustering (Eisen, et al., 1998; Tamayo, et al., 1999), sample clustering and class discovery (Alon, et al., 1999; Golub, et al., 1999), sample class prediction (Golub, et al., 1999; Wu, et al., 2003), and gene selection (Xing, Jordan, & Karp, 2001; Yu & Liu, 2004). This article introduces the basic concepts of gene expression microarray data and describes relevant data-mining tasks. It briefly reviews the state-of-the-art methods for each data-mining task and identifies emerging challenges and future research directions in microarray data analysis.

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