Extracting meaning from microarray data

2003 ◽  
Vol 31 (6) ◽  
pp. 1474-1475
Author(s):  
R.K. Curtis ◽  
M.D. Brand
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Microarray Data ◽  
Indirect Effects ◽  
Microarray Data Analysis ◽  
Control Analysis ◽  
Modular Control ◽  
Methods Of Analysis

Gene expression is complex: many mRNAs change in abundance in response to a new condition. But while some of these expression changes may be direct, many may be downstream, indirect effects. One of the major problems of microarray data analysis is distinguishing between these changes. Some of the most common methods of analysis are discussed, in the context of their ability to distinguish between direct and indirect expression changes. The application of modular control analysis to microarray data in order to partition and quantify the importance of mRNA clusters in mediating responses is described.

Computational Strategies for Analyzing Data in Gene Expression Microarray Experiments

2003 ◽  
Vol 01 (03) ◽  
pp. 541-586 ◽  
Author(s):  
Tero Aittokallio ◽  
Markus Kurki ◽  
Olli Nevalainen ◽  
Tuomas Nikula ◽  
Anne West ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Microarray Data ◽  
Microarray Data Analysis ◽  
Biological Research ◽  
Microarray Experiments ◽  
Dna Microarray Data ◽  
Open Questions ◽  
Analysis Technique ◽  
Wide Range

Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.

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.

AI Methods for Analyzing Microarray Data

2011 ◽  
pp. 877-884
Author(s):  
Amira Djebbari ◽  
Aedín C. Culhane ◽  
Alice J. Armstrong ◽  
John Quackenbush
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Microarray Data ◽  
Expression Patterns ◽  
Microarray Data Analysis ◽  
K Nearest Neighbor ◽  
Learning Methods ◽  
Class Prediction ◽  
Class Discovery ◽  
Information Management Systems

Biological systems can be viewed as information management systems, with a basic instruction set stored in each cell’s DNA as “genes.” For most genes, their information is enabled when they are transcribed into RNA which is subsequently translated into the proteins that form much of a cell’s machinery. Although details of the process for individual genes are known, more complex interactions between elements are yet to be discovered. What we do know is that diseases can result if there are changes in the genes themselves, in the proteins they encode, or if RNAs or proteins are made at the wrong time or in the wrong quantities. Recent advances in biotechnology led to the development of DNA microarrays, which quantitatively measure the expression of thousands of genes simultaneously and provide a snapshot of a cell’s response to a particular condition. Finding patterns of gene expression that provide insight into biological endpoints offers great opportunities for revolutionizing diagnostic and prognostic medicine and providing mechanistic insight in data-driven research in the life sciences, an area with a great need for advances, given the urgency associated with diseases. However, microarray data analysis presents a number of challenges, from noisy data to the curse of dimensionality (large number of features, small number of instances) to problems with no clear solutions (e.g. real world mappings of genes to traits or diseases that are not yet known). Finding patterns of gene expression in microarray data poses problems of class discovery, comparison, prediction, and network analysis which are often approached with AI methods. Many of these methods have been successfully applied to microarray data analysis in a variety of applications ranging from clustering of yeast gene expression patterns (Eisen et al., 1998) to classification of different types of leukemia (Golub et al., 1999). Unsupervised learning methods (e.g. hierarchical clustering) explore clusters in data and have been used for class discovery of distinct forms of diffuse large B-cell lymphoma (Alizadeh et al., 2000). Supervised learning methods (e.g. artificial neural networks) utilize a previously determined mapping between biological samples and classes (i.e. labels) to generate models for class prediction. A k-nearest neighbor (k-NN) approach was used to train a gene expression classifier of different forms of brain tumors and its predictions were able to distinguish biopsy samples with different prognosis suggesting that microarray profiles can predict clinical outcome and direct treatment (Nutt et al., 2003). Bayesian networks constructed from microarray data hold promise for elucidating the underlying biological mechanisms of disease (Friedman et al., 2000).

scholarly journals Ratio statistics of gene expression levels and applications to microarray data analysis

2002 ◽  
Vol 18 (9) ◽  
pp. 1207-1215 ◽  
Author(s):  
Y. Chen ◽  
V. Kamat ◽  
E. R. Dougherty ◽  
M. L. Bittner ◽  
P. S. Meltzer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Microarray Data ◽  
Microarray Data Analysis ◽  
Expression Levels ◽  
Gene Expression Levels
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