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Fundamentals of cDNA Microarray Data Analysis (2003)
| Venue: | Trends Genet |
| Citations: | 80 - 0 self |
BibTeX
@ARTICLE{Leung03fundamentalsof,
author = {Yuk Fai Leung and Duccio Cavalieri},
title = {Fundamentals of cDNA Microarray Data Analysis},
journal = {Trends Genet},
year = {2003},
volume = {19},
pages = {649--659}
}
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Abstract
genomics research. The multi-step, data-intensive nature of this technology has created an unprecedented informatics and analytical challenge. It is important to understand the crucial steps that can affect the outcome of the analysis. In this review, we provide an overview of the contemporary trend on various main analysis steps in the microarray data analysis process, which includes experimental design, data standardization, image acquisition and analysis, normalization, statistical significance inference, exploratory data analysis, class prediction and pathway analysis, as well as various considerations relevant to their implementation. The development of microarray technology has been phenomenal in the past few years. It has become a standard tool in many genomics research laboratories. The reason for this popularity is that microarrays have revolutionized the approach to biological research. Instead of working on a gene-by-gene basis, scientists can now study tens of thousands of genes at once. Unfortunately, they are often daunted and confused by the complexity of data analyses. Although it is advisable to collaborate with statisticians and mathematicians on performing a proper data analysis, it is crucial to understand the fundamentals of data analysis. In this review, we explain these fundamentals step-by-step (Figure 1; Table 1). Instead of discussing any particular analysis software, we focus primarily on the rationale behind the analysis processes and the key factors that affect the quality of the result. For a compilation of current microarray analysis software see a recent article [1] and author’s website
Keyphrases
cdna microarray data analysis data analysis microarray data analysis process standard tool statistical significance inference various consideration recent article unprecedented informatics class prediction many genomics research laboratory current microarray analysis software contemporary trend various main analysis step past year fundamental step-by-step analytical challenge gene-by-gene basis data standardization crucial step data-intensive nature image acquisition experimental design key factor microarray technology genomics research proper data analysis pathway analysis exploratory data analysis biological research particular analysis software
