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Synthetic data from this program have been used to validate network discovery methods for microarrays [26].
The 'spike-in' data is a control dataset which has been used for evaluating the effectiveness of analysis methods for microarrays.
Present analysis methods for microarrays are mainly focused on known coding regions [8], [10], and researchers soon run into problems when trying to analyse signals from intergenic regions or un-annotated genomes, because of the difficulty in defining consistently expressed segments of the genome without the aid of an annotation.
The statistical analysis of gene expression was performed using linear methods for microarrays (limma package in Bioconductor software) [ 11].
The analysis of differential expression was performed using linear methods for microarrays (limma package in Bioconductor software) [ 22].
Briefly, the statistical analysis of gene expression was performed using linear methods for microarrays (limma package in Bioconductor software) [ 7].
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Allison DB. (2002) Statistical methods for microarray research for drug target identification.
The presumption of independence underlies all the familiar permutation, cross-validation, and bootstrap methods for microarray analysis, so it is important to know when independence fails.
The original authors provide a response and set in motion a discussion in the field about appropriate methods for microarray data analysis.
Electrical readout systems have emerged as an alternative to conventional optical methods for microarray analysis thanks to its potential advantages like low-cost, low-power and easy miniaturization of the required instrumentation.
For example, most classification and regression methods for microarray data are based on the whole set of genes and have not made use of pathway information.
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