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* Image analysis: gridding, spot recognition of the scanned image (segmentation algorithm), removal or marking of poor-quality and low-intensity features (called ''flagging'').
* Data processing: background subtraction (based on global or local background), determination of spot intensities and intensity ratios, visualisation of data (e.g. see [[MA plot]]), and log-transformation of ratios, global or [[Local regression|local]] normalization of intensity ratios, and segmentation into different copy number regions using [[step detection]] algorithms.<ref>{{cite journal|last=Little|first= M.A.|coauthors=Jones, N.S.|title=Generalized Methods and Solvers for Piecewise Constant Signals: Part I| journal=[[Proceedings of the Royal Society A]]|url=http://www.maxlittle.net/publications/pwc_filtering_arxiv.pdf|year = 2011 }}</ref>
* Class discovery analysis: This analytic approach, sometimes called unsupervised classification or knowledge discovery, tries to identify whether microarrays (objects, patients, mice, etc.) or genes cluster together in groups. Identifying naturally existing groups of objects (microarrays or genes) which cluster together can enable the discovery of new groups that otherwise were not previously known to exist. During knowledge discovery analysis, various unsupervised classification techniques can be employed with DNA microarray data to identify novel clusters (classes) of arrays.<ref name="Peterson">{{cite book|author=Peterson,
|year= 2013|title=Classification Analysis of DNA Microarrays|publisher=John Wiley and Sons|isbn=978-0-470-17081-6|url=http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470170816.html|}}</ref> This type of approach is not hypothesis-driven, but rather is based on iterative pattern recognition or statistical learning methods to find an optimal number of clusters in the data. Examples of unsupervised analyses include self-organizing maps, neural gas, k-means cluster analyses, hierarchical cluster analysis, and model-based cluster analysis. The input data used in class discovery analyses are commonly based on lists of genes having high informativeness (low noise) based on low values of the coefficient of variation or high values of Shannon entropy, etc. The determination of the most likely or optimal number of clusters obtained from an unsupervised analysis is called cluster validity. Some commonly used metrics for cluster validity are the silhouette index, Davies-Bouldin index, Dunn's index, or Hubert's <math>\Gamma</math> statistic.
* Class prediction analysis: This approach, called supervised classification, establishes the basis for developing a predictive model into which future unknown test objects can be input in order to predict the most likely class membership of the test objects. Supervised analysis<ref name="Peterson"/> for class prediction involves use of techniques such as linear regression, k-nearest neighbor, learning vector quantization, decision tree analysis, random forests, naive Bayes, logistic regression, kernel regression, artificial neural networks, support vector machines, mixture of experts, and supervised neural gas. In addition, various metaheuristic methods are employed, such as genetic algorithms, covariance matrix self-adaptation, particle swarm optimization, and ant colony optimization. Input data for class prediction are usually based on filtered lists of genes which are predictive of class, determined using classical hypothesis tests (next section), Gini diversity index, or information gain (entropy).
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