DNA microarray: Difference between revisions

Revision as of 13:14, 30 October 2013

Revision as of 13:16, 30 October 2013

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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 arraysmicroarrays (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, L.E.

|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.

* 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 objectobjects. 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).

* Hypothesis-driven statistical analysis: Identification of statistically significant changes in gene expression are commonly identified using the [[t-test]], [[ANOVA]], [[Bayesian method]]<ref>Ben-Gal I., Shani A., Gohr A., Grau J., Arviv S., Shmilovici A., Posch S. and Grosse I. (2005), Identification of Transcription Factor Binding Sites with Variable-order Bayesian Networks, Bioinformatics,vol. 21, no. 11, 2657-2666. Available at http://bioinformatics.oxfordjournals.org/content/21/11/2657.full.pdf?keytype=ref&ijkey=KkxNhRdTSfvtvXY</ref> [[Mann–Whitney test]] methods tailored to microarray data sets, which take into account [[multiple comparisons]]<ref>Yuk Fai Leung and Duccio Cavalieri, Fundamentals of cDNA microarray data analysis. TRENDS in Genetics Vol.19 No.11 November 2003</ref> or [[cluster analysis]].<ref name="Priness2007">{{cite journal|author=Priness I., Maimon O., Ben-Gal I.|year=2007|title=Evaluation of gene-expression clustering via mutual information distance measure|journal=BMC Bioinformatics|volume=8|issue=1|page=111|url=Available at http://www.biomedcentral.com/1471-2105/8/111|doi=10.1186/1471-2105-8-111|pmid=17397530|pmc=1858704}}</ref> These methods assess statistical power based on the variation present in the data and the number of experimental replicates, and can help minimize [[Type I and type II errors]] in the analyses.<ref name="Wei">{{cite journal|author=Wei C, Li J, Bumgarner RE.|year= 2004|title=Sample size for detecting differentially expressed genes in microarray experiments|journal=BMC Genomics|volume=5|pages=87|pmid=15533245|doi=10.1186/1471-2164-5-87|pmc=533874}}</ref>

<!-- {{Citation needed|date=July 2008}}as in many other cases where authorities disagree, a sound conservative approach is to directly compare different normalization methods to determine the effects of these different methods on the results obtained. This can be done, for example, by investigating the performance of various methods on data from "spike-in" experiments. {{Citation needed|date=July 2008}} -->