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| [[ChIP-on-chip|Chromatin immunoprecipitation on Chip]]
| DNA sequences bound to a particular protein can be isolated by [[immunoprecipitation|immunoprecipitating]] that protein ([[Chromatin immunoprecipitation|ChIP]]), these fragments can be then hybridized to a microarray (such as a [[tiling array]]) allowing the determination of protein binding site occupancy throughout the genome. Example protein to [[Chromatin immunoprecipitation|immunoprecipitate]] are histone modifications ([[H3K27me3]], H3K4me2, H3K9me3, etc.), [[Polycomb-group protein]] (PRC2:Suz12, PRC1:YY1) and [[trithorax-group protein]] (Ash1) to study the [[epigenetics|epigenetic landscape]] or [[RNA
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| [[DNA adenine methyltransferase identification|DamID]]
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| [[Fusion gene]]s microarray
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| [[Tiling array]]
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# The two samples to be compared (pairwise comparison) are grown/acquired. In this example treated sample ([[Case-control|case]]) and untreated sample ([[Case-control|control]]).
# The [[nucleic acid]] of interest is purified: this can be [[RNA]] for [[expression profiling]], [[DNA]] for [[comparative hybridization]], or DNA/RNA bound to a particular [[protein]] which is [[Chromatin immunoprecipitation|immunoprecipitated]] ([[ChIP-on-chip]]) for [[Epigenetics|epigenetic]] or regulation studies. In this example total RNA is isolated (both nuclear and [[cytoplasm]]ic) by [[
# The purified RNA is analysed for quality (by [[capillary electrophoresis]]) and quantity (for example, by using a [[NanoDrop]] or NanoPhotometer [[spectrometer]]). If the material is of acceptable quality and sufficient quantity is present (e.g., >1[[μg]], although the required amount varies by microarray platform), the experiment can proceed.
# The labeled product is generated via [[reverse transcription]] and followed by an optional [[Polymerase chain reaction|PCR]] amplification. The RNA is reverse transcribed with either polyT primers (which amplify only [[mRNA]]) or random primers (which amplify all RNA, most of which is [[rRNA]]). [[MicroRNA|miRNA]] microarrays ligate an oligonucleotide to the purified small RNA (isolated with a fractionator), which is then reverse transcribed and amplified.
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=== Data analysis ===
[[File:Toxicology Research at FDA (NCTR 1470) (6009042166).jpg|thumb|[[National Center for Toxicological Research]] scientist reviews microarray data.]]
{{main|Microarray analysis techniques}}
{{See also|Gene chip analysis}}
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|date= 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 methods include self-organizing maps, neural gas, k-means cluster analyses,<ref>De Souto M et al. (2008) Clustering cancer gene expression data: a comparative study, BMC Bioinformatics, 9(497).</ref> hierarchical cluster analysis, Genomic Signal Processing based clustering and model-based cluster analysis. For some of these methods the user also has to define a distance measure between pairs of objects. Although the Pearson correlation coefficient is usually employed, several other measures have been proposed and evaluated in the literature.<ref>{{cite journal|last1=Jaskowiak|first1=Pablo A|last2=Campello|first2=Ricardo JGB|last3=Costa|first3=Ivan G|title=On the selection of appropriate distances for gene expression data clustering|journal=BMC Bioinformatics|volume=15|issue=Suppl 2|pages=S2|doi=10.1186/1471-2105-15-S2-S2|pmid=24564555|pmc=4072854|year=2014 |doi-access=free }}</ref> 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,<ref>Bolshakova N, Azuaje F (2003) Cluster validation techniques for genome expression data, Signal Processing, Vol. 83, pp. 825–833.</ref> 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 algorithm]]s, 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 name="Ben-GalShani2005">{{cite journal|last1=Ben Gal|first1=I.|last2=Shani|first2=A.|last3=Gohr|first3=A.|last4=Grau|first4=J.|last5=Arviv|first5=S.|last6=Shmilovici|first6=A.|last7=Posch|first7=S.|last8=Grosse|first8=I.|title=Identification of transcription factor binding sites with variable-order Bayesian networks|journal=Bioinformatics|volume=21|issue=11|year=2005|pages=2657–2666|issn=1367-4803|doi=10.1093/bioinformatics/bti410|pmid=15797905|doi-access=}}</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.|author2=Maimon O.|author3=Ben-Gal I.|date=2007|title=Evaluation of gene-expression clustering via mutual information distance measure|journal=BMC Bioinformatics|volume=8|issue=1|page=111|doi=10.1186/1471-2105-8-111|pmid=17397530|pmc=1858704 |doi-access=free }}</ref> These methods assess statistical power based on the variation present in the data and the number of experimental replicates, and can help minimize [[
<!-- {{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}} -->
* Dimensional reduction: Analysts often reduce the number of dimensions (genes) prior to data analysis.<ref name="Peterson"/> This may involve linear approaches such as principal components analysis (PCA), or non-linear manifold learning (distance metric learning) using kernel PCA, diffusion maps, Laplacian eigenmaps, local linear embedding, locally preserving projections, and Sammon's mapping.
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=== Data warehousing ===
Microarray data was found to be more useful when compared to other similar datasets. The sheer volume of data, specialized formats (such as [[MIAME]]), and curation efforts associated with the datasets require specialized databases to store the data. A number of open-source data warehousing solutions, such as [[InterMine]] and [
== Alternative technologies ==
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* {{curlie|Science/Biology/Biochemistry_and_Molecular_Biology/Gene_Expression/Products_and_Services|Products and Services for Gene Expression}}
* {{curlie|Science/Biology/Bioinformatics/Online_Services/Gene_Expression_and_Regulation|Online Services for Gene Expression Analysis}}
* [
* [
* [https://web.archive.org/web/20150924040600/http://www.genome.gov/page.cfm?pageID=10000533 Rundown of microarray technology]
* [http://www.arraymining.net ArrayMining.net]{{snd}}a free web-server for online microarray analysis
* [https://web.archive.org/web/20090223111750/http://www.unsolvedmysteries.oregonstate.edu/microarray_07 Microarray – How does it work?]
* [
* [
{{Molecular Biology}}
{{Glass science}}
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