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	[[File:From_spit_to_DNA-sample.webm\|thumb\|''How to use a microarray for genotyping.'' The video shows the process of extracting genotypes from a human spit sample using microarrays. Genotyping is a major use of DNA microarrays, but with some modifications they can also be used for other purposes such as measurement of gene expression and epigenetic markers.]]		[[File:From_spit_to_DNA-sample.webm\|thumb\|''How to use a microarray for genotyping.'' The video shows the process of extracting genotypes from a human spit sample using microarrays. Genotyping is a major use of DNA microarrays, but with some modifications they can also be used for other purposes such as measurement of gene expression and epigenetic markers.]]

	A '''DNA microarray''' (also commonly known as [[DNA]] chip or [[biochip]]) is a collection of microscopic DNA spots attached to a solid surface. Scientists use DNA [[microarray]]s to measure the [[Gene expression\|expression]] levels of large numbers of genes simultaneously or to [[genotyping\|genotype]] multiple regions of a genome. Each DNA spot contains [[Pico-\|picomoles]] (10<sup>−12</sup> [[Mole (unit)\|moles]]) of a specific DNA sequence, known as ''[[Hybridization probe\|probe]]s'' (or ''reporters'' or ''[[oligonucleotide\|oligo]]s''). These can be a short section of a [[gene]] or other DNA element that are used to [[Nucleic acid hybridization#Hybridization\|hybridize]] a [[cDNA]] or cRNA (also called anti-sense RNA) <!--Agilent kit--> sample (called ''target'') under high-stringency conditions. Probe-target hybridization is usually detected and quantified by detection of [[fluorophore]]-, silver-, or [[chemiluminescence]]-labeled targets to determine relative abundance of nucleic acid sequences in the target. The original nucleic acid arrays were macro arrays approximately 9 cm × 12 cm and the first computerized image based analysis was published in 1981.<ref name="Taub">{{cite journal\|last1=Taub\|first1=Floyd\|title=Laboratory methods: Sequential comparative hybridizations analyzed by computerized image processing can identify and quantitate regulated RNAs\|journal=DNA\|date=1983\|volume=2\|issue=4\|pages=309–327\|doi=10.1089/dna.1983.2.309\|pmid=6198132}}</ref> It was invented by [[Patrick O. Brown]]. An example of its application is in SNPs arrays for polymorphisms in cardiovascular diseases, cancer, pathogens and GWAS analysis. It is also used for the identification of structural variations and the measurement of gene expression.		A '''DNA microarray''' (also commonly known as a '''DNA chip''' or '''[[biochip]]''') is a collection of microscopic [[DNA]] spots attached to a solid surface. Scientists use DNA [[microarray]]s to measure the [[Gene expression\|expression]] levels of large numbers of genes simultaneously or to [[genotyping\|genotype]] multiple regions of a genome. Each DNA spot contains [[Pico-\|picomoles]] (10<sup>−12</sup> [[Mole (unit)\|moles]]) of a specific DNA sequence, known as ''[[Hybridization probe\|probe]]s'' (or ''reporters'' or ''[[oligonucleotide\|oligo]]s''). These can be a short section of a [[gene]] or other DNA element that are used to [[Nucleic acid hybridization#Hybridization\|hybridize]] a [[cDNA]] or cRNA (also called anti-sense RNA) <!--Agilent kit--> sample (called ''target'') under high-stringency conditions. Probe-target hybridization is usually detected and quantified by detection of [[fluorophore]]-, silver-, or [[chemiluminescence]]-labeled targets to determine relative abundance of nucleic acid sequences in the target. The original nucleic acid arrays were macro arrays approximately 9 cm × 12 cm and the first computerized image based analysis was published in 1981.<ref name="Taub">{{cite journal\|last1=Taub\|first1=Floyd\|title=Laboratory methods: Sequential comparative hybridizations analyzed by computerized image processing can identify and quantitate regulated RNAs\|journal=DNA\|date=1983\|volume=2\|issue=4\|pages=309–327\|doi=10.1089/dna.1983.2.309\|pmid=6198132}}</ref> It was invented by [[Patrick O. Brown]]. An example of its application is in SNPs arrays for polymorphisms in cardiovascular diseases, cancer, pathogens and GWAS analysis. It is also used for the identification of structural variations and the measurement of gene expression.

	== Principle ==		== Principle ==

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← Previous revision		Revision as of 13:10, 19 July 2025
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	[[File:Microarray printing.ogv\|thumb\|A DNA microarray being printed by a [[robot]] at the [[University of Delaware]] ]]		[[File:Microarray printing.ogv\|thumb\|A DNA microarray being printed by a [[robot]] at the [[University of Delaware]] ]]

	Microarrays can be fabricated using a variety of technologies, including printing with fine-pointed pins onto glass slides, [[photolithography]] using pre-made masks, photolithography using dynamic micromirror devices, ink-jet printing,<ref>J Biochem Biophys Methods. 2000 Mar 16;42(3):105–10. DNA-printing: utilization of a standard inkjet printer for the transfer of nucleic acids to solid supports. Goldmann T, Gonzalez JS.</ref><ref>{{cite journal\|journal=Genome Biology \| title=POSaM: a fast, flexible, open-source, inkjet oligonucleotide synthesizer and microarrayer\| author=Lausted C\| volume = 5 \| ~~pages~~=R58 \| doi=10.1186/gb-2004-5-8-r58 \| pmid=15287980 \| date=2004\| issue=8\| pmc=507883\|display-authors=etal\| doi-access=free}}</ref> or [[electrochemistry]] on microelectrode arrays.		Microarrays can be fabricated using a variety of technologies, including printing with fine-pointed pins onto glass slides, [[photolithography]] using pre-made masks, photolithography using dynamic micromirror devices, ink-jet printing,<ref>J Biochem Biophys Methods. 2000 Mar 16;42(3):105–10. DNA-printing: utilization of a standard inkjet printer for the transfer of nucleic acids to solid supports. Goldmann T, Gonzalez JS.</ref><ref>{{cite journal\|journal=Genome Biology \| title=POSaM: a fast, flexible, open-source, inkjet oligonucleotide synthesizer and microarrayer\| author=Lausted C\| volume = 5 \| article-number=R58 \| doi=10.1186/gb-2004-5-8-r58 \| pmid=15287980 \| date=2004\| issue=8\| pmc=507883\|display-authors=etal\| doi-access=free}}</ref> or [[electrochemistry]] on microelectrode arrays.

	In ''spotted microarrays'', the probes are [[oligonucleotide synthesis\|oligonucleotide]]s, [[cDNA]] or small fragments of [[Polymerase chain reaction\|PCR]] products that correspond to [[mRNA]]s. The probes are [[oligonucleotide synthesis\|synthesized]] prior to deposition on the array surface and are then "spotted" onto glass. A common approach utilizes an array of fine pins or needles controlled by a robotic arm that is dipped into wells containing DNA probes and then depositing each probe at designated locations on the array surface. The resulting "grid" of probes represents the nucleic acid profiles of the prepared probes and is ready to receive complementary cDNA or cRNA "targets" derived from experimental or clinical samples.		In ''spotted microarrays'', the probes are [[oligonucleotide synthesis\|oligonucleotide]]s, [[cDNA]] or small fragments of [[Polymerase chain reaction\|PCR]] products that correspond to [[mRNA]]s. The probes are [[oligonucleotide synthesis\|synthesized]] prior to deposition on the array surface and are then "spotted" onto glass. A common approach utilizes an array of fine pins or needles controlled by a robotic arm that is dipped into wells containing DNA probes and then depositing each probe at designated locations on the array surface. The resulting "grid" of probes represents the nucleic acid profiles of the prepared probes and is ready to receive complementary cDNA or cRNA "targets" derived from experimental or clinical samples.
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	[[File:Toxicology Research at FDA (NCTR 1470) (6009042166).jpg\|thumb\|[[National Center for Toxicological Research]] scientist reviews microarray data.]]		[[File:Toxicology Research at FDA (NCTR 1470) (6009042166).jpg\|thumb\|[[National Center for Toxicological Research]] scientist reviews microarray data.]]

	Microarray data sets are commonly very large, and analytical precision is influenced by a number of variables. [[Statistics\|Statistical]] challenges include taking into account effects of background noise and appropriate [[Normalization (statistics)\|normalization]] of the data. Normalization methods may be suited to specific platforms and, in the case of commercial platforms, the analysis may be proprietary.<ref>{{cite web\|title=Prosigna {{!}} Prosigna algorithm\|url=http://prosigna.com/x-us/overview/prosigna-algorithm/\|website=prosigna.com\|access-date=22 June 2017}}</ref> Algorithms that affect statistical analysis include:		Microarray data sets are commonly very large, and analytical precision is influenced by a number of variables. [[Statistics\|Statistical]] challenges include taking into account effects of background noise and appropriate [[Normalization (statistics)\|normalization]] of the data. Normalization methods may be suited to specific platforms and, in the case of commercial platforms, the analysis may be proprietary.<ref>{{cite web\|title=Prosigna {{!}} Prosigna algorithm\|url=http://prosigna.com/x-us/overview/prosigna-algorithm/\|website=prosigna.com\|access-date=22 June 2017\|archive-date=9 November 2017\|archive-url=https://web.archive.org/web/20171109082205/http://prosigna.com/x-us/overview/prosigna-algorithm/\|url-status=dead}}</ref> Algorithms that affect statistical analysis include:

	* Image analysis: gridding, spot recognition of the scanned image (segmentation algorithm), removal or marking of poor-quality and low-intensity features (called ''flagging'').		* 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.\|author2=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\|date = 2011 \|doi=10.1098/rspa.2010.0671\|pmid= 22003312\|pmc= 3191861\|volume=467\|issue= 2135\|pages=3088–3114}}</ref>		* 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.\|author2=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\|date=2011\|doi=10.1098/rspa.2010.0671\|pmid=22003312\|pmc=3191861\|volume=467\|issue=2135\|pages=3088–3114\|archive-date=19 August 2019\|access-date=6 July 2011\|archive-url=https://web.archive.org/web/20190819140345/http://www.maxlittle.net/publications/pwc_filtering_arxiv.pdf\|url-status=dead}}</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, Leif E.		* 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, Leif E.
	\|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.		\|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).		* 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 [[type I and type II errors]] in the analyses.<ref name="Wei">{{cite journal\|author=Wei C \|author2=Li J \|author3=Bumgarner RE\|date= 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 \|doi-access=free }}</ref>		* 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 [[type I and type II errors]] in the analyses.<ref name="Wei">{{cite journal\|author=Wei C \|author2=Li J \|author3=Bumgarner RE\|date= 2004\|title=Sample size for detecting differentially expressed genes in microarray experiments\|journal=BMC Genomics\|volume=5\|article-number=87\|pmid=15533245\|doi=10.1186/1471-2164-5-87\|pmc=533874 \|doi-access=free }}</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}} -->		<!-- {{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.		* 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.

DNA microarray - Revision history

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