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{{short description|Collection of microscopic DNA spots attached to a solid surface}}
{{Use dmy dates|date=December
[[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
== Principle ==
[[File:NA hybrid.svg|thumb|Hybridization of the target to the probe]]▼
{{Main|Nucleic acid hybridization}}
{{
<!-- KEEP SECTION SIMPLE -->
▲[[File:NA hybrid.svg|thumb|Hybridization of the target to the probe]]
The core principle behind microarrays is hybridization between two DNA strands, the property of [[Complementarity (molecular biology)|complementary]] nucleic acid sequences to specifically pair with each other by forming [[hydrogen bond]]s between complementary [[Nucleotide|nucleotide base pairs]]. A high number of complementary base pairs in a nucleotide sequence means tighter [[non-covalent]] bonding between the two strands. After washing off non-specific bonding sequences, only strongly paired strands will remain hybridized. Fluorescently labeled target sequences that bind to a probe sequence generate a signal that depends on the hybridization conditions (such as temperature), and washing after hybridization. Total strength of the signal, from a spot (feature), depends upon the amount of target sample binding to the probes present on that spot. Microarrays use relative quantitation in which the intensity of a feature is compared to the intensity of the same feature under a different condition, <!-- 2 channel experiments are mentioned below! so do not repeat --> and the identity of the feature is known by its position.
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| [[Gene expression profiling]]
| In an [[mRNA]] or [[gene expression profiling]] experiment the [[Gene expression|expression]] levels of thousands of genes are simultaneously monitored to study the effects of certain treatments, [[disease]]s, and developmental stages on gene expression. For example, microarray-based gene expression profiling can be used to identify genes whose expression is changed in response to [[pathogens]] or other organisms by comparing gene expression in infected to that in uninfected cells or tissues.<ref name="Adomas et al.">{{cite journal|author=Adomas A|author2=Heller G|author3=Olson A|author4=Osborne J|author5=Karlsson M|author6=Nahalkova J|author7=Van Zyl L|author8=Sederoff R|author9=Stenlid J|author10=Finlay R|author11=Asiegbu FO|date=2008|title=Comparative analysis of transcript abundance in Pinus sylvestris after challenge with a saprotrophic, pathogenic or mutualistic fungus|journal=Tree Physiol.|volume=28|pages=885–897|pmid=18381269|issue=6|doi=10.1093/treephys/28.6.885|doi-access=
|-
| [[Comparative genomic hybridization]]
| Assessing genome content in different cells or closely related organisms, as originally described by [[Patrick O. Brown|Patrick Brown]], Jonathan Pollack, [[Ash Alizadeh]] and colleagues at [[Stanford University|Stanford]].<ref name="Pollack et al.">{{cite journal|author=Pollack JR|author2=Perou CM|author3=Alizadeh AA|author4=Eisen MB|author5=Pergamenschikov A|author6=Williams CF|author7=Jeffrey SS|author8=Botstein D|author9=Brown PO|date= 1999|title=Genome-wide analysis of DNA copy-number changes using cDNA microarrays|journal=Nat Genet|volume=23|pages=41–46|pmid=10471496|doi=10.1038/12640|issue=1|s2cid=997032|url=https://cdr.lib.unc.edu/downloads/sj139421j }}</ref><ref name="Moran et al.">{{cite journal|author=Moran G|author2=Stokes C|author3=Thewes S|author4=Hube B|author5=Coleman DC|author6=Sullivan D|date= 2004|title=Comparative genomics using Candida albicans DNA microarrays reveals absence and divergence of virulence-associated genes in Candida dubliniensis|journal=Microbiology|volume=150|pages=3363–3382|pmid=15470115|doi=10.1099/mic.0.27221-0|issue=Pt 10|doi-access=free|hdl=2262/6097|hdl-access=free}}</ref>
|-
| GeneID
| Small microarrays to check IDs of organisms in food and feed (like [[GMO]] [https://web.archive.org/web/20090228210111/http://bgmo.jrc.ec.europa.eu/home/docs.htm]), [[mycoplasms]] in cell culture, or [[pathogens]] for disease detection, mostly combining [[Polymerase chain reaction|PCR]] and microarray technology.
|-
| [[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
|-
| [[DNA adenine methyltransferase identification|DamID]]
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| [[SNP array|SNP detection]]
| Identifying [[single nucleotide polymorphism]] among [[alleles]] within or between populations.<ref name="Hacia et al.">{{cite journal |author=Hacia JG|author2=Fan JB|author3=Ryder O|author4= Jin L|author5=Edgemon K|author6=Ghandour G|author7=Mayer RA|author8= Sun B|author9=Hsie L|author10=Robbins CM|author11=Brody LC|author12=Wang D|author13=Lander ES|author14=Lipshutz R|author15=Fodor SP|author16=Collins FS|date= 1999|title=Determination of ancestral alleles for human single-nucleotide polymorphisms using high-density oligonucleotide arrays|journal=Nat Genet|volume=22|pages=164–167|pmid=10369258 | doi = 10.1038/9674|issue=2|s2cid=41718227}}</ref> Several applications of microarrays make use of SNP detection, including [[genotyping]], [[forensic]] analysis, measuring [[Genetic predisposition|predisposition]] to disease, identifying drug-candidates, evaluating [[germline]] mutations in individuals or [[Somatic (biology)|somatic]] mutations in cancers, assessing [[loss of heterozygosity]], or [[genetic linkage]] analysis.
|-
| [[Alternative splicing]] detection
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| [[Fusion gene]]s microarray
| A
|-
| [[Tiling array]]
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|-
|Double-stranded B-DNA microarrays
|Right-handed double-stranded B-DNA microarrays can be used to characterize novel drugs and biologicals that can be employed to bind specific regions of immobilized, intact, double-stranded DNA. This approach can be used to inhibit gene expression.<ref name="Gagna 895–914">{{Cite journal|title = Novel multistranded, alternative, plasmid and helical transitional DNA and RNA microarrays: implications for therapeutics|journal = Pharmacogenomics|date = 2009-05-01|issn = 1744-8042|pmid = 19450135|pages = 895–914|volume = 10|issue = 5|doi = 10.2217/pgs.09.27|first1 = Claude E.|last1 = Gagna|first2 = W. Clark|last2 = Lambert}}</ref><ref name="Gagna 381–401">{{Cite journal|title = Cell biology, chemogenomics and chemoproteomics
|-
|Double-stranded Z-DNA microarrays
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|Multi-stranded DNA and RNA microarrays can be used to identify novel drugs that bind to these multi-stranded nucleic acid sequences. This approach can be used to discover new drugs and biologicals that have the ability to inhibit gene expression.<ref name="Gagna 895–914"/><ref name="Gagna 381–401"/><ref>{{Cite journal|title = Triplex technology in studies of DNA damage, DNA repair, and mutagenesis|journal = Biochimie|date = 2011-08-01|issn = 1638-6183|pmc = 3545518|pmid = 21501652|pages = 1197–1208|volume = 93|issue = 8|doi = 10.1016/j.biochi.2011.04.001|first1 = Anirban|last1 = Mukherjee|first2 = Karen M.|last2 = Vasquez}}</ref><ref>{{Cite journal|title = G-quadruplexes and their regulatory roles in biology|journal = Nucleic Acids Research|date = 2015-10-15|issn = 1362-4962|pmc = 4605312|pmid = 26350216|pages = 8627–8637|volume = 43|issue = 18|doi = 10.1093/nar/gkv862|first1 = Daniela|last1 = Rhodes|first2 = Hans J.|last2 = Lipps}}</ref> These microarrays also allow for characterization of their structure under different environmental conditions.
|}
Specialised arrays tailored to particular [[crop]]s are becoming increasingly popular in [[molecular breeding]] applications. In the future they could be used to screen [[seedling]]s at early stages to lower the number of unneeded seedlings tried out in breeding operations.<ref name="Rasheed-et-al-2017">{{cite journal | last1=Rasheed | first1=Awais | last2=Hao | first2=Yuanfeng | last3=Xia | first3=Xianchun | last4=Khan | first4=Awais | last5=Xu | first5=Yunbi | last6=Varshney | first6=Rajeev K. | last7=He | first7=Zhonghu | title=Crop Breeding Chips and Genotyping Platforms: Progress, Challenges, and Perspectives | journal=[[Molecular Plant]] | publisher=[[Chinese Academy of Sciences|Chin Acad Sci]]+[[Chinese Society for Plant Biology|Chin Soc Plant Bio]]+[[Shanghai Institutes for Biological Sciences|Shanghai Inst Bio Sci]] ([[Elsevier]]) | volume=10 | issue=8 | year=2017 | issn=1674-2052 | doi=10.1016/j.molp.2017.06.008 | pages=1047–1064 | s2cid=33780984 | pmid=28669791| doi-access=free | bibcode=2017MPlan..10.1047R | url=http://oar.icrisat.org/10133/1/S1674-2052%2817%2930174-0.pdf }}</ref>
=== Fabrication ===
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[[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):
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.
This technique is used by research scientists around the world to produce "in-house" printed microarrays
Publications exist which indicate in-house spotted microarrays may not provide the same level of sensitivity compared to commercial oligonucleotide arrays,<ref name="TRC Standardization">{{cite journal |date=2005 |title=Standardizing global gene expression analysis between laboratories and across platforms |journal=Nat Methods |volume=2 |pages=351–356 |pmid=15846362 |doi=10.1038/nmeth754 |last12=Deng |first12=S |last13=Dressman |first13=HK |last14=Fannin |first14=RD |last15=Farin |first15=FM |last16=Freedman |first16=JH |last17=Fry |first17=RC |last18=Harper |first18=A |last19=Humble |first19=MC |last20=Hurban |first20=P |last21=Kavanagh |first21=TJ |last22=Kaufmann |first22=WK |first23=KF |first24=L |first25=JA |first26=MR |last27=Li |first27=J |first28=YJ |last29=Lobenhofer |first29=EK |last30=Lu |last31=Malek |first31=RL |last32=Milton |first32=S |last33=Nagalla |first33=SR |last34=O'malley |first34=JP |last35=Palmer |first35=VS |last36=Pattee |first36=P |last7=Paules |first7=RS |last38=Perou |first38=CM |last9=Phillips |first39=K |last40=Qin |last41=Qiu |first41=Y |last42=Quigley |first42=SD |last43=Rodland |first43=M |last44=Rusyn |first44=I |last45=Samson |first45= LD|last46= Schwartz|last47=Shi |first47=Y |last48=Shin |last49=Sieber |last50=Slifer |last51=Speer |first51=MC |last52=Spencer |first52=PS |last53=Sproles |first53=DI |last54=Swenberg |first54=JA |last55=Suk|first55= WA |last56=Sullivan |first56=RC |last57=Tian |first57=R |last58=Tennant |first58=RW |last59= Todd |first59=SA |last60=Tucker |first60=CJ |last61=Van Houten |first61=B |last62=Weis |first62=BK |last63=Xuan |first63=S |last64=Zarbl |first64=H |last65=Members
In ''oligonucleotide microarrays'', the probes are short sequences designed to match parts of the sequence of known or predicted [[open reading frame]]s. Although oligonucleotide probes are often used in "spotted" microarrays, the term "oligonucleotide array" most often refers to a specific technique of manufacturing. Oligonucleotide arrays are produced by printing short oligonucleotide sequences designed to represent a single gene or family of gene splice-variants by [[oligonucleotide synthesis|synthesizing]] this sequence directly onto the array surface instead of depositing intact sequences. Sequences may be longer (60-mer probes such as the [[Agilent]] design) or shorter (25-mer probes produced by [[Affymetrix]]) depending on the desired purpose; longer probes are more specific to individual target genes, shorter probes may be spotted in higher density across the array and are cheaper to manufacture.
One technique used to produce oligonucleotide arrays include [[photolithographic]] synthesis (Affymetrix) on a silica substrate where light and light-sensitive masking agents are used to "build" a sequence one nucleotide at a time across the entire array.<ref name="Affy PNAS Paper">{{cite journal|author=Pease AC|author2=Solas D|author3=Sullivan EJ|author4=Cronin MT|author5=Holmes CP|author6=Fodor SP|date= 1994|title=Light-generated oligonucleotide arrays for rapid DNA sequence analysis|journal=PNAS|volume=91|pages=5022–5026|pmid=8197176|doi=10.1073/pnas.91.11.5022|issue=11|pmc=43922|bibcode=1994PNAS...91.5022P|doi-access=free}}</ref> Each applicable probe is selectively "unmasked" prior to bathing the array in a solution of a single nucleotide, then a masking reaction takes place and the next set of probes are unmasked in preparation for a different nucleotide exposure. After many repetitions, the sequences of every probe become fully constructed. More recently, Maskless Array Synthesis from NimbleGen Systems has combined flexibility with large numbers of probes.<ref name="NimbleGen Genome Res Paper">{{cite journal|author=Nuwaysir EF|author2=Huang W|author3=Albert TJ|author4=Singh J|author5=Nuwaysir K|author6=Pitas A|author7=Richmond T|author8=Gorski T|author9=Berg JP|author10=Ballin J|author11=McCormick M|author12=Norton J|author13=Pollock T|author14=Sumwalt T|author15=Butcher L|author16=Porter D|author17=Molla M|author18=Hall C|author19=Blattner F|author20=Sussman MR|author21=Wallace RL|author22=Cerrina F|author23=Green RD|date= 2002|title=Gene Expression Analysis Using Oligonucleotide Arrays Produced by Maskless Photolithography|journal=Genome Res|volume=12|pages=1749–1755|pmid=12421762|doi=10.1101/gr.362402|issue=11|pmc=187555}}</ref>
=== Two-channel vs. one-channel detection ===
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===A typical protocol===
[[File:Summary of RNA Microarray.svg|thumb|''Examples of levels of application of microarrays.'' Within the organisms, genes are transcribed and spliced to produce mature mRNA transcripts (red). The mRNA is extracted from the organism and reverse transcriptase is used to copy the mRNA into stable ds-cDNA (blue). In microarrays, the ds-cDNA is fragmented and fluorescently labelled (orange). The labelled fragments bind to an ordered array of complementary oligonucleotides, and [[Fluorometer|measurement of fluorescent intensity]] across the array indicates the abundance of a predetermined set of sequences. These sequences are typically specifically chosen to report on genes of interest within the organism's genome.<ref>{{Cite journal|last1=Shafee|first1=Thomas|last2=Lowe|first2=Rohan|date=2017|title=Eukaryotic and prokaryotic gene structure|journal=WikiJournal of Medicine|language=en|volume=4|issue=1|doi=10.15347/wjm/2017.002|issn=2002-4436|doi-access=free|s2cid=35766676 }}</ref>]]
This is an example of a '''DNA microarray experiment''' which includes details for a particular case to better explain DNA microarray experiments, while listing modifications for RNA or other alternative experiments.
# 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.
#* The label is added either during the reverse transcription step, or following amplification if it is performed. The [[Sense (molecular biology)|sense]] labeling is dependent on the microarray; e.g. if the label is added with the RT mix, the [[cDNA]] is antisense and the microarray probe is sense, except in the case of negative controls.
#* The label is typically [[fluorescent]]; only one machine uses [[radioactivity in biology|radiolabels]].
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[[Image:Heatmap.png|right|thumb|Gene expression values from microarray experiments can be represented as [[heat map]]s to visualize the result of data analysis.]]
The advent of inexpensive microarray experiments created several specific bioinformatics challenges:<ref>{{
=== Experimental design ===
Due to the biological complexity of gene expression, the considerations of experimental design that are discussed in the [[expression profiling]] article are of critical importance if statistically and biologically valid conclusions are to be drawn from the data.
There are three main elements to consider when designing a microarray experiment. First, replication of the biological samples is essential for drawing conclusions from the experiment. Second, technical replicates (e.g. two RNA samples obtained from each experimental unit) may help to
=== Standardization ===
Microarray data is difficult to exchange due to the lack of standardization in platform fabrication, assay protocols, and analysis methods. This presents an [[interoperability]] problem in [[bioinformatics]]. Various [[grass-roots]] [[open-source model|open-source]] projects are trying to ease the exchange and analysis of data produced with non-proprietary chips:
For example, the "Minimum Information About a Microarray Experiment" ([[MIAME]]) checklist helps define the level of detail that should exist and is being adopted by many [[Scientific journal|journals]] as a requirement for the submission of papers incorporating microarray results. But MIAME does not describe the format for the information, so while many formats can support the MIAME requirements, {{as of|lc=y|2007}} no format permits verification of complete semantic compliance. The "MicroArray Quality Control (MAQC) Project" is being conducted by the US [[Food and Drug Administration]] (FDA) to develop standards and quality control metrics which will eventually allow the use of MicroArray data in drug discovery, clinical practice and regulatory decision-making.<ref>[https://web.archive.org/web/20051208055601/http://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/ NCTR Center for Toxicoinformatics
=== 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}}
▲[[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|
* 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=
* 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
* 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=
<!-- {{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.
* Network-based methods: Statistical methods that take the underlying structure of gene networks into account, representing either associative or causative interactions or dependencies among gene products.<ref name="Emmert">{{cite book |author=Emmert-Streib, F.|author2=Dehmer, M.|
Microarray data may require further processing aimed at reducing the dimensionality of the data to aid comprehension and more focused analysis.<ref>{{cite journal
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| pmid = 14969494
| issue = 4
| citeseerx = 10.1.1.730.3670| s2cid = 16248921}}</ref> Other methods permit analysis of data consisting of a low number of biological or technical [[Replication (statistics)|replicate]]s; for example, the Local Pooled Error (LPE) test pools [[standard deviation]]s of genes with similar expression levels in an effort to compensate for insufficient replication.<ref>{{cite journal
| author = Jain N| author2 = Thatte J| author3 = Braciale T| author4 = Ley K| author5 = O'Connell M| author6 = Lee JK | date = 2003
| title = Local-pooled-error test for identifying differentially expressed genes with a small number of replicated microarrays
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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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| date = July 2008
| pmid=18516045
| s2cid = 205418589
}}</ref><ref name="wang2009">{{Cite journal
| doi = 10.1038/nrg2484
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== See also ==
{{Portal|Biology|Technology}}
{{
* [[Transcriptomics technologies]]
** [[Serial analysis of gene expression]]
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* [[Phenotype microarray]]
* [[Systems biology]]
* [[Whole genome sequencing]]
{{div col end}}
== References ==
{{Reflist
== External links ==
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|label=DNA microarrays}}
{{Commons category|DNA microarrays}}
* [
* [https://pubmed.ncbi.nlm.nih.gov/14551912/ PLoS Biology Primer: Microarray Analysis]
▲* [http://www.1lec.com/microarray/ Microarray Animation] 1Lec.com
* [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
* [
* [
{{Molecular Biology}}
{{Glass science}}
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[[Category:Gene expression]]
[[Category:Bioinformatics]]
[[Category:Genetics techniques]]
[[Category:Glass coating and surface modification]]
[[Category:DNA]]
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