Editing DNA microarray (section)

== Microarrays and bioinformatics ==
[[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>{{Cite journal |last1=Tinker |first1=Anna V. |last2=Boussioutas |first2=Alex |last3=Bowtell |first3=David D.L. |date=2006 |title=The challenges of gene expression microarrays for the study of human cancer |journal=Cancer Cell |volume=9 |issue=5 |pages=333–339 |doi=10.1016/j.ccr.2006.05.001 |issn=1535-6108|doi-access=free |pmid=16697954 }}</ref> the multiple levels of replication in experimental design ([[#Experimental design|Experimental design]]); the number of platforms and independent groups and data format ([[#Standardization|Standardization]]); the statistical treatment of the data ([[#Data analysis|Data analysis]]); mapping each probe to the [[mRNA]] transcript that it measures ([[#Annotation|Annotation]]); the sheer volume of data and the ability to share it ([[#Data warehousing|Data warehousing]]).

=== 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 quantitate precision. The biological replicates include independent RNA extractions. Technical replicates may be two [[wikt:Special:Search/aliquot|aliquots]] of the same extraction. Third, spots of each cDNA clone or oligonucleotide are present as replicates (at least duplicates) on the microarray slide, to provide a measure of technical precision in each hybridization. It is critical that information about the sample preparation and handling is discussed, in order to help identify the independent units in the experiment and to avoid inflated estimates of [[statistical significance]].<ref>{{cite journal |title=Fundamentals of experimental design for cDNA microarrays | journal=Nature Genetics |series=supplement |volume=32 |date=2002 | doi=10.1038/ng1031 |url=http://www.vmrf.org/research-websites/gcf/Forms/Churchill.pdf |pages=490–5 |pmid=12454643 |last1=Churchill |first1=GA | s2cid=15412245 |url-status=dead |archive-url=https://web.archive.org/web/20050508225647/http://www.vmrf.org/research-websites/gcf/Forms/Churchill.pdf |archive-date=2005-05-08 |access-date=12 December 2013}}</ref>

=== 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 – MAQC Project<!-- Bot generated title -->]</ref> The [[MGED Society]] has developed standards for the representation of gene expression experiment results and relevant annotations.

=== Data analysis ===
{{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|access-date=22 June 2017}}</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'').
* 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>
* 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. 
* 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>
<!-- {{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.|name-list-style=amp |date=2008 |title=Analysis of Microarray Data A Network-Based Approach |publisher=Wiley-VCH |isbn=978-3-527-31822-3}}</ref> [[Weighted correlation network analysis|Weighted gene co-expression network analysis]] is widely used for identifying co-expression modules and intramodular hub genes. Modules may corresponds to cell types or pathways. Highly connected intramodular hubs best represent their respective modules.

Microarray data may require further processing aimed at reducing the dimensionality of the data to aid comprehension and more focused analysis.<ref>{{cite journal
 | author = Wouters L| author2 = Gõhlmann HW| author3 = Bijnens L| author4 = Kass SU| author5 = Molenberghs G| author6 = Lewi PJ | date = 2003
 | title = Graphical exploration of gene expression data: a comparative study of three multivariate methods
 | journal = Biometrics
 | volume = 59 | pages = 1131–1139
 | doi = 10.1111/j.0006-341X.2003.00130.x
 | 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
 | journal = Bioinformatics
 | volume = 19 | pages = 1945–1951
 | doi = 10.1093/bioinformatics/btg264
 | pmid = 14555628
 | issue = 15
| doi-access = free}}</ref>

=== Annotation ===
The relation between a probe and the [[mRNA]] that it is expected to detect is not trivial.<ref>{{cite journal|last1=Barbosa-Morais|first1=N. L.|last2=Dunning|first2=M. J.|last3=Samarajiwa|first3=S. A.|last4=Darot|first4=J. F. J.|last5=Ritchie|first5=M. E.|last6=Lynch|first6=A. G.|last7=Tavare|first7=S.|title=A re-annotation pipeline for Illumina BeadArrays: improving the interpretation of gene expression data|journal=Nucleic Acids Research|date=18 November 2009|volume=38|issue=3|pages=e17|doi=10.1093/nar/gkp942|pmid=19923232|pmc=2817484}}</ref> Some mRNAs may cross-hybridize probes in the array that are supposed to detect another mRNA. In addition, mRNAs may experience amplification bias that is sequence or molecule-specific. Thirdly, probes that are designed to detect the mRNA of a particular gene may be relying on genomic [[Expressed sequence tag|EST]] information that is incorrectly associated with that gene.

=== 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 [[BioMart]], have been created for the specific purpose of integrating diverse biological datasets, and also support analysis.