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[[Image:Microarray2.gif|thumb|350px|Example of an approximately 40,000 probe spotted oligo microarray with enlarged inset to show detail.]]
'''Microarray analysis techniques''' are used in interpreting the data generated from experiments on DNA ('''Gene chip analysis'''), RNA, and protein [[microarray]]s, which allow researchers to investigate the expression state of a large number of genes
==Introduction==
Microarray data analysis is the final step in reading and processing data produced by a microarray chip. Samples undergo various processes including purification and scanning using the microchip, which then produces a large amount of data that requires processing via computer software. It involves several distinct steps, as outlined in the image below. Changing any one of the steps will change the outcome of the analysis, so the
[[File:Microarray exp horizontal.svg|thumb|800px|none|The steps required in a microarray experiment]]
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===Aggregation and normalization===
Comparing two different arrays or two different samples hybridized to the same array generally involves making adjustments for systematic errors introduced by differences in procedures and dye intensity effects. Dye normalization for two color arrays is often achieved by [[local regression]]. LIMMA provides a set of tools for background correction and scaling, as well as an option to average on-slide duplicate spots.<ref>{{cite web |url=http://bioinf.wehi.edu.au/limma/ |title=LIMMA Library: Linear Models for Microarray Data |access-date=2008-01-01 }}</ref> A common method for evaluating how well normalized an array is, is to plot an [[MA plot]] of the data. MA plots can be produced using programs and languages such as R
Raw Affy data contains about twenty probes for the same RNA target. Half of these are "mismatch spots",
The current Affymetrix MAS5 algorithm, which uses both perfect match and mismatch probes, continues to enjoy popularity and do well in head to head tests.<ref>{{cite journal |vauthors=Lim WK, Wang K, Lefebvre C, Califano A |title=Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks |journal=Bioinformatics |volume=23 |issue=13 |pages=i282–8 |year=2007 |pmid=17646307 |doi=10.1093/bioinformatics/btm201|doi-access=free }}</ref>
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===Identification of significant differential expression===
Many strategies exist to identify array probes that show an unusual level of over-expression or under-expression. The simplest one is to call "significant" any probe that differs by an average of at least twofold between treatment groups. More sophisticated approaches are often related to [[t-test]]s or other mechanisms that take both [[effect size]] and variability into account.
=== Clustering ===
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==== Hierarchical clustering ====
{{main|Hierarchical clustering}}
Hierarchical clustering is a statistical method for finding relatively [[Homogeneity and heterogeneity#Homogeneity|homogeneous]] clusters. Hierarchical clustering consists of two separate phases. Initially, a [[distance matrix]] containing all the pairwise distances between the genes is calculated. [[Pearson product-moment correlation coefficient|Pearson's correlation]] and [[Spearman's rank correlation coefficient|Spearman's correlation]] are often used as dissimilarity estimates, but other methods, like [[Taxicab geometry|Manhattan distance]] or [[Euclidean distance]], can also be applied. Given the number of distance measures available and their influence in the clustering algorithm results, several studies have compared and evaluated different distance measures for the clustering of microarray data, considering their intrinsic properties and robustness to noise.<ref name=Gentleman>{{cite book|last1=Gentleman|first1=Robert|title=Bioinformatics and computational biology solutions using R and Bioconductor|date=2005|publisher=Springer Science+Business Media|___location=New York|isbn=978-0-387-29362-2|display-authors=etal}}</ref><ref name=Jaskowiak2013>{{cite journal|last1=Jaskowiak|first1=Pablo A.|last2=Campello|first2=Ricardo J.G.B.|last3=Costa|first3=Ivan G.|title=Proximity Measures for Clustering Gene Expression Microarray Data: A Validation Methodology and a Comparative Analysis|journal=IEEE/ACM Transactions on Computational Biology and Bioinformatics|volume=10|issue=4|pages=845–857|doi=10.1109/TCBB.2013.9|pmid=24334380|year=2013|s2cid=760277}}</ref><ref name=Jaskowiak2014>{{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> After calculation of the initial
* Single linkage (minimum method, nearest neighbor)
* Average linkage ([[UPGMA]])
* Complete linkage (maximum method, furthest neighbor)
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==== K-means clustering ====
{{main|k-means clustering}}
K-means clustering is an algorithm for grouping genes or samples based on pattern into ''K'' groups. Grouping is done by minimizing the sum of the squares of distances between the data and the corresponding cluster [[centroid]]. Thus the purpose of K-means clustering is to classify data based on similar expression.<ref>{{cite web |url=http://www.biostat.ucsf.edu/ |title=Home |website=biostat.ucsf.edu}}</ref> K-means clustering algorithm and some of its variants (including [[k-medoids]]) have been shown to produce good results for gene expression data (at least better than hierarchical clustering methods). Empirical comparisons of [[k-means]], [[k-medoids]], hierarchical methods and, different distance measures can be found in the literature.<ref name="Jaskowiak2014" /><ref name=Souto2011>{{cite journal|last1=de Souto|first1=Marcilio C. P.|last2=Costa|first2=Ivan G.|last3=de Araujo|first3=Daniel S. A.|last4=Ludermir|first4=Teresa B.|last5=Schliep|first5=Alexander|title=Clustering cancer gene expression data: a comparative study|journal=BMC Bioinformatics|volume=9|issue=1|pages=497|doi=10.1186/1471-2105-9-497|pmid=19038021|pmc=2632677|year=2008 |doi-access=free }}</ref>
===Pattern recognition===
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[[File:Funrich.jpg|thumb|500px|none|Example of FunRich tool output. Image shows the result of comparing 4 different genes.]]
Specialized software tools for statistical analysis to determine the extent of over- or under-expression of a gene in a microarray experiment relative to a reference state have also been developed to aid in identifying genes or gene sets associated with particular [[phenotype]]s. One such method of analysis, known as [[Gene Set Enrichment]] Analysis (GSEA), uses a [[Kolmogorov-Smirnov]]-style statistic to identify groups of genes that are regulated together.<ref name=":0">{{cite journal |vauthors=Subramanian A, Tamayo P, Mootha VK, etal |title=Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles |journal=Proc. Natl. Acad. Sci. U.S.A. |volume=102 |issue=43 |pages=15545–50 |year=2005 |pmid=16199517 |doi=10.1073/pnas.0506580102 |pmc=1239896|doi-access=free }}</ref> This third-party statistics package offers the user information on the genes or gene sets of interest, including links to entries in databases such as NCBI's [[GenBank]] and curated databases such as Biocarta<ref>{{cite web |url=http://www.biocarta.com/ |title=BioCarta - Charting Pathways of Life |access-date=2007-12-31 }}</ref> and [[Gene Ontology]]. Protein complex enrichment analysis tool (COMPLEAT) provides similar enrichment analysis at the level of protein complexes.<ref>{{cite journal |vauthors=Vinayagam A, Hu Y, Kulkarni M, Roesel C, etal |title= Protein Complex-Based Analysis Framework for High-Throughput Data Sets. 6, rs5 (2013). |journal= Sci. Signal. |volume=6 |issue=r5 |year=2013 |pmid= 23443684 |doi= 10.1126/scisignal.2003629 |url= http://www.flyrnai.org/compleat/ |pages=rs5 |pmc=3756668}}</ref> The tool can identify the dynamic protein complex regulation under different condition or time points. Related system, PAINT<ref>{{cite web |url=http://www.dbi.tju.edu/dbi/staticpages.php?page=tools&menu=37 |title=DBI Web |access-date=2007-12-31 |url-status=dead |archive-url=https://web.archive.org/web/20070705061522/http://www.dbi.tju.edu/dbi/staticpages.php?page=tools |archive-date=2007-07-05 }}</ref>
==Significance analysis of microarrays (SAM)==
[[Image:SAM.png|thumb|right]]
'''Significance analysis of microarrays (SAM)''' is a [[statistics|statistical technique]], established in 2001 by Virginia Tusher, [[Robert Tibshirani]] and [[Gilbert Chu]], for determining whether changes in [[gene expression]] are statistically significant. With the advent of [[DNA microarray]]s, it is now possible to measure the expression of thousands of genes in a single hybridization experiment.
SAM identifies statistically significant genes by carrying out gene specific [[Student's t-test|t-tests]] and computes a statistic ''d<sub>j</sub>'' for each gene ''j'', which measures the strength of the relationship between gene expression and a response variable.<ref name="R1">Chu, G., Narasimhan, B, Tibshirani, R, Tusher, V. "SAM "Significance Analysis of Microarrays" Users Guide and technical document." [http://www-stat.stanford.edu/~tibs/SAM/sam.pdf]</ref><ref name="R7"/><ref name="R8"><Zhang, S. (2007). "A comprehensive evaluation of SAM, the SAM R-package and a simple modification to improve its performance." BMC Bioinformatics 8: 230.</ref> This analysis uses [[non-parametric statistics]], since the data may not follow a [[normal distribution]]. The response variable describes and groups the data based on experimental conditions. In this method, repeated [[permutations]] of the data are used to determine if the expression of any gene is significant related to the response.
===Basic protocol===
* Perform [[microarray]] experiments — DNA microarray with oligo and cDNA primers, SNP arrays, protein arrays, etc.
* Input Expression Analysis in Microsoft Excel — see below
* Run SAM as a Microsoft Excel
* Adjust the Delta
* List Differentially Expressed Genes (Positively and Negatively Expressed Genes)
===Running SAM===
* SAM is available for download online at http://www-stat.stanford.edu/~tibs/SAM/ for academic and non-academic users after completion of a registration step.
* SAM is run as an Excel Add-In, and the
* [[Permutations]] are calculated based on the number of samples
* Block Permutations
** Blocks are batches of microarrays; for example for eight samples split into two groups (control and affected) there are 4!=24 permutations for each block and the total number of permutations is (24)(24)= 576.
the number of permutations is set by the user when imputing correct values for the data set to run SAM
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===Algorithm===
SAM calculates a test statistic for relative difference in gene expression based on permutation analysis of expression data and calculates a false discovery rate.
[[Image:Samcalc.jpg]] [[Image:RandS.jpg]]
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<math>\mathrm{False \ discovery \ rate \ (FDR) = \frac{Median \ (or \ 90^{th} \ percentile) \ of \ \# \ of \ falsely \ called \ genes}{Number \ of \ genes \ called \ significant}}</math>
'''Fold changes''' (t) are specified to guarantee genes called significant change at least a pre-specified amount.
The SAM algorithm can be stated as:
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* Correlates expression data to clinical parameters<ref name="R6"/>
* Correlates expression data with time<ref name="R1"/>
* Uses data permutation to estimates False Discovery Rate for multiple testing<ref name="R7"/><ref name="R8"/><ref name="R6"/><ref name="R5">{{cite journal | last1 = Larsson | first1 = O. W. C | last2 = Timmons | first2 = JA. | year = 2005 | title = Considerations when using the significance analysis of microarrays (SAM) algorithm | journal = BMC Bioinformatics | volume = 6 | page = 129 | doi = 10.1186/1471-2105-6-129 | pmid = 15921534 | pmc = 1173086 | doi-access = free }}</ref>
* Reports local false discovery rate (the FDR for genes having a similar d<sub>i</sub> as that gene)<ref name="R1"/> and miss rates <ref name="R1"/><ref name="R7"/>
* Can work with blocked design for when treatments are applied within different batches of arrays<ref name="R1"/>
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===Background correction===
Depending on the type of array, signal related
signal intensity of the area between spots. A variety of tools for background correction and further analysis are available from TIGR,<ref>{{cite web |url=http://www.tigr.org/software/microarray.shtml |title=J. Craig Venter Institute -- Software |access-date=2008-01-01 }}</ref> Agilent ([[GeneSpring]]),<ref>{{cite web |url=http://www.chem.agilent.com/scripts/pds.asp?lpage=27881 |title=Agilent | GeneSpring GX |access-date=2008-01-02 }}</ref> and [[Ocimum Bio Solutions]] (Genowiz).<ref>{{cite web |url=http://www3.ocimumbio.com/data-analysis-insights/analytical-tools/genowiz/ |title=Ocimum Biosolutions | Genowiz |access-date=2009-04-02 |url-status=dead |archive-url=https://web.archive.org/web/20091124165434/http://www3.ocimumbio.com/data-analysis-insights/analytical-tools/genowiz/ |archive-date=2009-11-24 }}</ref>
===Spot filtering===
Visual identification of local artifacts, such as printing or washing defects, may likewise suggest the removal of individual spots. This can take a substantial amount of time depending on the quality of array manufacture.
==See also==
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* [https://doi.org/10.1016/B978-0-12-809633-8.20163-5 Comparative Transcriptomics Analysis] in [https://www.sciencedirect.com/science/referenceworks/9780128096338 Reference Module in Life Sciences]
* [https://web.archive.org/web/20090615060922/http://www-stat-class.stanford.edu/~tibs/clickwrap/sam.html SAM download instructions]
* [http://mmjggl.caltech.edu/microarray/data_analysis_fundamentals_manual.pdf GeneChip® Expression Analysis-Data Analysis Fundamentals]
* [http://www.stat.duke.edu/~mw/ABS04/RefInfo/data_analysis_fundamentals_manual.pdf Duke data_analysis_fundamentals_manual]
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