Microarray analysis techniques: Difference between revisions

Revision as of 14:32, 11 July 2019

Revision as of 15:33, 30 September 2019

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==Techniques==

[[File:Toxicology Research at FDA (NCTR 1470) (6009042166).jpg|thumb|[[National Center for Toxicological Research]] scientist reviews microarray data]]

Most microarray manufacturers, such as [[Affymetrix]] and [[Agilent]],<ref>{{cite web|url=http://www.chem.agilent.com/Scripts/PCol.asp?lPage=494 |title=Agilent &#124; DNA Microarrays |accessdate=2008-01-02 |formaturl-status= |website= |deadurl=yesdead |archiveurl=https://web.archive.org/web/20071222130157/http://www.chem.agilent.com/Scripts/PCol.asp?lPage=494 |archivedate=December 22, 2007 }}</ref> provide commercial data analysis software alongside their microarray products. There are also open source options that utilize a variety of methods for analyzing microarray data.

===Aggregation and normalization===

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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>{{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}}</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 |accessdate=2007-12-31 |format= |website=}}</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 |accessdate=2007-12-31 |format= |website= |deadurlurl-status=yesdead |archiveurl=https://web.archive.org/web/20070705061522/http://www.dbi.tju.edu/dbi/staticpages.php?page=tools |archivedate=2007-07-05 |df= }}</ref> and SCOPE<ref>{{cite web |url=http://genie.dartmouth.edu/scope/ |title=SCOPE |accessdate=2007-12-31 |format= |website=}}</ref> performs a statistical analysis on gene promoter regions, identifying over and under representation of previously identified [[transcription factor]] response elements. Another statistical analysis tool is Rank Sum Statistics for Gene Set Collections (RssGsc), which uses rank sum probability distribution functions to find gene sets that explain experimental data.<ref>{{cite web |url=http://rssgsc.sourceforge.net/ |title=RssGsc |accessdate=2008-10-15 |format= |website=}}</ref> A further approach is contextual meta-analysis, i.e. finding out how a gene cluster responds to a variety of experimental contexts. [[Genevestigator]] is a public tool to perform contextual meta-analysis across contexts such as anatomical parts, stages of development, and response to diseases, chemicals, stresses, and [[neoplasms]].

==Significance analysis of microarrays (SAM)==

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===Background correction===

Depending on the type of array, signal related to nonspecific binding of the fluorophore can be subtracted to achieve better results. One approach involves subtracting the average

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 |accessdate=2008-01-01 |website=}}</ref> Agilent ([[GeneSpring]]),<ref>{{cite web |url=http://www.chem.agilent.com/scripts/pds.asp?lpage=27881 |title=Agilent &#124; GeneSpring GX |accessdate=2008-01-02 |format= |website=}}</ref> and [[Ocimum Bio Solutions]] (Genowiz).<ref>{{cite web |url=http://www3.ocimumbio.com/data-analysis-insights/analytical-tools/genowiz/ |title=Ocimum Biosolutions &#124; Genowiz |accessdate=2009-04-02 |format= |website= |deadurlurl-status=yesdead |archiveurl=https://web.archive.org/web/20091124165434/http://www3.ocimumbio.com/data-analysis-insights/analytical-tools/genowiz/ |archivedate=2009-11-24 |df= }}</ref>

===Spot filtering===