Microarray analysis techniques: Difference between revisions

Revision as of 07:42, 30 November 2020

Revision as of 12:38, 9 December 2020

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==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 MAQC Project<ref>{{cite web | url = https://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/ | title = MicroArray Quality Control (MAQC) Project | accessdateaccess-date = 2007-12-26 | author = Dr. Leming Shi, National Center for Toxicological Research | publisher = U.S. Food and Drug Administration }}</ref> was created to identify a set of standard strategies. Companies exist that use the MAQC protocols to perform a complete analysis.<ref>{{cite web |url=http://www.genusbiosystems.com/services-data.shtml |title=GenUs BioSystems - Services - Data Analysis |accessdateaccess-date=2008-01-02 |website=}}</ref>

[[File:Microarray exp horizontal.svg|thumb|800px|none|The steps required in a microarray experiment]]

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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 |accessdateaccess-date=2008-01-02 |url-status=dead |archiveurlarchive-url=https://web.archive.org/web/20071222130157/http://www.chem.agilent.com/Scripts/PCol.asp?lPage=494 |archivedatearchive-date=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===

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 |accessdateaccess-date=2008-01-01 |format= |website=}}</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, MATLAB, and Excel.

Raw Affy data contains about twenty probes for the same RNA target. Half of these are "mismatch spots", which do not precisely match the target sequence. These can theoretically measure the amount of nonspecific binding for a given target. Robust Multi-array Average (RMA) <ref>{{cite journal|last=Irizarry|first=RA|author2=Hobbs, B |author3=Collin, F |author4=Beazer-Barclay, YD |author5=Antonellis, KJ |author6=Scherf, U |author7= Speed, TP |title=Exploration, normalization, and summaries of high density oligonucleotide array probe level data.|journal=Biostatistics|volume=4|issue=2|pages=249–64|year=2003|pmid=12925520 |doi=10.1093/biostatistics/4.2.249|doi-access=free}}</ref> is a normalization approach that does not take advantage of these mismatch spots, but still must summarize the perfect matches through [[median polish]].<ref>{{cite journal |vauthors=Bolstad BM, Irizarry RA, Astrand M, Speed TP |title=A comparison of normalization methods for high density oligonucleotide array data based on variance and bias |journal=Bioinformatics |volume=19 |issue=2 |pages=185–93 |year=2003 |pmid=12538238 |doi=10.1093/bioinformatics/19.2.185|doi-access=free }}</ref> The median polish algorithm, although robust, behaves differently depending on the number of samples analyzed.<ref>{{cite journal |vauthors=Giorgi FM, Bolger AM, Lohse M, Usadel B |title=Algorithm-driven Artifacts in median polish summarization of Microarray data |journal=BMC Bioinformatics |volume=11 |pages=553 |year=2010 |pmid=21070630 |doi=10.1186/1471-2105-11-553 |pmc=2998528}}</ref> Quantile normalization, also part of RMA, is one sensible approach to normalize a batch of arrays in order to make further comparisons meaningful.

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===Pattern recognition===

Commercial systems for gene network analysis such as Ingenuity<ref>{{cite web |url=http://www.ingenuity.com/ |title=Ingenuity Systems |accessdateaccess-date=2007-12-31 |format= |website=}}</ref> and Pathway studio<ref>{{cite web |url=http://www.ariadnegenomics.com/products/pathway-studio/ |title=Ariadne Genomics: Pathway Studio |accessdateaccess-date=2007-12-31 |format= |website= |archive-url=https://web.archive.org/web/20071230035556/http://www.ariadnegenomics.com/products/pathway-studio |archive-date=2007-12-30 |url-status=dead }}</ref> create visual representations of differentially expressed genes based on current scientific literature. Non-commercial tools such as FunRich,<ref>{{cite web |url=http://www.funrich.org/ |title=FunRich: Functional Enrichment Analysis |accessdateaccess-date=2014-09-09 |format= |website=}}</ref> [[GenMAPP]] and [[Anduril (workflow engine)#Moksiskaan|Moksiskaan]] also aid in organizing and visualizing gene network data procured from one or several microarray experiments. A wide variety of microarray analysis tools are available through [[Bioconductor]] written in the [[R programming language]]. The frequently cited SAM module and other microarray tools<ref>[{{cite web |url=http://www-stat.stanford.edu/~tibs/SAM/ |title=Significance Analysis of Microarrays |accessdateaccess-date=2007-12-31 |format= |website=}}]</ref> are available through Stanford University. Another set is available from Harvard and MIT.<ref>{{cite web |url=http://www.broad.mit.edu/tools/software.html |title=Software - Broad |accessdateaccess-date=2007-12-31 |website=}}</ref>

[[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 |accessdateaccess-date=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 |accessdateaccess-date=2007-12-31 |url-status=dead |archiveurlarchive-url=https://web.archive.org/web/20070705061522/http://www.dbi.tju.edu/dbi/staticpages.php?page=tools |archivedatearchive-date=2007-07-05 }}</ref> and SCOPE<ref>{{cite web |url=http://genie.dartmouth.edu/scope/ |title=SCOPE |accessdateaccess-date=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 |accessdateaccess-date=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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*[[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. A minimum of 1000 permutations are recommended;<ref name="R1"/><ref name="R2">{{cite journal | last1 = Dinu | first1 = I. P. | last2 = JD | last3 = Mueller | first3 = T | last4 = Liu | first4 = Q | last5 = Adewale | first5 = AJ | last6 = Jhangri | first6 = GS | last7 = Einecke | first7 = G | last8 = Famulski | first8 = KS | last9 = Halloran | first9 = P | last10 = Yasui | first10 = Y. | year = 2007 | title = Improving gene set analysis of microarray data by SAM-GS. | url = | journal = BMC Bioinformatics | volume = 8 | issue = | page = 242 | doi=10.1186/1471-2105-8-242| pmid = 17612399 | pmc = 1931607 }}</ref><ref name="R3">{{cite journal | last1 = Jeffery | first1 = I. H. | last2 = DG | last3 = Culhane | first3 = AC. | year = 2006 | title = Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data | url = | journal = BMC Bioinformatics | volume = 7 | issue = | page = 359 | doi=10.1186/1471-2105-7-359| pmid = 16872483 | pmc = 1544358 }}</ref>

the number of permutations is set by the user when imputing correct values for the data set to run SAM

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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 | url = | journal = BMC Bioinformatics | volume = 6 | issue = | page = 129 | doi = 10.1186/1471-2105-6-129 | pmid = 15921534 | pmc = 1173086 }}</ref>

*Reports local false discovery rate (the FDR for genes having a similar d_i 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 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 |accessdateaccess-date=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 |accessdateaccess-date=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 |accessdateaccess-date=2009-04-02 |url-status=dead |archiveurlarchive-url=https://web.archive.org/web/20091124165434/http://www3.ocimumbio.com/data-analysis-insights/analytical-tools/genowiz/ |archivedatearchive-date=2009-11-24 }}</ref>

===Spot filtering===

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{{reflist|refs=

<ref name="R6">{{cite journal | last1 = Tusher | first1 = V. G. | last2 = Tibshirani | first2 = R. | display-authors =et al | year = 2001 | title = Significance analysis of microarrays applied to the ionizing radiation response | url = http://www-stat.stanford.edu/~tibs/SAM/pnassam.pdf | journal = Proceedings of the National Academy of Sciences | volume = 98 | issue = 9| pages = 5116–5121 | doi=10.1073/pnas.091062498| pmid = 11309499 | pmc = 33173 | bibcode = 2001PNAS...98.5116G }}</ref>

<ref name="R7">{{cite journal | last1 = Zang | first1 = S. | last2 = Guo | first2 = R. | display-authors = et al | year = 2007 | title = Integration of statistical inference methods and a novel control measure to improve sensitivity and specificity of data analysis in expression profiling studies | url = | journal = Journal of Biomedical Informatics | volume = 40 | issue = 5| pages = 552–560 | doi=10.1016/j.jbi.2007.01.002| pmid = 17317331 }}</ref>

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