Microarray analysis techniques: Difference between revisions

Revision as of 11:27, 15 November 2011

Revision as of 18:42, 23 January 2012

Line 1:

[[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, RNA, and protein [[microarray]]s, which allow researchers to investigate the expression state of a large number of genes - in many cases, an organism's entire [[genome]] - in a single experiment. Such experiments generate a very large volume of genetic data that can be difficult to analyze, especially in the absence of good gene annotation.

==Introduction==

Microarray data analysis involves several distinct steps, as outlined below. Changing any one of the steps has the potential to change the outcome of the analysis, so the MAQC Project<ref>{{cite web | url = http://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/ | title = MicroArray Quality Control (MAQC) Project | accessdate = 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 |accessdate=2008-01-02 |format= |work=}}</ref>

==Creating raw 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 |format= |work=}} {{Dead link|date=October 2010|bot=H3llBot}}</ref>, provide commercial data analysis software with microarray equipment such as plate readers.

==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 |format= |work=}}</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= |work=}}</ref> and Ocimum Biosolutions (Genowiz).<ref>{{cite web |url=http://www.ocimumbio.com/web/bioinformatic/prod_details.asp?prod_id=20&prodType=1 |title=Ocimum Biosolutions &#124; Genowiz |accessdate=2009-04-02 |format= |work=}}</ref>

==Quality control==

Entire arrays may have obvious flaws detectable by visual inspection, pairwise comparisons to arrays in the same experimental group, or by analysis of RNA degradation.<ref>{{cite journal |author=Wilson CL, Miller CJ |title=Simpleaffy: a BioConductor package for Affymetrix Quality Control and data analysis |journal=Bioinformatics |volume=21 |issue=18 |pages=3683–5 |year=2005 |pmid=16076888 |doi=10.1093/bioinformatics/bti605}}</ref>. Results may improve by removing these arrays from the analysis entirely.

==Spot filtering==

Line 18 ⟶ 19:

==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 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 |accessdate=2008-01-01 |format= |work=}}</ref>. A common method for evaluating how well normalized an array is, is to plot an [[MA plot]] of the data.

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 Multichip Average (RMA) 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 |author=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}}</ref> The median polish algorithm, although robust, behaves differently depending on the number of samples analyzed.<ref>{{cite journal |author=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.

Line 30 ⟶ 31:

==Pattern recognition==

Commercial systems for gene network analysis such as Ingenuity<ref> {{cite web |url=http://www.ingenuity.com/ |title=Ingenuity Systems |accessdate=2007-12-31 |format= |work=}} </ref> and Pathway studio<ref>{{cite web |url=http://www.ariadnegenomics.com/products/pathway-studio/ |title=Ariadne Genomics: Pathway Studio |accessdate=2007-12-31 |format= |work=}}</ref> create visual representations of differentially expressed genes based on current scientific literature. Non-commercial tools such as [[GenMAPP]] 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 Excel module and other microarray tools<ref>[{{cite web |url=http://www-stat.stanford.edu/~tibs/SAM/ |title=Significance Analysis of Microarrays |accessdate=2007-12-31 |format= |work=}}]</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 |accessdate=2007-12-31 |format= |work=}}</ref>.

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 |author=Subramanian A, Tamayo P, Mootha VK, ''et al.'' |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= |work=}}] </ref> and [[Gene Ontology]]. 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= |work=}}</ref> and SCOPE<ref>{{cite web |url=http://genie.dartmouth.edu/scope/ |title=SCOPE |accessdate=2007-12-31 |format= |work=}}</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= |work=}}</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]].

==References==

{{Reflist|2}}

==External links==

* [http://www.bioinf.jku.at/software/farms/farms.html FARMS - Factor Analysis for Robust Microarray Summarization, an R package] &mdash;software

[[Category:Microarrays]]

[[Category:Bioinformatics algorithms]]