Microarray analysis techniques: Difference between revisions

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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, 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. Most microarray manufacturers, such as [[Affymetrix]], provide commercial data analysis software with microarray equipment such as plate readers. 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 availible 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>

==Introduction==

Microarray data analysis involves several distinct steps, as outlined below. This following mostly relates to DNA microarrays.

==Creating raw data==

Most microarray manufacturers, such as [[Affymetrix]], provide commercial data analysis software with microarray equipment such as plate readers. Raw Affy data contains information about mismatch spots, spots which do not precisely match the target sequence. These can theoretically measure the amount of nonspecific binding for a given target, but some popular approaches like RMA do not take advantage of them.<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=}}</ref>

==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.

==Quality control==

Entire arrays have obvious flaws detectable by visual inspection, pairwise comparisons to other arrays, or by analysis of RNA degredation. Results may improve by removing these arrays from the analysis entirely.

==Spot filtering==

Visual identification of local artifacts, such as printing or washing defects, may likewise suggest the removal of individual spots. In addition, some procedures call for the elimination of all spots with an expression value below a certain threshold.

==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. In the case of Affymetrics arrays, there are multiple probesets on an array for the same target sequence, requiring some sort of summarization. The RMA method uses [[median polish]] as opposed to a straight average. Dye normaization for two color arrays is often acheived by [[local regression]]. Quantile normalization, also part of RMA, is one sensible approach to normalize a batch of arrays in order to make further comparisons meaningful.

==Identification of significant differential expression==

Many strategies exist to idenitfy which array probes show an unusual level of over expression or under expression. The simplest one is to call "significant" any probe that differs by at least two fold, on average between treatment groups. More sophicated approaches are often related to [[t test]]s or other mechanisms that take both effect size and variability into account.

==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 availible 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}}</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.