Microarray analysis techniques: Difference between revisions

Revision as of 06:00, 2 August 2014

Revision as of 07:47, 3 August 2014

Line 28:

==Identification of significant differential expression==

Many strategies exist to identify 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 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. Curiously, the p-values associated with particular genes do not reproduce well between replicate experiments, and lists generated by straight fold change perform much better.<ref>{{cite journal |author=Shi L, Reid LH, Jones WD, et al. |title=The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements |journal=Nat. Biotechnol. |volume=24 |issue=9 |pages=1151–61 |year=2006 |pmid=16964229 |doi=10.1038/nbt1239 |pmc=3272078}}</ref><ref>{{cite journal |author=Guo L, Lobenhofer EK, Wang C, et al. |title=Rat toxicogenomic study reveals analytical consistency across microarray platforms |journal=Nat. Biotechnol. |volume=24 |issue=9 |pages=1162–9 |year=2006 |pmid=17061323 |doi=10.1038/nbt1238}}</ref> This represents an extremely important observation, since the point of performing experiments has to do with predicting general behavior. The MAQC group recommends using a fold change assessment plus a non-stringent p-value cutoff, further pointing out that changes in the background correction and scaling process have only a minimal impact on the rank order of fold change differences, but a substantial impact on p-values.

==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]] 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 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 |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]]. Protein complex enrichment analysis tool (COMPLEAT) provides similar enrichment analysis at the level of protein complexes.<ref>{{cite journal |author=Vinayagam A, Hu Y, Kulkarni M, Roesel C, et al. |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}}</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= |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==