Microarray analysis techniques: Difference between revisions

Revision as of 05:12, 18 January 2016

Revision as of 16:52, 3 June 2016

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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.{{factcitation needed|date=February 2015}} Such experiments can generate very large volumes of data, allowing researchers to assess the overall state of a cell or organism. These large data amount can be difficult to analyze, especially in the absence of good gene annotation.

==Introduction==

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==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 |authorvauthors=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==

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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 |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 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}}</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 |authorvauthors=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 |authorvauthors=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.

The current Affymetrix MAS5 algorithm, which uses both perfect match and mismatch probes, continues to enjoy popularity and do well in head to head tests.<ref>{{cite journal |authorvauthors=Lim WK, Wang K, Lefebvre C, Califano A |title=Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks |journal=Bioinformatics |volume=23 |issue=13 |pages=i282–8 |year=2007 |pmid=17646307 |doi=10.1093/bioinformatics/btm201}}</ref>

Factor Analysis for Robust Microarray Summarization (FARMS)<ref>[[Sepp Hochreiter]], Djork-Arné Clevert, and Klaus Obermayer, 2006. A new summarization method for affymetrix probe level data" ''Bioinformatics'' 22(8), 943-949. [http://bioinformatics.oxfordjournals.org/cgi/content/short/22/8/943]</ref> is a model-based technique for summarizing array data at perfect match probe level. It is based on a factor analysis model for which a Bayesian maximum a posteriori method optimizes the model parameters under the assumption of Gaussian measurement noise. According to the Affycomp benchmark<ref>http://affycomp.jhsph.edu/</ref> FARMS outperformed all other summarizations methods with respect to sensitivity and specificity.