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 ('''Gene chip analysis'''), RNA, and protein [[microarray]]s, which allow researchers to investigate the expression state of a large number of genes - {{snd}}in many cases, an organism's entire [[genome]] - {{snd}}in a single experiment.{{citation<ref needed|datename=February":0" 2015}}/> Such experiments can generate very large amounts of data, allowing researchers to assess the overall state of a cell or organism. Data in such large quantities is difficult - {{snd}}if not impossible - {{snd}}to analyze without the help of computer programs.

==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/ | archive-url = https://web.archive.org/web/20051208055601/http://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/ | url-status = dead | archive-date = December 8, 2005 | title = MicroArray Quality Control (MAQC) Project | access-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 |access-date=2008-01-02 }}</ref>

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

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===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 |access-date=2008-01-01 }}</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, and MATLAB,.<ref>{{Cite journal |last1=Gatto |first1=Laurent |last2=Breckels |first2=Lisa M. |last3=Naake |first3=Thomas |last4=Gibb |first4=Sebastian |date=2015 |title=Visualization of proteomics data using R and ExcelBioconductor |journal=Proteomics |language=en |volume=15 |issue=8 |pages=1375–1389 |doi=10.1002/pmic.201400392 |issn=1615-9853 |pmc=4510819 |pmid=25690415}}</ref><ref>{{Cite web |title=Create intensity versus ratio scatter plot of microarray data - MATLAB mairplot |url=https://www.mathworks.com/help/bioinfo/ref/mairplot.html#responsive_offcanvas |access-date=2023-11-24 |website=MathWorks}}</ref>

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|lastlast1=Irizarry|firstfirst1=RA|author-link1=Rafael Irizarry (scientist) |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 |doi-access=free }}</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 |vauthors=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|doi-access=free }}</ref>

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===Identification of significant differential expression===

Many strategies exist to identify array probes that 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 name=":1">{{cite journal |vauthors=Shi L, Reid LH, Jones WD, etal |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 |vauthors=Guo L, Lobenhofer EK, Wang C, etal |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|s2cid=8192240 }}</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.<ref name=":1" />

=== Clustering ===

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==== Hierarchical clustering ====

{{main|Hierarchical clustering}}

Hierarchical clustering is a statistical method for finding relatively [[Homogeneity and heterogeneity#Homogeneity|homogeneous]] clusters. Hierarchical clustering consists of two separate phases. Initially, a [[distance matrix]] containing all the pairwise distances between the genes is calculated. [[Pearson product-moment correlation coefficient|Pearson's correlation]] and [[Spearman's rank correlation coefficient|Spearman's correlation]] are often used as dissimilarity estimates, but other methods, like [[Taxicab geometry|Manhattan distance]] or [[Euclidean distance]], can also be applied. Given the number of distance measures available and their influence in the clustering algorithm results, several studies have compared and evaluated different distance measures for the clustering of microarray data, considering their intrinsic properties and robustness to noise.<ref name=Gentleman>{{cite book|last1=Gentleman|first1=Robert|title=Bioinformatics and computational biology solutions using R and Bioconductor|date=2005|publisher=Springer Science+Business Media|___location=New York|isbn=978-0-387-29362-2|display-authors=etal}}</ref><ref name=Jaskowiak2013>{{cite journal|last1=Jaskowiak|first1=Pablo A.|last2=Campello|first2=Ricardo J.G.B.|last3=Costa|first3=Ivan G.|title=Proximity Measures for Clustering Gene Expression Microarray Data: A Validation Methodology and a Comparative Analysis|journal=IEEE/ACM Transactions on Computational Biology and Bioinformatics|volume=10|issue=4|pages=845–857|doi=10.1109/TCBB.2013.9|pmid=24334380|year=2013|s2cid=760277}}</ref><ref name=Jaskowiak2014>{{cite journal|last1=Jaskowiak|first1=Pablo A|last2=Campello|first2=Ricardo JGB|last3=Costa|first3=Ivan G|title=On the selection of appropriate distances for gene expression data clustering|journal=BMC Bioinformatics|volume=15|issue=Suppl 2|pages=S2|doi=10.1186/1471-2105-15-S2-S2|pmid=24564555|pmc=4072854|year=2014 |doi-access=free }}</ref> After calculation of the initial distance matrix, the hierarchical clustering algorithm either (A) joins iteratively the two closest clusters starting from single data points (agglomerative, bottom-up approach, which is fairly more commonly used), or (B) partitions clusters iteratively starting from the complete set (divisive, top-down approach). After each step, a new distance matrix between the newly formed clusters and the other clusters is recalculated. Hierarchical [[cluster analysis]] methods include:

*Complete Single linkage (maximumminimum method, furthestnearest neighbor)

*Single Average linkage (minimum method, nearest neighbor[[UPGMA]])

*Average Complete linkage ([[UPGMA]]maximum method, furthest neighbor).

*Complete linkage (maximum method, furthest neighbor)

Different studies have already shown empirically that the Single linkage clustering algorithm produces poor results when employed to gene expression microarray data and thus should be avoided.<ref name="Jaskowiak2014" /><ref name="Souto2011" />

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==== K-means clustering ====

{{main|k-means clustering}}

K-means clustering is an algorithm for grouping genes or samples based on pattern into ''K'' groups. Grouping is done by minimizing the sum of the squares of distances between the data and the corresponding cluster [[centroid]]. Thus the purpose of K-means clustering is to classify data based on similar expression.<ref>{{cite web |url=http://www.biostat.ucsf.edu/ |title=Home |website=biostat.ucsf.edu}}</ref> K-means clustering algorithm and some of its variants (including [[k-medoids]]) have been shown to produce good results for gene expression data (at least better than hierarchical clustering methods). Empirical comparisons of [[k-means]], [[k-medoids]], hierarchical methods and, different distance measures can be found in the literature.<ref name="Jaskowiak2014" /><ref name=Souto2011>{{cite journal|last1=de Souto|first1=Marcilio C. P.|last2=Costa|first2=Ivan G.|last3=de Araujo|first3=Daniel S. A.|last4=Ludermir|first4=Teresa B.|last5=Schliep|first5=Alexander|title=Clustering cancer gene expression data: a comparative study|journal=BMC Bioinformatics|volume=9|issue=1|pages=497|doi=10.1186/1471-2105-9-497|pmid=19038021|pmc=2632677|year=2008 |doi-access=free }}</ref>

===Pattern recognition===

Commercial systems for gene network analysis such as Ingenuity<ref>{{cite web |url=http://www.ingenuity.com/ |title=Ingenuity Systems |access-date=2007-12-31 }}</ref> and Pathway studio<ref>{{cite web |url=http://www.ariadnegenomics.com/products/pathway-studio/ |title=Ariadne Genomics: Pathway Studio |access-date=2007-12-31 |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 |access-date=2014-09-09 }}</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 |access-date=2007-12-31 }}]</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 |access-date=2007-12-31 }}</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 name=":0">{{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|doi-access=free }}</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 |access-date=2007-12-31 }}</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 |access-date=2007-12-31 |url-status=dead |archive-url=https://web.archive.org/web/20070705061522/http://www.dbi.tju.edu/dbi/staticpages.php?page=tools |archive-date=2007-07-05 }}</ref> and SCOPE<ref>{{cite web |url=http://genie.dartmouth.edu/scope/ |title=SCOPE |access-date=2007-12-31 |archive-date=2011-08-17 |archive-url=https://web.archive.org/web/20110817031914/http://genie.dartmouth.edu/scope/ |url-status=dead }}</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=httphttps://rssgsc.sourceforge.net/ |title=RssGsc |access-date=2008-10-15 }}</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)==

[[Image:SAM.png|thumb|right]]

'''Significance analysis of microarrays (SAM)''' is a [[statistics|statistical technique]], established in 2001 by Virginia Tusher, [[Robert Tibshirani]] and [[Gilbert Chu]], for determining whether changes in [[gene expression]] are statistically significant. With the advent of [[DNA microarray]]s, it is now possible to measure the expression of thousands of genes in a single hybridization experiment. The data generated is considerable, and a method for sorting out what is significant and what isn't is essential. SAM is distributed by [[Stanford University]] in an [[R (programming language)|R-package]].<ref>{{Cite web |title=SAM: Significance Analysis of Microarrays |url=https://tibshirani.su.domains/SAM/ |access-date=2023-11-24 |website=tibshirani.su.domains}}</ref>

SAM identifies statistically significant genes by carrying out gene specific [[Student's t-test|t-tests]] and computes a statistic ''d_j'' for each gene ''j'', which measures the strength of the relationship between gene expression and a response variable.<ref name="R1">Chu, G., Narasimhan, B, Tibshirani, R, Tusher, V. "SAM "Significance Analysis of Microarrays" Users Guide and technical document." [http://www-stat.stanford.edu/~tibs/SAM/sam.pdf]</ref><ref name="R7"/><ref name="R8"><Zhang, S. (2007). "A comprehensive evaluation of SAM, the SAM R-package and a simple modification to improve its performance." BMC Bioinformatics 8: 230.</ref> This analysis uses [[non-parametric statistics]], since the data may not follow a [[normal distribution]]. The response variable describes and groups the data based on experimental conditions. In this method, repeated [[permutations]] of the data are used to determine if the expression of any gene is significant related to the response. The use of permutation-based analysis accounts for correlations in genes and avoids [[wikt:Special:Search/parametric|parametric]] assumptions about the distribution of individual genes. This is an advantage over other techniques (e.g., [[ANOVA]] and [[Bonferroni]]), which assume equal variance and/or independence of genes.<ref name="R6"/>

===Basic protocol===

* Perform [[microarray]] experiments &mdash;— DNA microarray with oligo and cDNA primers, SNP arrays, protein arrays, etc.

* Input Expression Analysis in Microsoft Excel &mdash;— see below

* Run SAM as a Microsoft Excel Add-Ins

* Adjust the Delta tuning parameter to get a significant # of genes along with an acceptable false discovery rate (FDR)) and Assess Sample Size by calculating the mean difference in expression in the SAM Plot Controller

* List Differentially Expressed Genes (Positively and Negatively Expressed Genes)

===Running SAM===

* SAM is available for download online at http://www-stat.stanford.edu/~tibs/SAM/ for academic and non-academic users after completion of a registration step.

* SAM is run as an Excel Add-In, and the SAM Plot Controller allows Customization of the False Discovery Rate and Delta, while the SAM Plot and SAM Output functionality generate a List of Significant Genes, Delta Table, and Assessment of Sample Sizes

* [[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. | journal = BMC Bioinformatics | volume = 8 | page = 242 | doi=10.1186/1471-2105-8-242| pmid = 17612399 | pmc = 1931607 | doi-access = free }}</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 | journal = BMC Bioinformatics | volume = 7 | page = 359 | doi=10.1186/1471-2105-7-359| pmid = 16872483 | pmc = 1544358 | doi-access = free }}</ref>

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

====Response formats====

Types:<ref name="R1"/>

* '''Quantitative''' &mdash;— real-valued (such as heart rate)

* '''One class''' &mdash;— tests whether the mean gene expression differs from zero

* '''Two class''' &mdash;— two sets of measurements

** '''Unpaired''' &mdash;— measurement units are different in the two groups; e.g. control and treatment groups with samples from different patients

** '''Paired''' &mdash;— same experimental units are measured in the two groups; e.g. samples before and after treatment from the same patients

* '''Multiclass''' &mdash;— more than two groups with each containing different experimental units; generalization of two class unpaired type

* '''Survival''' &mdash;— data of a time until an event (for example death or relapse)

* '''Time course''' &mdash;— each experimental units is measured at more than one time point; experimental units fall into a one or two class design

* '''Pattern discovery''' &mdash;— no explicit response parameter is specified; the user specifies eigengene (principal component) of the expression data and treats it as a quantitative response

===Algorithm===

SAM calculates a test statistic for relative difference in gene expression based on permutation analysis of expression data and calculates a false discovery rate. The principal calculations of the program are illustrated below.<ref name="R1"/><ref name="R7"/><ref name="R8"/>

[[Image:Samcalc.jpg]] [[Image:RandS.jpg]]

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<math>\mathrm{False \ discovery \ rate \ (FDR) = \frac{Median \ (or \ 90^{th} \ percentile) \ of \ \# \ of \ falsely \ called \ genes}{Number \ of \ genes \ called \ significant}}</math>

'''Fold changes''' (t) are specified to guarantee genes called significant change at least a pre-specified amount. This means that the absolute value of the average expression levels of a gene under each of two conditions must be greater than the fold change (t) to be called positive and less than the inverse of the fold change (t) to be called negative.

The SAM algorithm can be stated as:

# Order test statistics according to magnitude <ref name="R7"/><ref name="R8"/>

# For each permutation compute the ordered null (unaffected) scores <ref name="R7"/><ref name="R8"/>

# Plot the ordered test statistic against the expected null scores <ref name="R7"/><ref name="R8"/>

# Call each gene significant if the absolute value of the test statistic for that gene minus the mean test statistic for that gene is greater than a stated threshold <ref name="R8"/>

# Estimate the false discovery rate based on expected versus observed values <ref name="R7"/><ref name="R8"/>

====Output====

* Significant gene sets

** Positive gene set &mdash;— higher expression of most genes in the gene set correlates with higher values of the phenotype {{var|y}}

** Negative gene set &mdash;— lower expression of most genes in the gene set correlates with higher values of the phenotype {{var|y}}

===SAM features===

* Data from Oligo or cDNA arrays, SNP array, protein arrays, etc. can be utilized in SAM<ref name="R7"/><ref name="R8"/>

* 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 | journal = BMC Bioinformatics | volume = 6 | page = 129 | doi = 10.1186/1471-2105-6-129 | pmid = 15921534 | pmc = 1173086 | doi-access = free }}</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"/>

* Can adjust threshold determining number of gene called significant<ref name="R1"/>

==Error correction and quality control==

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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 |access-date=2008-01-01 }}</ref> Agilent ([[GeneSpring]]),<ref>{{cite web |url=http://www.chem.agilent.com/scripts/pds.asp?lpage=27881 |title=Agilent &#124; GeneSpring GX |access-date=2008-01-02 }}</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 |access-date=2009-04-02 |url-status=dead |archive-url=https://web.archive.org/web/20091124165434/http://www3.ocimumbio.com/data-analysis-insights/analytical-tools/genowiz/ |archive-date=2009-11-24 }}</ref>

===Spot filtering===

Visual identification of local artifacts, such as printing or washing defects, may likewise suggest the removal of individual spots. This can take a substantial amount of time depending on the quality of array manufacture. In addition, some procedures call for the elimination of all spots with an expression value below a certain intensity threshold.

==See also==

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==External links==

* [https://web.archive.org/web/20130525084842/http://arrayexplorer.com/ ArrayExplorer - Compare microarray side by side to find the one that best suits your research needs]

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

* [http://statsarray.com/ StatsArray - Online Microarray Analysis Services] &mdash;software—software

* [http://www.arraymining.net ArrayMining.net - web-application for online analysis of microarray data] &mdash;software—software

* [https://web.archive.org/web/20160611175636/http://www.funrich.org/ FunRich - Perform gene set enrichment analysis] &mdash;software—software

* [https://doi.org/10.1016/B978-0-12-809633-8.20163-5 Comparative Transcriptomics Analysis] in [https://www.sciencedirect.com/science/referenceworks/9780128096338 Reference Module in Life Sciences]

* [https://web.archive.org/web/20090615060922/http://www-stat-class.stanford.edu/~tibs/clickwrap/sam.html SAM download instructions]

* [http://mmjggl.caltech.edu/microarray/data_analysis_fundamentals_manual.pdf GeneChip® Expression Analysis-Data Analysis Fundamentals] (by Affymetrix)

* [http://www.stat.duke.edu/~mw/ABS04/RefInfo/data_analysis_fundamentals_manual.pdf Duke data_analysis_fundamentals_manual]