Microarray analysis techniques: Difference between revisions

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Revision as of 20:14, 26 March 2023

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{{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}}</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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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>{{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=http://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]].

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===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 }}</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 }}</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===

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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 }}</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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==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]