Editing Flow cytometry (section)

===Computational analysis===

Recent progress on automated population identification using computational methods has offered an alternative to traditional gating strategies. Automated identification systems could potentially help findings of rare and hidden populations. Representative automated methods include FLOCK<ref>{{cite journal | vauthors = Qian Y, Wei C, Eun-Hyung Lee F, Campbell J, Halliley J, Lee JA, Cai J, Kong YM, Sadat E, Thomson E, Dunn P, Seegmiller AC, Karandikar NJ, Tipton CM, Mosmann T, Sanz I, Scheuermann RH | display-authors = 6 | title = Elucidation of seventeen human peripheral blood B-cell subsets and quantification of the tetanus response using a density-based method for the automated identification of cell populations in multidimensional flow cytometry data | journal = Cytometry Part B | volume = 78 | issue = Suppl 1 | pages = S69-82 | year = 2010 | pmid = 20839340 | pmc = 3084630 | doi = 10.1002/cyto.b.20554 }}</ref> in Immunology Database and Analysis Portal (ImmPort),<ref>{{cite web|url=https://www.immport.org/immportWeb/home/home.do?loginType=full |title=Immunology Database and Analysis Portal |access-date=2009-09-03 |url-status=dead |archive-url=https://web.archive.org/web/20110726174823/https://www.immport.org/immportWeb/home/home.do?loginType=full |archive-date=July 26, 2011 }}</ref> SamSPECTRAL<ref name="pmid20667133">{{cite journal | vauthors = Zare H, Shooshtari P, Gupta A, Brinkman RR | title = Data reduction for spectral clustering to analyze high throughput flow cytometry data | journal = BMC Bioinformatics | volume = 11 | pages = 403 | date = July 2010 | pmid = 20667133 | pmc = 2923634 | doi = 10.1186/1471-2105-11-403 | doi-access = free }}</ref> and flowClust<ref>{{cite web |url=http://www.bioconductor.org/packages/2.5/bioc/html/flowClust.html | title=flowClust |access-date=2009-09-03}}</ref><ref>{{cite journal | vauthors = Lo K, Brinkman RR, Gottardo R | title = Automated gating of flow cytometry data via robust model-based clustering | journal = Cytometry Part A | volume = 73 | issue = 4 | pages = 321–32 | date = April 2008 | pmid = 18307272 | doi = 10.1002/cyto.a.20531 | doi-access = free }}</ref><ref>{{cite journal | vauthors = Lo K, Hahne F, Brinkman RR, Gottardo R | title = flowClust: a Bioconductor package for automated gating of flow cytometry data | journal = BMC Bioinformatics | volume = 10 | pages = 145 | date = May 2009 | pmid = 19442304 | pmc = 2701419 | doi = 10.1186/1471-2105-10-145 | doi-access = free }}</ref> in [[Bioconductor]], and FLAME<ref>{{cite web|url=http://www.broadinstitute.org/cancer/software/genepattern/modules/FLAME/ |title=FLow analysis with Automated Multivariate Estimation (FLAME) |access-date=2009-09-03 |url-status=dead |archive-url=https://web.archive.org/web/20090821120132/http://broadinstitute.org/cancer/software/genepattern/modules/FLAME/ |archive-date=August 21, 2009 }}</ref> in [[GenePattern]]. T-Distributed Stochastic Neighbor Embedding (tSNE) is an algorithm designed to perform [[dimensionality reduction]], to allow visualization of complex multi-dimensional data in a two-dimensional "map".<ref>{{Cite journal| vauthors = Wattenberg M, Viégas F, Johnson I |date=Oct 13, 2016|title=How to Use t-SNE Effectively|journal=Distill|volume=1|issue=10|doi=10.23915/distill.00002 |doi-access=free}}</ref> Collaborative efforts have resulted in an open project called FlowCAP (Flow Cytometry: Critical Assessment of Population Identification Methods,<ref>{{cite web | url=http://flowcap.flowsite.org/| archive-url=https://archive.today/20120709045611/http://flowcap.flowsite.org/| url-status=usurped| archive-date=July 9, 2012|title=FlowCAP – Flow Cytometry: Critical Assessment of Population Identification Methods|access-date=2009-09-03 }}</ref>) to provide an objective way to compare and evaluate the flow cytometry data clustering methods, and also to establish guidance about appropriate use and application of these methods.