Editing Flow cytometry (section)

==Data analysis==
{{Main|Flow cytometry bioinformatics}}

=== Compensation ===
{{main|Compensation (cytometry)}}
Each fluorochrome has a broad fluorescence spectrum. When more than one fluorochrome is used, an overlap between fluorochromes can occur. This situation is called spectrum overlap, and must be corrected. For example, the emission spectrum for FITC and PE is one in which the light emitted by the fluorescein overlaps the same wavelength as it passes through the filter used for PE. This spectral overlap is corrected by removing a portion of the FITC signal from the PE signals or vice versa. This process is called color compensation, which calculates a fluorochrome as a percentage to measure itself.<ref name=":0">{{cite journal | vauthors = Roederer M | title = Spectral compensation for flow cytometry: visualization artifacts, limitations, and caveats | journal = Cytometry | volume = 45 | issue = 3 | pages = 194–205 | date = November 2001 | pmid = 11746088 | doi = 10.1002/1097-0320(20011101)45:3<194::aid-cyto1163>3.0.co;2-c | doi-access = free }}</ref>

Compensation is the mathematical process by which spectral overlap of multiparameter flow cytometric data is corrected. Since fluorochromes can have wide-ranging spectrum, they can overlap, causing the undesirable result of confusion during the analysis of data. This overlap, known as spillover and quantified in the spillover coefficient, is usually caused by detectors for a certain fluorochrome measuring a significant peak in wavelength from a different fluorochrome. Linear algebra is most often used to make this correction.<ref name=":0" />

In general, when graphs of one or more parameters are displayed, it is to show that the other parameters do not contribute to the distribution shown. Especially when using the parameters which are more than double, this problem is more severe. Currently, no tools have been discovered to efficiently display multidimensional parameters. Compensation is very important to see the distinction between cells.
[[File:Picoplancton cytometrie.jpg|thumb|upright=1.25|right|Analysis of a marine sample of [[photosynthesis|photosynthetic]] [[picoplankton]] by flow cytometry showing three different populations (''[[Prochlorococcus]]'', ''[[Synechococcus]]'', and [[picoeukaryote]]s)]]{{cn|date=April 2022}}

===Gating===
[[File:Flow cytometric gating by side scatter and CD45.png|thumb|Flow cytometry gating into main categories of [[blood cell]]s by [[side scatter]] and [[PTPRC|CD45]], in a case with normal distributions.]]
The data generated by flow cytometers can be plotted in a single [[dimension]] to produce a [[histogram]], or in two-dimensional dot plots, or even in three dimensions. The regions on these plots can be sequentially separated, based on fluorescence [[intensity (physics)|intensity]], by creating a series of subset extractions, termed "gates." Specific gating [[Medical guidelines|protocols]] exist for diagnostic and clinical purposes, especially in relation to [[hematology]]. Individual single cells are often distinguished from cell doublets or higher aggregates by their "time-of-flight" (denoted also as a "pulse-width") through the narrowly focused laser beam<ref>{{cite journal | vauthors = Sharpless T, Traganos F, Darzynkiewicz Z, Melamed MR | title = Flow cytofluorimetry: discrimination between single cells and cell aggregates by direct size measurements | journal = Acta Cytologica | volume = 19 | issue = 6 | pages = 577–81 | date = 1975 | pmid = 1108568 }}</ref>

The plots are often made on logarithmic scales. Because different fluorescent dyes' emission spectra overlap,<ref name="auto">{{cite web|url=http://www.thefcn.org/flow-fluorochromes|title=Fluorochrome Table (Tools)|website=Flow Cytometry Network}}</ref><ref>{{cite web|url=http://pingu.salk.edu/flow/fluo.html|title=Table of Fluorochromes|url-status=dead|archive-url=https://web.archive.org/web/20141020225338/http://pingu.salk.edu/flow/fluo.html |archive-date=October 20, 2014}}</ref> signals at the detectors have to be compensated electronically as well as computationally. Data accumulated using the flow cytometer can be analyzed using software. Once the data is collected, there is no need to stay connected to the flow cytometer and analysis is most often performed on a separate computer.{{citation needed|date=September 2018}} This is especially necessary in core facilities where usage of these machines is in high demand.{{citation needed|date=September 2018}}

===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.

=== FMO controls ===
Fluorescence minus one (FMO) controls are important for data interpretation when building multi-color panels – in which a cell is stained with multiple fluorochromes simultaneously. FMO controls provide a measure of fluorescence spillover in a given channel and allow for compensation. To generate a FMO control, a sample is stained with all the fluorochromes except the one that is being tested – meaning if you are using 4 different fluorochromes your FMO control must contain only 3 of them (example: fluorochromes – A, B, C, D; FMOs – ABC_, AB_D, A_CD, _BCD).{{cn|date=April 2022}}