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AutoGate: automating analysis of flow cytometry data

Meehan, Stephen and Walther, Guenther and Moore, Wayne and Orlova, Darya and Meehan, Connor and Parks, David and Ghosn, Eliver and Philips, Megan and Mitsunaga, Erin and Waters, Jeffrey and Kantor, Aaron and Okamura, Ross and Owumi, Solomon and Yang, Yang and Herzenberg, Leonard A. and Herzenberg, Leonore A. (2014) AutoGate: automating analysis of flow cytometry data. Immunologic Research, 58 (2-3). pp. 218-223. ISSN 0257-277X. PMCID PMC4464812. http://resolver.caltech.edu/CaltechAUTHORS:20140623-152935203

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Abstract

Nowadays, one can hardly imagine biology and medicine without flow cytometry to measure CD4 T cell counts in HIV, follow bone marrow transplant patients, characterize leukemias, etc. Similarly, without flow cytometry, there would be a bleak future for stem cell deployment, HIV drug development and full characterization of the cells and cell interactions in the immune system. But while flow instruments have improved markedly, the development of automated tools for processing and analyzing flow data has lagged sorely behind. To address this deficit, we have developed automated flow analysis software technology, provisionally named AutoComp and AutoGate. AutoComp acquires sample and reagent labels from users or flow data files, and uses this information to complete the flow data compensation task. AutoGate replaces the manual subsetting capabilities provided by current analysis packages with newly defined statistical algorithms that automatically and accurately detect, display and delineate subsets in well-labeled and well-recognized formats (histograms, contour and dot plots). Users guide analyses by successively specifying axes (flow parameters) for data subset displays and selecting statistically defined subsets to be used for the next analysis round. Ultimately, this process generates analysis “trees” that can be applied to automatically guide analyses for similar samples. The first AutoComp/AutoGate version is currently in the hands of a small group of users at Stanford, Emory and NIH. When this “early adopter” phase is complete, the authors expect to distribute the software free of charge to .edu, .org and .gov users.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://link.springer.com/article/10.1007/s12026-014-8519-yPublisherArticle
http://dx.doi.org/10.1007/s12026-014-8519-yDOIArticle
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4464812PubMed CentralArticle
http://rdcu.be/ulk2PublisherFree ReadCube access
Additional Information:© 2014 Springer Science+Business Media. Published online: 14 May 2014. Work described in this article is sponsored in part by the NIH Grant number R01AI098519.
Funders:
Funding AgencyGrant Number
NIHR01AI098519
Subject Keywords:Multiparameter flow cytometry; Automating fluorescence compensation; Automatic cell subsets identification; Guiding gating strategy
PubMed Central ID:PMC4464812
Record Number:CaltechAUTHORS:20140623-152935203
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20140623-152935203
Official Citation: AutoGate: automating analysis of flow cytometry data Stephen Meehan, Guenther Walther, Wayne Moore, Darya Orlova… Pages: 218-223
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:46452
Collection:CaltechAUTHORS
Deposited By: Ruth Sustaita
Deposited On:23 Jun 2014 22:53
Last Modified:05 Apr 2019 22:03

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