AutoGate: automating analysis of flow cytometry data.
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.
Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA.
Stephen
S
Meehan
Guenther
G
Walther
Wayne
W
Moore
Darya
D
Orlova
Connor
C
Meehan
David
D
Parks
Eliver
E
Ghosn
Megan
M
Philips
Erin
E
Mitsunaga
Jeffrey
J
Waters
Aaron
A
Kantor
Ross
R
Okamura
Solomon
S
Owumi
Yang
Y
Yang
Leonard A
LA
Herzenberg
Leonore A
LA
Herzenberg
D000465:Algorithms; D057225:Data Mining / Q000379:methods; D005434:Flow Cytometry / Q000379:methods; D006801:Humans; D012984:Software