Rapid automated analysis method of flow cytometry data / 军事医学
Military Medical Sciences
;
(12): 736-741, 2015.
Article
in Chinese
| WPRIM
| ID: wpr-481082
ABSTRACT
Objective A major component of flow cytometry data analysis involves gating , which is the process of identifying homogeneous groups of cells .As manual gating is error-prone, non-reproducible, nonstandardized, and time-consuming , we propose a time-efficient and accurate approach to automated analysis of flow cytometry data .Methods Unlike manual analysis that successively gates the data projected onto a two-dimensional filed, this approach, using the K-means clustering results , directly analyzed multidimensional flow cytometry data via a similar subpopulations-merged algorithm.In order to apply the K-means to analysis of flow cytometric data , kernel density estimation for selecting the initial number of clustering and k-d tree for optimizing efficiency were proposed .After K-means clustering , results closest to the true populations could be achieved via a two-segment line regression algorithm .Results The misclassification rate (MR) was 0.0736 and time was 2 s in Experiment One, but was 0.0805 and 1 s respectively in Experiment Two. Conclusion The approach we proposed is capable of a rapid and direct analysis of the multidimensional flow cytometry data with a lower misclassification rate compared to both nonprobabilistic and probabilistic clustering methods .
Full text:
Available
Index:
WPRIM (Western Pacific)
Language:
Chinese
Journal:
Military Medical Sciences
Year:
2015
Type:
Article
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