Open AccessCluster analysis of flow cytometric list mode data on a personal computer
Author(s) -
Schut Tom C. Bakker,
De Grooth Bart G.,
Greve Jan
Publication year - 1993
Publication title -
cytometry
Language(s) - English
Resource type - Journals
eISSN - 1097-0320
pISSN - 0196-4763
DOI - 10.1002/cyto.990140609
Subject(s) - computer science , pascal (unit) , cluster (spacecraft) , personal computer , k nearest neighbors algorithm , principal component analysis , cluster analysis , data mining , computer cluster , mode (computer interface) , flow (mathematics) , reliability (semiconductor) , algorithm , artificial intelligence , mathematics , computer network , power (physics) , physics , geometry , quantum mechanics , computer hardware , programming language , operating system
A cluster analysis algorithm, dedicated to analysis of flow cytometric data is described. The algorithm is written in Pascal and implemented on an MS‐DOS personal computer. It uses k‐means, initialized with a large number of seed points, followed by a modified nearest neighbor technique to reduce the large number of subclusters. Thus we combine the advantage of the k‐means (speed) with that of the nearest neighbor technique (accuracy). In order to achieve a rapid analysis, no complex data transformations such as principal components analysis were used. Results of the cluster analysis on both real and artificial flow cytometric data are presented and discussed. The results show that it is possible to get very good cluster analysis partitions, which compare favorably with manually gated analysis in both time and in reliability, using a personal computer. © 1993 Wiley‐Liss, Inc.