Comparison of five clustering algorithms to classify phytoplankton from flow cytometry data

Wilkins, M. F.; Hardy, Sam A.; Boddy, Lynne; Morris, Colin W.

doi:10.1002/1097-0320(20010701)44:3<210::aid-cyto1113>3.0.co;2-y

Cited by 31 publications

(27 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A number of methods have been suggested for the use in flow cytometry (10)(11)(12)(13)(14)(15)(16). Most methods rely on estimate of number of populations leaving behind hierarchical nature of complex biological sample or use the hierarchy of clusters only as a proxy for building models of estimated number of components/clusters (28).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Detection and monitoring of normal and leukemic cell populations with hierarchical clustering of flow cytometry data

et al. 2011

View full text Add to dashboard Cite

Flow cytometry is a valuable tool in research and diagnostics including minimal residual disease (MRD) monitoring of hematologic malignancies. However, its gradual advancement toward increasing numbers of fluorescent parameters leads to information rich datasets, which are challenging to analyze by standard gating and do not reflect the multidimensionality of the data. We have developed a novel method to analyze complex flow cytometry data, based on hierarchical clustering analysis (HCA) but with a new underlying algorithm, using Mahalanobis distance measure. HCA is scalable to analyze complex multiparameter datasets (here demonstrated on up to 12 color flow cytometry and on a 20-parameter synthetic dataset). We have validated this method by comparison with standard gating approaches when performed independently by expert cytometrists. Acute lymphoblastic leukemia blast populations were analyzed in diagnostic and follow-up datasets (n 5 123) from three centers. HCA results correlated very well (Passing-Bablok correlation coefficient 5 0.992, slope 5 1, intercept 5 20.01) with standard gating data obtained by the I-BFM FLOW-MRD study group. To further improve the performance in follow-up samples with low MRD levels and to automate MRD detection, we combined HCA with support vector machine (SVM) learning. HCA in combination with SVM provides a novel diagnostic tool that not only allows analysis of increasingly complex flow cytometry data but also is less observer-dependent compared with classical gating and has potential for automation. ' 2011 International Society for Advancement of Cytometry Key terms hierarchical clustering; minimal residual disease; acute lymphoblastic leukemia; support vector machines; multiparameter flow cytometry IN childhood acute lymphoblastic leukemia (ALL), response to therapy as measured by minimal residual disease (MRD) monitoring is an important biomarker for predicting relapse and stratifying treatment (1-5). MRD can be assessed by molecular analysis of B-and T-cell receptor gene rearrangements or by flow cytometric analysis of aberrant immunophenotypes. Flow cytometric MRD monitoring is a fast and sensitive method and has been incorporated in several large childhood ALL clinical trials (1,6,7). However, flow cytometry generates increasingly large and information-rich datasets, which provide new challenges for analysis. Modern multilaser flow cytometers are able to simultaneously measure up to 12 or more parameters and acquire such information from millions of single cells (8,9). Traditional gating of populations on two-parameter plots is tedious (e.g., 28 plots in six-color flow cytometry, 66 plots for 10-color analysis, 91 plots for 12-color analysis, etc.) and does not reflect the multidimensionality of the data. Moreover, both the setting of the gates and interpreta-

show abstract

Section: Discussionmentioning

confidence: 99%

“…Alternative analytical methods have been tested for flow cytometry data (10)(11)(12)(13)(14)(15)(16), but most of them rely on prior knowledge of the number of clusters (cell populations) expected in the sample. These methods produce limited number of clusters without information on their inner hierarchy (subpopulations).…”

mentioning

confidence: 99%

Detection and monitoring of normal and leukemic cell populations with hierarchical clustering of flow cytometry data

et al. 2011

View full text Add to dashboard Cite

show abstract

“…By allowing data points to be associated to some degree with all clusters, not just the closest, the algorithm can be made more robust to the problem of local minima. Several extensions of this "fuzzy" version of the k-means algorithm, incorporating cluster shape information via scatter matrices (77), have recently been tried on a flow cytometric data set with some success (78). However, the value of k, the number of clusters, must be specified beforehand, and it would be preferable if the "natural" number of clusters could be determined automatically from the data.…”

Section: Non-neural Methodsmentioning

confidence: 99%

Pattern recognition in flow cytometry

Boddy¹,

Wilkins²,

Morris³

2001

Cytometry

Self Cite

View full text Add to dashboard Cite

“…Demers et al 12 have proposed an extension of K-means to allow for nonspherical clusters, but this algorithm has been shown to have poorer performance than fuzzy K-means clustering. 13 Although the fuzzy K-means 14 takes into consideration some form of classification uncertainty, it is a heuristic-based algorithm that lacks a formal statistical foundation. Nima et al 15 have proposed a method of rapid cell population identification based on K-means that uses a change point detection algorithm to determine the number of sub-populations, which thus enables the method to be effective with nonspherical and concave distribution cell populations.…”

Section: Introductionmentioning

confidence: 99%

AUTOMATED GATING OF PORTABLE CYTOMETER DATA BASED ON SKEW t MIXTURE MODELS

Wang

Chen

Zhi

et al. 2015

J. Mech. Med. Biol.

View full text Add to dashboard Cite

A major component of flow cytometry (FCM) data analysis involves gating, which is the process of identifying homogeneous groups of cells. With the rapid development of the portable flow cytometer, manual gating techniques have been unable to meet the demand for accurate and rapid analysis of samples. To provide a practical application for portable devices, we propose a flexible, statistical model-based clustering approach for identifying cell populations in FCM data. This approach, which mimics the manual gating process, employs a finite mixture model with a density function of skew t distribution and estimates parameters via an expectation maximization algorithm. Data analysis from an experiment on a patient's peripheral blood samples have proven that the proposed methodology yields better results in terms of robustness against outliers than current state-of-the-art automated gating methods, has more flexibility in clustering symmetric data and leads to lower misclassification rates (misclassification rates of skew t method is 0.06442) when handling highly asymmetric data. The method we proposed will improve data analysis of portable flow cytometers, especially when the users have no professional training.

show abstract

Comparison of five clustering algorithms to classify phytoplankton from flow cytometry data

Cited by 31 publications

References 23 publications

Detection and monitoring of normal and leukemic cell populations with hierarchical clustering of flow cytometry data

Detection and monitoring of normal and leukemic cell populations with hierarchical clustering of flow cytometry data

Pattern recognition in flow cytometry

AUTOMATED GATING OF PORTABLE CYTOMETER DATA BASED ON SKEW t MIXTURE MODELS

Contact Info

Product

Resources

About