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flowClust: A Bioconductor Package for Automated Gating of Flow Cytometry Data

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flowClust: A Bioconductor Package for Automated Gating of Flow Cytometry Data

Kenneth Lo et al. BMC Bioinformatics.

Abstract

Background: As a high-throughput technology that offers rapid quantification of multidimensional characteristics for millions of cells, flow cytometry (FCM) is widely used in health research, medical diagnosis and treatment, and vaccine development. Nevertheless, there is an increasing concern about the lack of appropriate software tools to provide an automated analysis platform to parallelize the high-throughput data-generation platform. Currently, to a large extent, FCM data analysis relies on the manual selection of sequential regions in 2-D graphical projections to extract the cell populations of interest. This is a time-consuming task that ignores the high-dimensionality of FCM data.

Results: In view of the aforementioned issues, we have developed an R package called flowClust to automate FCM analysis. flowClust implements a robust model-based clustering approach based on multivariate t mixture models with the Box-Cox transformation. The package provides the functionality to identify cell populations whilst simultaneously handling the commonly encountered issues of outlier identification and data transformation. It offers various tools to summarize and visualize a wealth of features of the clustering results. In addition, to ensure its convenience of use, flowClust has been adapted for the current FCM data format, and integrated with existing Bioconductor packages dedicated to FCM analysis.

Conclusion: flowClust addresses the issue of a dearth of software that helps automate FCM analysis with a sound theoretical foundation. It tends to give reproducible results, and helps reduce the significant subjectivity and human time cost encountered in FCM analysis. The package contributes to the cytometry community by offering an efficient, automated analysis platform which facilitates the active, ongoing technological advancement.

Figures

Figure 1
Figure 1
A plot of BIC against the number of clusters for the first-stage cluster analysis. The BIC curve remains relatively flat beyond four clusters, suggesting that the model fit using four clusters is appropriate.
Figure 2
Figure 2
A scatterplot revealing the cluster assignment in the first-stage analysis. Clusters 1, 3 and 4 correspond to the lymphocyte population, while cluster 2 is referred to as the dead cell population. The black solid lines represent the 90% quantile region of the clusters which define the cluster boundaries. Points outside the boundary of the cluster to which they are assigned are called outliers and marked with "+".
Figure 3
Figure 3
A plot of BIC against the number of clusters for the second-stage cluster analysis. The BIC curve remains relatively flat beyond 11 clusters, suggesting that the model fit using 11 clusters is appropriate.
Figure 4
Figure 4
A contour plot superimposed on a scatterplot of CD8β against CD4 for the CD3+ population. The red and purple clusters at the upper right correspond to the CD3+CD4+CD8β+ cell population, indicative of the GvHD.
Figure 5
Figure 5
An image plot of CD8β against CD4 for the CD3+ population. The five clusters corresponding to the CD3+ population shown in Figure 5 can also be identified clearly on this image plot.

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