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SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 2: biological evaluation.

Mosmann TR, Naim I, Rebhahn J, Datta S, Cavenaugh JS, Weaver JM, Sharma G - Cytometry A (2014)

Bottom Line: Comparison of antigen-stimulated and control human peripheral blood cell samples demonstrated that SWIFT could identify biologically significant subpopulations, such as rare cytokine-producing influenza-specific T cells.A sensitivity of better than one part per million was attained in very large samples.A companion manuscript (Part 1) details the algorithmic development of SWIFT.

View Article: PubMed Central - PubMed

Affiliation: David H. Smith Center for Vaccine Biology and Immunology, University of Rochester Medical Center, University of Rochester, Rochester, New York.

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Performance characteristics of SWIFT including robust convergence on a final cluster number. A single sample of influenza-stimulated human PBMC was analyzed by ICS and flow cytometry, and then a random subset of 100,000 cells was clustered in SWIFT using seven fluorescence and two scatter dimensions. (A) Different values of the input cluster number were used in the first step (six replicates per point). The numbers of clusters found after the first (Initial, green), second (Split, orange), and third (Merged, blue) steps are shown. (B) Run times for the analyses in A are shown. Analysis was performed on a 2.4 GHz Mac Pro with 8 cores. (C) Samples containing different cell numbers, randomly sampled from a concatenate of influenza-stimulated human PBMC samples, were analyzed in triplicate in SWIFT using 100 input clusters. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
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fig03: Performance characteristics of SWIFT including robust convergence on a final cluster number. A single sample of influenza-stimulated human PBMC was analyzed by ICS and flow cytometry, and then a random subset of 100,000 cells was clustered in SWIFT using seven fluorescence and two scatter dimensions. (A) Different values of the input cluster number were used in the first step (six replicates per point). The numbers of clusters found after the first (Initial, green), second (Split, orange), and third (Merged, blue) steps are shown. (B) Run times for the analyses in A are shown. Analysis was performed on a 2.4 GHz Mac Pro with 8 cores. (C) Samples containing different cell numbers, randomly sampled from a concatenate of influenza-stimulated human PBMC samples, were analyzed in triplicate in SWIFT using 100 input clusters. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

Mentions: In addition to the identification of rare subpopulations and asymmetric subpopulations, the splitting and merging steps in SWIFT have the further benefit of converging on a stable cluster number. The EM algorithm requires an initial estimate of K, the cluster number, which is used for the first clustering step. However, if this number is too low, the splitting stage will generate a large increase in cluster number in the second step, and if the initial estimate of K is too high, the final merging step will perform a large number of merging operations. The effect of these two steps is to converge on a relatively stable final number of clusters, varying by only 1.3-fold even when the input initial estimate of K was varied over a 100-fold range (Fig. 3A). The Bayesian information criterion (BIC) was also tested in SWIFT as a criterion for determining K, but was slower and less robust than the modality-based splitting and merging steps in SWIFT (data not shown). SWIFT exhibits strong self-normalizing behavior to converge on a stable number of clusters. Although the final number of clusters is higher than determined by other flow cytometry analysis programs, the level of resolution described by SWIFT is consistent with the known biological diversity of the cell populations (see below).


SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 2: biological evaluation.

Mosmann TR, Naim I, Rebhahn J, Datta S, Cavenaugh JS, Weaver JM, Sharma G - Cytometry A (2014)

Performance characteristics of SWIFT including robust convergence on a final cluster number. A single sample of influenza-stimulated human PBMC was analyzed by ICS and flow cytometry, and then a random subset of 100,000 cells was clustered in SWIFT using seven fluorescence and two scatter dimensions. (A) Different values of the input cluster number were used in the first step (six replicates per point). The numbers of clusters found after the first (Initial, green), second (Split, orange), and third (Merged, blue) steps are shown. (B) Run times for the analyses in A are shown. Analysis was performed on a 2.4 GHz Mac Pro with 8 cores. (C) Samples containing different cell numbers, randomly sampled from a concatenate of influenza-stimulated human PBMC samples, were analyzed in triplicate in SWIFT using 100 input clusters. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4238823&req=5

fig03: Performance characteristics of SWIFT including robust convergence on a final cluster number. A single sample of influenza-stimulated human PBMC was analyzed by ICS and flow cytometry, and then a random subset of 100,000 cells was clustered in SWIFT using seven fluorescence and two scatter dimensions. (A) Different values of the input cluster number were used in the first step (six replicates per point). The numbers of clusters found after the first (Initial, green), second (Split, orange), and third (Merged, blue) steps are shown. (B) Run times for the analyses in A are shown. Analysis was performed on a 2.4 GHz Mac Pro with 8 cores. (C) Samples containing different cell numbers, randomly sampled from a concatenate of influenza-stimulated human PBMC samples, were analyzed in triplicate in SWIFT using 100 input clusters. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
Mentions: In addition to the identification of rare subpopulations and asymmetric subpopulations, the splitting and merging steps in SWIFT have the further benefit of converging on a stable cluster number. The EM algorithm requires an initial estimate of K, the cluster number, which is used for the first clustering step. However, if this number is too low, the splitting stage will generate a large increase in cluster number in the second step, and if the initial estimate of K is too high, the final merging step will perform a large number of merging operations. The effect of these two steps is to converge on a relatively stable final number of clusters, varying by only 1.3-fold even when the input initial estimate of K was varied over a 100-fold range (Fig. 3A). The Bayesian information criterion (BIC) was also tested in SWIFT as a criterion for determining K, but was slower and less robust than the modality-based splitting and merging steps in SWIFT (data not shown). SWIFT exhibits strong self-normalizing behavior to converge on a stable number of clusters. Although the final number of clusters is higher than determined by other flow cytometry analysis programs, the level of resolution described by SWIFT is consistent with the known biological diversity of the cell populations (see below).

Bottom Line: Comparison of antigen-stimulated and control human peripheral blood cell samples demonstrated that SWIFT could identify biologically significant subpopulations, such as rare cytokine-producing influenza-specific T cells.A sensitivity of better than one part per million was attained in very large samples.A companion manuscript (Part 1) details the algorithmic development of SWIFT.

View Article: PubMed Central - PubMed

Affiliation: David H. Smith Center for Vaccine Biology and Immunology, University of Rochester Medical Center, University of Rochester, Rochester, New York.

Show MeSH