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Automatic clustering of flow cytometry data with density-based merging.

Walther G, Zimmerman N, Moore W, Parks D, Meehan S, Belitskaya I, Pan J, Herzenberg L - Adv Bioinformatics (2009)

Bottom Line: A current limit to the potential of this technology is the lack of automated tools for analyzing the resulting data.Our approach advances the paradigm of manually gating sequential two-dimensional projections of the data to a procedure that automatically produces gates based on statistical theory.We illustrate the methodology with a sample of mouse spleen and peritoneal cavity cells.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, Stanford University, Stanford, CA 94305, USA.

ABSTRACT
The ability of flow cytometry to allow fast single cell interrogation of a large number of cells has made this technology ubiquitous and indispensable in the clinical and laboratory setting. A current limit to the potential of this technology is the lack of automated tools for analyzing the resulting data. We describe methodology and software to automatically identify cell populations in flow cytometry data. Our approach advances the paradigm of manually gating sequential two-dimensional projections of the data to a procedure that automatically produces gates based on statistical theory. Our approach is nonparametric and can reproduce nonconvex subpopulations that are known to occur in flow cytometry samples, but which cannot be produced with current parametric model-based approaches. We illustrate the methodology with a sample of mouse spleen and peritoneal cavity cells.

No MeSH data available.


Differences in manual versus DBM gating in scatter dimensions—cells included by both gates (top), cells included in the manual gate and excluded by the DBM gate (middle), and cells included in the DBM gate and excluded by the manual gate (bottom) are displayed (column 1). Cells are live/dead gated as described in the text, and shown in the context of the next manual gating decision (column 2).
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Related In: Results  -  Collection


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fig2: Differences in manual versus DBM gating in scatter dimensions—cells included by both gates (top), cells included in the manual gate and excluded by the DBM gate (middle), and cells included in the DBM gate and excluded by the manual gate (bottom) are displayed (column 1). Cells are live/dead gated as described in the text, and shown in the context of the next manual gating decision (column 2).

Mentions: In each case, a small percentage of the events captured by one of the gating methods are excluded from the other (Figure 2). Importantly we find that the DBM gate tends to better capture the desired events then does the researcher's gate. We define desirable events as those included in the subsequent gates that the expert set. The gate set by the expert included fewer cells in the desired subset than the DBM gate, resulting in a loss of desired cells (3474 cells). The expert gate also included fewer cells outside the desired subset. However, the additional “nondesired” cells included in the DBM gate are not relevant since the expert has gated these out of the subsequent analysis. Thus, in this situation, the DBM gate is more successful than the expert gate.


Automatic clustering of flow cytometry data with density-based merging.

Walther G, Zimmerman N, Moore W, Parks D, Meehan S, Belitskaya I, Pan J, Herzenberg L - Adv Bioinformatics (2009)

Differences in manual versus DBM gating in scatter dimensions—cells included by both gates (top), cells included in the manual gate and excluded by the DBM gate (middle), and cells included in the DBM gate and excluded by the manual gate (bottom) are displayed (column 1). Cells are live/dead gated as described in the text, and shown in the context of the next manual gating decision (column 2).
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2801806&req=5

fig2: Differences in manual versus DBM gating in scatter dimensions—cells included by both gates (top), cells included in the manual gate and excluded by the DBM gate (middle), and cells included in the DBM gate and excluded by the manual gate (bottom) are displayed (column 1). Cells are live/dead gated as described in the text, and shown in the context of the next manual gating decision (column 2).
Mentions: In each case, a small percentage of the events captured by one of the gating methods are excluded from the other (Figure 2). Importantly we find that the DBM gate tends to better capture the desired events then does the researcher's gate. We define desirable events as those included in the subsequent gates that the expert set. The gate set by the expert included fewer cells in the desired subset than the DBM gate, resulting in a loss of desired cells (3474 cells). The expert gate also included fewer cells outside the desired subset. However, the additional “nondesired” cells included in the DBM gate are not relevant since the expert has gated these out of the subsequent analysis. Thus, in this situation, the DBM gate is more successful than the expert gate.

Bottom Line: A current limit to the potential of this technology is the lack of automated tools for analyzing the resulting data.Our approach advances the paradigm of manually gating sequential two-dimensional projections of the data to a procedure that automatically produces gates based on statistical theory.We illustrate the methodology with a sample of mouse spleen and peritoneal cavity cells.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, Stanford University, Stanford, CA 94305, USA.

ABSTRACT
The ability of flow cytometry to allow fast single cell interrogation of a large number of cells has made this technology ubiquitous and indispensable in the clinical and laboratory setting. A current limit to the potential of this technology is the lack of automated tools for analyzing the resulting data. We describe methodology and software to automatically identify cell populations in flow cytometry data. Our approach advances the paradigm of manually gating sequential two-dimensional projections of the data to a procedure that automatically produces gates based on statistical theory. Our approach is nonparametric and can reproduce nonconvex subpopulations that are known to occur in flow cytometry samples, but which cannot be produced with current parametric model-based approaches. We illustrate the methodology with a sample of mouse spleen and peritoneal cavity cells.

No MeSH data available.