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1.
Figure 4

Figure 4. From: Optimizing transformations for automated, high throughput analysis of flow cytometry data.

Variation in cell subpopulation locations. Variation in population location measured as the sum of squared distances between population centers for a) lymphocyte populations in the FSC vs SSC channels. b) metaclusters of cell populations in the fluorescence channels.

Greg Finak, et al. BMC Bioinformatics. 2010;11:546-546.
2.
Figure 5

Figure 5. From: Optimizing transformations for automated, high throughput analysis of flow cytometry data.

Metaclusters in the scatter channels. Metaclusters of lymphocyte populations in the FSC and SSC dimensions for four optimized and three default transformations. We observe little difference between the transformations in the forward and side scatter dimensions for gating lymphocyte populations, suggesting that the primary benefit in the FSC vs SSC dimensions is for data visualization. Contours represent the 90th quantiles of the cell subpopulation distributions.

Greg Finak, et al. BMC Bioinformatics. 2010;11:546-546.
3.
Figure 6

Figure 6. From: Optimizing transformations for automated, high throughput analysis of flow cytometry data.

Metaclusters in the fluorescence channels. Metaclusters of cell populations defined in the fluorescence channels of the lymphoma data under different transformations. Only the primary cell populations are shown for comparison (CD19+/CD3-/CD5+ and CD19-/CD3+/CD5-). The number of metaclusters for the transformed data is shown in brackets. The number of cell populations in a metacluster is shown above the plot. Contours represent the 90th percentiles of the cell subpopulation distributions.

Greg Finak, et al. BMC Bioinformatics. 2010;11:546-546.
4.
Figure 1

Figure 1. From: Optimizing transformations for automated, high throughput analysis of flow cytometry data.

Flowchart of the analysis pipeline. Flowchart describing our analysis pipeline. a) Procedure for analyzing FSC vs SSC channels. Standard data analysis procedures are depicted by path 1, whereas procedures applying parameter-optimized transformations are depicted by path 2. b) Procedure for analyzing fluorescence channels. Standard procedures are depicted by path 1). Procedures utilizing optimized transformations are depicted by path 2. The default transformation depicted in 1) is the generalized arcsinh with default parameters (a = 1, b = 1, c = 0), as defined in the flowCore package. Normalization follows transformation in b) to ensure that the transformed data are on a common scale.

Greg Finak, et al. BMC Bioinformatics. 2010;11:546-546.
5.
Figure 3

Figure 3. From: Optimizing transformations for automated, high throughput analysis of flow cytometry data.

Visualization of FCM data under different transformations. Visualization of flow cytometry data under different transformations. a) The FSC and SSC dimensions of a representative sample transformed using the biexponential, generalized arcsinh, generalized Box-Cox, and linlog transformations with optimized parameters. Untransformed and default-parameter generalized arcsinh and biexponential transformed data are shown for comparison. Some parameter-optimized trans-formations (biexponential and generalized Box-Cox in this example) improve visualization and resolution of the lymphocyte cell population when compared to default or untransformed data. b) Comparison of a fluorescence channel data under different parameter optimized and default transformations. In this example, the optimized biexponential and optimized linlog improve the resolution of the two populations in the CD19 vs CD5 channels, compared to the default generalized arcsinh or default biexponential. Points represent individual events, contours represent the two-dimensional kernel density estimate of the data.

Greg Finak, et al. BMC Bioinformatics. 2010;11:546-546.
6.
Figure 2

Figure 2. From: Optimizing transformations for automated, high throughput analysis of flow cytometry data.

Simulation study results. Results of transformations on simulated data. a) A single simulated sample is shown as a series of bivariate dot plot projections. Data are presented on the original scale (top row), and on the scale of the inverse biexponential transform (bottom row). Points represent individual events. b) Boxplots representing the distribution of misclassification rates of flowMerge models with K = 9 components fitted to the simulated data set under different transformations. c) Intra-cluster variability measured as the total sum of squared deviations for metaclustered populations identified by flowMerge under different transformations. d) Example bivariate projections of metaclusters for untransformed data (top row), default biexponential (second row), optimized biexponential (third row), optimized generalized arcsinh (fourth row), and default generalized arcsinh (fifth row). Corresponding metaclusters were selected where possible. Metaclusters are labeled as +/+, -/-, -/+ for artificial markers A and B. Ellipses represent 90th quantile contours of subpopulations.

Greg Finak, et al. BMC Bioinformatics. 2010;11:546-546.

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