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FIND: a new software tool and development platform for enhanced multicolor flow analysis.

Dabdoub SM, Ray WC, Justice SS - BMC Bioinformatics (2011)

Bottom Line: With this software, users can easily load single or multiple data sets, perform automated event classification, and graphically compare results within and between experiments.We also make available a simple plugin system that enables researchers to implement and share their data analysis and classification/population discovery algorithms.The FIND (Flow Investigation using N-Dimensions) platform presented here provides a powerful, user-friendly environment for analysis of Flow Cytometry data as well as providing a common platform for implementation and distribution of new automated analysis techniques to users around the world.

View Article: PubMed Central - HTML - PubMed

Affiliation: Biophysics Program, The Ohio State University, Columbus, Ohio, USA. dabdoub.2@osu.edu

ABSTRACT

Background: Flow Cytometry is a process by which cells, and other microscopic particles, can be identified, counted, and sorted mechanically through the use of hydrodynamic pressure and laser-activated fluorescence labeling. As immunostained cells pass individually through the flow chamber of the instrument, laser pulses cause fluorescence emissions that are recorded digitally for later analysis as multidimensional vectors. Current, widely adopted analysis software limits users to manual separation of events based on viewing two or three simultaneous dimensions. While this may be adequate for experiments using four or fewer colors, advances have lead to laser flow cytometers capable of recording 20 different colors simultaneously. In addition, mass-spectrometry based machines capable of recording at least 100 separate channels are being developed. Analysis of such high-dimensional data by visual exploration alone can be error-prone and susceptible to unnecessary bias. Fortunately, the field of Data Mining provides many tools for automated group classification of multi-dimensional data, and many algorithms have been adapted or created for flow cytometry. However, the majority of this research has not been made available to users through analysis software packages and, as such, are not in wide use.

Results: We have developed a new software application for analysis of multi-color flow cytometry data. The main goals of this effort were to provide a user-friendly tool for automated gating (classification) of multi-color data as well as a platform for development and dissemination of new analysis tools. With this software, users can easily load single or multiple data sets, perform automated event classification, and graphically compare results within and between experiments. We also make available a simple plugin system that enables researchers to implement and share their data analysis and classification/population discovery algorithms.

Conclusions: The FIND (Flow Investigation using N-Dimensions) platform presented here provides a powerful, user-friendly environment for analysis of Flow Cytometry data as well as providing a common platform for implementation and distribution of new automated analysis techniques to users around the world.

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Related in: MedlinePlus

Using FIND to perform automated cluster analysis on three data sets. Row 1: Scatter plots of the original data. Row 2: The Bakker Schut et al. [3] modified k-means algorithm is applied to two data sets and the resulting clusters are visualized with 2D scatterplots coded by color. The overlay features an example of an options dialog (top) for a clustering algorithm as well as the provided inline help system (bottom).
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Figure 4: Using FIND to perform automated cluster analysis on three data sets. Row 1: Scatter plots of the original data. Row 2: The Bakker Schut et al. [3] modified k-means algorithm is applied to two data sets and the resulting clusters are visualized with 2D scatterplots coded by color. The overlay features an example of an options dialog (top) for a clustering algorithm as well as the provided inline help system (bottom).

Mentions: Before performing automated analysis, the user may wish to begin the analysis with a visual data exploration. Currently FIND provides the following 2D plots: scatterplots, histograms, heatmaps, and side-by-side boxplots of all dimensions selected for analysis. With the simple configurable grid plot structure of the display pane, any or all of these plots can be displayed together for one or more of the loaded data sets (Figure 4). Additionally, each plot has its own properties dialog, allowing the user to configure aspects of the plot such as range, data transformation (linear, log, etc...), and other plot-specific options. Due to the limiting factor of screen space, FIND enables users to create groupings of plots, called Figures. Each Figure, represented in the data pane (Figure 2), stores the contents of the display pane such that clicking on a Figure Set entry switches the display to the plots and selections within that Figure. This gives users great freedom to create more plots than would ordinarily be possible, as well as, for example, to focus analysis of different populations to individual Figures (See Figure 5).


FIND: a new software tool and development platform for enhanced multicolor flow analysis.

Dabdoub SM, Ray WC, Justice SS - BMC Bioinformatics (2011)

Using FIND to perform automated cluster analysis on three data sets. Row 1: Scatter plots of the original data. Row 2: The Bakker Schut et al. [3] modified k-means algorithm is applied to two data sets and the resulting clusters are visualized with 2D scatterplots coded by color. The overlay features an example of an options dialog (top) for a clustering algorithm as well as the provided inline help system (bottom).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3119067&req=5

Figure 4: Using FIND to perform automated cluster analysis on three data sets. Row 1: Scatter plots of the original data. Row 2: The Bakker Schut et al. [3] modified k-means algorithm is applied to two data sets and the resulting clusters are visualized with 2D scatterplots coded by color. The overlay features an example of an options dialog (top) for a clustering algorithm as well as the provided inline help system (bottom).
Mentions: Before performing automated analysis, the user may wish to begin the analysis with a visual data exploration. Currently FIND provides the following 2D plots: scatterplots, histograms, heatmaps, and side-by-side boxplots of all dimensions selected for analysis. With the simple configurable grid plot structure of the display pane, any or all of these plots can be displayed together for one or more of the loaded data sets (Figure 4). Additionally, each plot has its own properties dialog, allowing the user to configure aspects of the plot such as range, data transformation (linear, log, etc...), and other plot-specific options. Due to the limiting factor of screen space, FIND enables users to create groupings of plots, called Figures. Each Figure, represented in the data pane (Figure 2), stores the contents of the display pane such that clicking on a Figure Set entry switches the display to the plots and selections within that Figure. This gives users great freedom to create more plots than would ordinarily be possible, as well as, for example, to focus analysis of different populations to individual Figures (See Figure 5).

Bottom Line: With this software, users can easily load single or multiple data sets, perform automated event classification, and graphically compare results within and between experiments.We also make available a simple plugin system that enables researchers to implement and share their data analysis and classification/population discovery algorithms.The FIND (Flow Investigation using N-Dimensions) platform presented here provides a powerful, user-friendly environment for analysis of Flow Cytometry data as well as providing a common platform for implementation and distribution of new automated analysis techniques to users around the world.

View Article: PubMed Central - HTML - PubMed

Affiliation: Biophysics Program, The Ohio State University, Columbus, Ohio, USA. dabdoub.2@osu.edu

ABSTRACT

Background: Flow Cytometry is a process by which cells, and other microscopic particles, can be identified, counted, and sorted mechanically through the use of hydrodynamic pressure and laser-activated fluorescence labeling. As immunostained cells pass individually through the flow chamber of the instrument, laser pulses cause fluorescence emissions that are recorded digitally for later analysis as multidimensional vectors. Current, widely adopted analysis software limits users to manual separation of events based on viewing two or three simultaneous dimensions. While this may be adequate for experiments using four or fewer colors, advances have lead to laser flow cytometers capable of recording 20 different colors simultaneously. In addition, mass-spectrometry based machines capable of recording at least 100 separate channels are being developed. Analysis of such high-dimensional data by visual exploration alone can be error-prone and susceptible to unnecessary bias. Fortunately, the field of Data Mining provides many tools for automated group classification of multi-dimensional data, and many algorithms have been adapted or created for flow cytometry. However, the majority of this research has not been made available to users through analysis software packages and, as such, are not in wide use.

Results: We have developed a new software application for analysis of multi-color flow cytometry data. The main goals of this effort were to provide a user-friendly tool for automated gating (classification) of multi-color data as well as a platform for development and dissemination of new analysis tools. With this software, users can easily load single or multiple data sets, perform automated event classification, and graphically compare results within and between experiments. We also make available a simple plugin system that enables researchers to implement and share their data analysis and classification/population discovery algorithms.

Conclusions: The FIND (Flow Investigation using N-Dimensions) platform presented here provides a powerful, user-friendly environment for analysis of Flow Cytometry data as well as providing a common platform for implementation and distribution of new automated analysis techniques to users around the world.

Show MeSH
Related in: MedlinePlus