Quick Tips on Flow Cytometry Data Analysis

How flow cytometry data are displayed can have an impact on your ability to interpret them. In the following “Scientist to Scientist Educational Series” video on data display, Alan Stall, PhD, Senior Principal Scientist, BD Biosciences, explains how to select between different display and scaling options to optimize flow cytometry data analysis.
 

Data Display Options

Dr. Stall begins the video by presenting different data display options. The dot plot was the early display standard for bivariate plots. Contour plots are a more informative display option than dot plots. Advantage: They allow you to easily assess the relative size of different populations and give insight into the exact boundaries between populations, enabling more accurate gating.
 

Kinds of Contour Plots

• Standard contour plot: Easiest to interpret
• Pseudocolor: Difficult to identify small populations
• Plain lines with outliers: Can help to define small populations
  

The visualization of populations with dot plots or pseudocolor plots is dependent on the number of samples. Dot plots can make it difficult to distinguish different subpopulations. With contour plots, subpopulations are seen equally regardless of sample size.

Data Scaling

Once you've selected your data display, you need to select which numeric access you're going to use:

• Linear: Rarely used for biologic data
• Logarithmic: Most common type of scaling, allows data to be displayed in a wide dynamic range
• Disadvantage: Data values below zero can’t be displayed
• Bi-exponential: Combines the best characteristics of linear and logarithmic in the same scale

With the bi-exponential scale, higher values use a log scale. As you approach zero, they switch to a linear scale. This scale enables you to visualize whether compensation is correct and how much compensation spillover there is.

Flow cytometry data analysis with histograms

Flow cytometry data are usually displayed as one-dimensional histograms or two-dimensional bivariate plots. The latter can distinguish multiple subpopulations within the sample.
 

• Histograms are used to display single-marker expressions, as measured on the x-axis. When comparing histograms, there are multiple options for the y-axis.
• Auto: Automatically selects y-axis scale for each graph
      

Disadvantage: Can lead to misleading comparisons

• Max: Manually specifies maximum value for all graphs
• Model: Sets biggest peak of each histogram to be equal to 100%
   

Advantage: Profile of histogram is independent of the number of events analysed
 

Bivariate plots show the correlated expression of two markers. The width basis is the ability to adjust the area around and below zero via a scaling factor. Visually changing the width basis will alter the apparent spread. When comparing exponential plots, always make sure the axes are similar or equivalent.
 

Watch the video below to see how you can adjust your data display to improve flow cytometry data analysis.