Display

Overview of Data Display

• Definition: Data display refers to the various methods used to visualize flow cytometry data, allowing for the identification of cell populations, the assessment of marker expression, and the communication of results
• Importance:
  • Data Exploration: To explore the data and identify patterns or trends
  • Data Analysis: To analyze the data and quantify the expression of cell markers
  • Data Communication: To communicate the results to others
• Common Types of Data Displays:
  • Dot Plots
  • Density Plots
  • Contour Plots
  • Histograms
  • Heat Maps
  • Spectra Plots
• Data Transformations:
  • Linear
  • Logarithmic
  • Biexponential (e.g., Logicle, Hyperlog)

Types of Data Displays

• Dot Plots:
  • Description: Each event (cell) is represented as a single dot on a two-dimensional plot, with the x and y axes representing two different parameters
  • Advantages: Can visualize all of the events in the data set
  • Disadvantages: Can be difficult to visualize dense populations of cells
  • Use Cases: To visualize cell populations and identify gating boundaries
• Density Plots:
  • Description: Similar to dot plots, but the density of the dots is used to represent the number of events in a given area
  • Advantages: Can visualize dense populations of cells more easily than dot plots
  • Disadvantages: May obscure rare events
  • Use Cases: To visualize cell populations and identify gating boundaries
• Contour Plots:
  • Description: Similar to density plots, but the data is represented as contour lines that connect points of equal density
  • Advantages: Can visualize dense populations of cells and identify subpopulations
  • Disadvantages: May obscure rare events
  • Use Cases: To visualize cell populations, identify gating boundaries, and identify subpopulations
• Histograms:
  • Description: A one-dimensional plot that shows the distribution of events for a single parameter
  • Advantages: Can easily visualize the distribution of data for a single parameter
  • Disadvantages: Cannot visualize the relationship between two parameters
  • Use Cases: To assess the expression of a single cell marker and identify positive and negative populations
• Heat Maps:
  • Description: A two-dimensional plot that uses color to represent the value of a parameter
  • Advantages: Can visualize the expression of multiple markers across multiple samples
  • Disadvantages: Can be difficult to interpret for large data sets
  • Use Cases: To compare the expression of markers across different samples or cell populations
• Spectra Plots:
  • Description: A plot that shows the emission spectrum of a fluorochrome
  • Advantages: Can visualize the spectral overlap between different fluorochromes
  • Disadvantages: Cannot visualize the expression of cell markers
  • Use Cases: To design flow cytometry panels and optimize compensation settings

Data Transformations

• Linear Scale:
  • Description: The data is displayed on a linear scale, with equal intervals representing equal changes in value
  • Advantages: Easy to interpret
  • Disadvantages: Can be difficult to visualize low-intensity signals
  • Use Cases: To visualize data with a narrow range of values and to compare the absolute expression levels of cell markers
• Logarithmic Scale:
  • Description: The data is displayed on a logarithmic scale, with equal intervals representing equal fold-changes in value
  • Advantages: Can visualize a wide range of values and compress the data. Useful in highlighting multiple populations
  • Disadvantages: Can distort the appearance of the data and make it difficult to compare absolute expression levels
  • Use Cases: To visualize data with a wide range of values and to identify populations with low expression levels
• Biexponential Scale:
  • Description: A hybrid scale that combines linear and logarithmic scales
  • Advantages: Can visualize both low-intensity and high-intensity signals
  • Disadvantages: Can be more difficult to interpret than linear or logarithmic scales
  • Use Cases: To visualize data with a wide range of values and to accurately represent both positive and negative populations
• Common Biexponential Transformations:
  • Logicle Transformation: A biexponential transformation that is commonly used in flow cytometry
  • Hyperlog Transformation: Another biexponential transformation that is similar to Logicle

Choosing the Right Display and Transformation

• Experimental Goals:
  • What are you trying to communicate with the data?
  • Are you trying to identify rare populations, compare expression levels, or visualize cellular relationships?
• Data Distribution:
  • Is the data normally distributed or skewed?
  • Does the data have a wide range of values or a narrow range of values?
• Audience:
  • Who are you presenting the data to?
  • Are they familiar with flow cytometry data displays and transformations?

Practical Guidelines

• Use Dot Plots or Density Plots to Visualize Cell Populations:
  • These displays are best for visualizing the overall distribution of cells and identifying gating boundaries
• Use Histograms to Assess the Expression of Single Cell Markers:
  • Histograms are best for visualizing the distribution of data for a single parameter and identifying positive and negative populations
• Use Logarithmic Scales to Visualize a Wide Range of Values:
  • Logarithmic scales are best for visualizing data with a wide range of values and for identifying populations with low expression levels
• Use Biexponential Scales to Accurately Represent Both Positive and Negative Populations:
  • Biexponential scales are best for visualizing data with a wide range of values and for accurately representing both positive and negative populations
• Avoid Overplotting:
  • If the data set is too large to visualize effectively using dot plots or density plots, consider using contour plots or heat maps

Troubleshooting Data Display Issues

• Overlapping Populations:
  • Possible Causes:
    • Poor resolution
    • Incorrect gating
    • Suboptimal transformation
  • Troubleshooting Steps:
    • Optimize staining protocol
    • Adjust instrument settings
    • Review gating strategy
    • Experiment with different transformation scales
• Loss of Detail:
  • Possible Causes:
    • Inappropriate binning
    • Incorrect transformation
  • Troubleshooting Steps:
    • Adjust binning of data
    • Experiment with different transformation scales
• Misleading Visuals:
  • Possible Causes:
    • Incorrectly set scales
    • Improper controls
  • Troubleshooting Steps:
    • Adjust scale and re-evaluate data
    • Inspect the controls for any issues