Fluorescence parameters displayed with Biexponential scaling in FlowJo ™.Biexponential Transformation Instructionsīiexponential scaling helps visualize data that is compressed against the low x- and y- axes. However FlowJoⓇ does provide transforms which accommodate the best of both worlds - such as “Biexponential”.įigure 5. If the dataset being displayed includes negative values, then a linear display may be more appropriate. Log scales do not include values of zero or lower, and may not appropriately illustrate low dynamic ranging details of a dataset. Fluorescence parameters displayed in log (left) and linear transformed space, within FlowJoⓇ. Log scales are suitable for this type of parameter.įigure 4. those that contain a large number of the target structure) emit light, sometimes at an intensity thousands of times higher than negative cells (those with few or no targets). In order to delineate a heterogeneous population of cells, a cell structure of interest is stained with a fluorescent dye. Log scales are used when plotting fluorescent flow cytometry data. The duality of the log scale enables the identification of meaningful shifts around low values as well as around bigger input. A shift of five units between the sixth and seventh intervals would be unnoticeable. Consider the scale along the x-axis in Figure 2: a shift of five units between the first and second interval would be significant. This allows for observation of percentage changes rather than absolute changes. The intervals increase successively by a factor of ten. Data with a logarithmic relationship log-10 specifically. Figure 2 is a plot of arbitrary values with a logarithmic relationship.įigure 3. Consecutive graduations along an axis represent equal changes in ratio. multiplicative) differences between values. A log scale is based on exponential (i.e. Logarithmic scales are very powerful when graphing parameters with a wide dynamic range. Scatter parameters displayed in linear (left) and log transformed space, within FlowJo ™. Linear plots are less practical when data points consume a larger dynamic range.įigure 2. These measurements are not concentrated in any particular region of the parameters’ scales, so both the parameter’s features are displayed well on a linear plot. FSC and SSC are relative to cell size and cell granularity, respectively. When working with flow cytometry data, linear scaling is commonly used when plotting forward scatter (FSC) and side scatter (SSC). Linear scales are most effective for displaying datasets with values spread evenly across a given range. This scale is constant for the entire span of the graph. Data with a linear relationshipĮach graduation along both axes represents a value change of one. Figure 1 is a plot of arbitrary values with a linear relationship.įigure 1. Rulers and measuring tapes are other examples of linear scales. Accordingly, the visual distance between data points is proportional to the numerical distance between the values. Linear scaling is achieved by plotting events within evenly distributed, equally sized bins. Linear and logarithmic scaling are two common methods of representing data. The optimal scaling depends on the nature of the data. channel) can be adjusted in order to appropriately interpret a given dataset. Graphical scaling and transforms of any parameter (i.e. An effective graph will clearly display the relationship between experiment parameters. Graphical representations of data are crucial when performing most analyses.
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