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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