Data visualization is important in flow cytometry data analysis and transformation of the data provides a method to visualize populations in for populations squished against the axes.
Traditionally, without transformation, after background fluorescence subtraction and the introduction of compensation error, data would be considered to have 'negative' fluorescence and would begin to 'pile-up' on the axis in the zero channel. Fluorescent baseline subtraction error during acquisition is a fundamental of flow cytometry and the basic reason why 'negative' fluorescence is observed (for more, click here). Further contributing to this phenomenon of 'negative' fluorescence is the contribution of compensation error. As the list of parameters in modern flow cytometric experiments expands, more compensation error is introduced into the final results (see this paper by Dr. Mario Roederer). Thus it has become imperative to transform the data so that the axis 'pile-up' can be observed. With the introduction of biexponential transformation minor nuances around 0 can be observed.
Traditional logarithmic scaling compresses the channels of visual space as the scale increases. This leads to 'visual misrepresentations' of dispersed populations in the lower ranges of the logarithmic scale and condensed populations in the higher ranges of the scale. However, this 'visual misrepresentation' is quickly realized when one considers that 10 units of fluorescence in the range of 1-10 is observed in the same visual space as 10,000 fluorescent units in the range of 1000-10000 when plotted on a logarithmic scale. By transforming the data, the scale is compressed in the lower range, typically from 1-10 or 1-100, leading to a more accurate visual representation of fluorescence units in the low range of the scale as compared to the higher range of the scale. Hence, using transformation provides a more precise visual tool in comparing populations with low fluorescence versus those with high fluorescence.
In the images below, compensated data is shown before and after the application of the display transformation. The settings for display transforms can be set in the Data Scaling preferences, and an overview of those preferences can be found here. Note the compression around the zero area and the representation of data lying in 'negative' space allows for more accurate representation of the event distribution. A display transformation can only be performed on compensated data.
If you would like a more detailed and in-depth look at data transformations, please refer to the papers linked below:
Interpreting flow cytometry data: a guide for
the perplexed.
Herzenberg, Tung, Moore, Parks.
A New ‘‘Logicle’’ Display Method Avoids
Deceptive Effects of Logarithmic Scaling for Low
Signals and Compensated Data.
Parks, Roederer, Moore.
