![]() Once data is compensated, transforms are linked to the compensation matrix and will only adjust data that has the same compensation matrix applied. ![]() Hence, you can change the preferences to change the transform on uncompensated data or to turn off the biexponential transform. FACSDiva, FlowJo, etc.) to scale it correctly.įor fcs3.0 data, uncompensated data will be transformed according to the Cytometers Preferences settings. The fcs file does not contain any information as to the method of transformation. If the default settings are used in FlowJo, when digital data (fcs3.0) is brought into FlowJo it will be automatically biexponentially transformed using the logicle implementation. The Herzenberg paper (review of logicle biexponential transformation) gives an excellent explanation of the transform and data display in general and we encourage anyone interested in the details of biexponential logicle transformation to read it! FlowJo and Logicle This dispersion can lead to visualization artifacts, which make it difficult to compare the populations effectively in this lower region to the populations with higher fluorescence intensity. Data at this end of the scale tends to be very disperse, due to the high resolution of these regions in the log scale. Since zero and negative values cannot be displayed on a traditional log scale, all of these events will be allocated to the first bin and compressed onto the axis.įurthermore, on a traditional log scale, the first and second decade only contain 10 divisions (or bins) for the data to fall into (bins 1 to 10) or ~90 bins (bins 11-100). Therefore, a cell with near-zero intensity might report a negative value post-processing. This is because of 1) baseline correction by the instrument and 2) photon counting error which introduces a spread of measured intensity values due to inaccuracies in the photomultiplier tube (PMT). However, the problem with a traditional log scale is evident as you approach lower intensity values and reach ‘0’.Īt the low end of the intensity scale, we can expect some events to have zero or negative fluorescence values. The effect of log transformation is perfect for large intensity values, where clustering the data into discreet populations is most effective for accurate interpretation. Its much easier to visualize the positive populations if we compress the high intensity data into a smaller region with compact populations. “Positive” populations may be 10,000 times as bright as the “negative” populations, and thousands of bins wide, so we would need immense graphs to display most flow data in linear. Why not simply view all data on a linear scale? The answer to that is the dynamic range of most flow data. One implementation of biexponential is called logicle as presented by Parks, Moore and Roederer (1). All digital (fcs3.0) data is output as linear and the options are for the software to log transform it (legacy) or biexponentially transform it (standard). ![]() The transform does not modify your actual recorded fluorescence data, only the amount of visual space that is allotted to various regions of the data. ![]() FlowJo v10 makes it easy to transform your raw data to expand or compress what is displayed on a graph. ![]()
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