Tips for Using Population Comparison.

In FlowJo, the Population Comparison platform is used to compare single or multiple parameters from a test sample to a single control sample OR a composite of more than one control sample.  The control population is a different population (either another sample or another population within the same sample) with which the test population is compared. The test population is the one with the Comparison platform icon under it.

One control

The most frequent use case for this platform is to compare each unknown sample to a control.  The control may be an FMO control, a biological (unstimulated), or isotype control.  Standard gating techniques may not work well when samples overlap significantly or there is only a slight shift when comparing an unknown sample to a control sample.  Therefore, this platform will apply several sophisticated algorithms to the data to calculate the difference between two samples.  An overview of the algorithms used is presented here.  Using these algorithms is a more objective approach than manually gating.

Multiple Controls

This platform can also be used to compare each unknown to a composite of several controls.  When comparing multiple samples against each other, it is sometimes not possible (or meaningful) to assign a single sample as the control.  In such a case, the Population Comparison platform can concatenate all of the control samples in order to use the average of all the control samples for comparison. This process mitigates the potential artifact introduced by the selection of a sample as control that might be significantly different than the expected control sample.

Determining the samples to be concatenated is the best approach for an iterative process. One can concatenate all the control samples and compute the distances of each control sample to the average of all of them. Thus, those control samples which are outliers can be removed from the control set. When using only one control, caution is warranted since reduction of the number of samples entered as controls can lead to sampling bias.

If you copy the Comparison Population platform from one sample to another (or to a group), the same control population is used for this sample, unless the control and test populations are different populations from the same sample. In that case, the control population will change with each new sample to ensure that the test and control populations continue to be derived from the same sample (i.e., the control population is an internal control for each sample).