Preventing Population and Gating Bias

One of the primary tools used to generate scientific data in the field of immunology is flow cytometry. The data generated by this technique is then analyzed using a program called FlowJo, which requires user input to look at specific populations generated by the experiment.  Due to this requirement for user input, there may be some inherent bias generated while looking at specific populations through a method called “gating” which allows you to isolate a specific population and further compare that population with other experimental parameters which then require further gating, leading to the possibility of more bias.

These biases can stem from the thoughts along the lines of, “Oh, this is where I should end this gate to include [x] amount of the population,” or, “Gating around this section of this population will make my data significant enough to be published.” In addition to this gating bias, those analyzing flow my also generate a bias on what they would like to analyze. For example, the scientist may want to investigate the relationship between variable x and y, but decide not to look at x and z even though z is on their panel for the sake of gating. This form of bias may prevent the analysis of data that could be significant and play a key role in what is being investigated. For both types of biases, there are methods that are being and have been developed to prevent this from occurring.

There are types of scripts in R that can generate gating for analysis based on control samples, which would prevent gating bias (unless the controls were biased, but that’s just bad science). There are also programs like CITRUS that allow users to input their data into a system that will use an algorithm to analyze each variable to every other variable and report which variables are significant when compared to each variable. These types of methods can help prevent biased generated in the previously mentioned ways, and as scientists we should continue investigating methods that allow us to analyze our data in ways that prevent us from overlooking valuable information or skewing data in our favor.

Flow gating example for you non-flow users:

https://www.researchgate.net/profile/Barbara_Shacklett/publication/228099465/figure/fig4/AS:195882004815878@1423713317377/Flow-cytometry-gating-pathway-for-T-cell-activation-markers-Initial-gating-was-on.png

Citrus: https://support.cytobank.org/hc/en-us/articles/226940667-Overview-of-CITRUS
Flowjo: https://www.flowjo.com/