Skip to content

6 Tips For Applying The Right Statistical Test To Your Flow Cytometry Data

Written by Tim Bushnell, PhD

Flow cytometry data are numbers rich.

Data from experiments can be population measurements (percent of CD4+ cells, for example), or it can be expression level (median fluorescent expression of CD69 on activated T cells).

Many times, researchers are content to show histograms to illustrate their point after a flow experiment. This approach misses the opportunity to take that content rich data and extend the analysis into a statistical analysis.

To properly perform statistical analysis, the first step is to understand the hypothesis. The hypothesis will guide the statistical analysis, identifying the correct test to be performed. There are several things that need to be considered when beginning the statistical analysis of the data.

1.  Design your experiment properly from the start.

Statistical power answers the question of what is the probability of correctly rejecting the null hypothesis when the null hypothesis falls. There are three factors that influence the power of an experiment: the sample size, the spread of the data and the number of replicates. The power of the experiment is related to the ability of the experiment to avoid statistical errors.

2.  Know the classes of statistical errors and how to avoid them.

False positives (Type I errors) are when a true null hypothesis is incorrectly rejected. False negatives (Type II errors) are when the test fails to reject a false null hypothesis.

In fact, the power of the experiment is defined as the b which is equal to the True positive/(true positive + false negative)

3.  Use the appropriate statistical test.

The biological hypothesis and experimental design will determine what is the appropriate test for the data. The distribution of the data is also important to consider. How best to determine the correct test? This table can help you determine which test is most appropriate.

4.  Set the appropriate threshold.

The a value is the threshold that will be used to determine in the data is statistically significant or not. For historical reasons, this value is usually set at 0.05. This can be interpreted as the chance of finding significance where there is none (i.e. The chance of committing a Type I error).

5.  Avoid the more significant trap.

Once the a value is set, if the P-value is below that value, the data is statistically significant. The data is not more significant if the P-value is 0.01 and the threshold is 0.05 than if the P-value is 0.04. If there is an expectation, and a desire to decrease the Type I error, the threshold should be set to a more stringent level (0.01 or more).

6.   Avoid multiple pairwise comparisons.

In the case where the experimental design has Drug X, Drug Y and the combination of Drug X and Y, to be compared to an untreated sample, what is the best test? Pairwise comparisons should not be performed in this case for the following reason. With the a set to 0.05, there is a 5% change of committing a Type one error. With each comparison, the change of committing a Type I error increases, as showing in the chart below.

Number of pairwise comparisons Changes of a Type I error
2 10%
3 15%
4 19%
5 23%

At the end of the day, the statistical analysis of your flow cytometry data is a critical step for proving the validity of the hypothesis that was being tested. With careful and considered approach to performing the correct testing, the published data will stand up to the rigors of peer review and help lead to another discovery.

Tim Bushnell, PhD


Advanced Microscopy

Learn the best practices and advanced techniques across the diverse fields of microscopy, including instrumentation, experimental setup, image analysis, figure preparation, and more.
flow cytometry tablet eBook cover

Modern Flow Cytometry

Learn the best practices of flow cytometry experimentation, data analysis, figure preparation, antibody panel design, instrumentation and more. 

Advanced 4-10 Color Compensation

Advanced 4-10 Color Compensation, Learn strategies for designing advanced antibody compensation panels and how to use your compensation matrix to analyze your experimental data.

Top 40 Networking Scripts For PhDs

If you want to get replies from top employers and recruiters, this ebook is for you. These networking scripts will show you the exact words ...

Informational Interviews For PhDs

If you want to learn how to set up and execute informational interviews with PhDs working in industry, this ebook is for you. Here, you ...

Industry Resume Guide For PhDs

If you have a PhD and want to create the perfect industry resume to attract employers, this ebook is for you. Here, you will get ...

Top 20 Industry Jobs For PhDs

If you want to learn about the top 20 industry careers for PhDs regardless of your PhD background, this ebook is for you. Here, you ...

Salary Negotiation For PhDs

If you have a PhD and want to learn advanced salary negotiation strategies, this ebook is for you. Here, you will learn how to set ...

Top 20 Transferable Skills For PhDs

If you want to learn the top 20 transferable skills the industry employers ranked as most important for PhDs to include on their resumes and ...

Related Posts You Might Like

We Tested 5 Major Flow Cytometry SPADE Programs for Speed – Here Are The Results

Written By: Tim Bushnell, PhD As a follow-up to our post on tSNE where we compared the speed of calculation in leading software packages, let’s ...
Read More

5 FlowJo Hacks To Boost The Quality Of Your Flow Cytometry Analysis

Written By: Tim Bushnell, PhD Primary data analysis, that is the analysis at the sample or tube level, is where the populations of interest are ...
Read More

Statistical Challenges Of Rare Event Measurements In Flow Cytometry

Written by Tim Bushnell, PhD To conclude our series on rare event analysis, it is time to discuss the statistics behind rare event analysis. The ...
Read More

How to Optimize Flow Cytometry Hardware For Rare Event Analysis

Written by Tim Bushnell, PhD “Not everything that can be counted counts and not everything that counts can be counted.” — William Bruce Cameron (but ...
Read More

How To Choose The Correct Antibody For Accurate Flow Cytometry Results

Written by Tim Bushnell, PhD Next to the flow cytometer itself, the most important component of a flow cytometry experiment comes down to the antibodies. ...
Read More

How To Achieve Accurate Flow Cytometry Calcium Flux Measurements

Written by Tim Bushnell, PhD Most flow cytometry experiments work with antibodies conjugated to a fluorochrome for some variation on immunophenotyping. However, any fluorochrome that ...
Read More