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Statistical power calculation

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bc2zbUVA

Contributor

Posts: 22

Joined: Thu Nov 19, 2015 4:23 pm

Post Wed Feb 08, 2017 9:59 pm

Statistical power calculation

My search in this forum turned up nothing, but I may have missed something. Is there a paper that discusses the likelihood of detecting a subpopulation and/or levels of differential expression between sample groups based on sample size and event numbers? I know it ultimately depends on the data and the algorithm, for example, Citrus recommends 8 samples per group, but if anyone has some insights on how this ability varies based on sample size and events per sample, I would appreciate it immensely.
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mleipold

Guru

Posts: 2162

Joined: Fri Nov 01, 2013 5:30 pm

Location: Stanford HIMC, CA, USA

Post Wed Feb 08, 2017 10:14 pm

Re: Statistical power calculation

Hi Brian,

Could you clarify what you mean?
1. Are you asking how many events you have to collect in order to reliably detect something?
2. Or are you asking how many samples have to be in each group in order to detect a difference?
3. Some combination of #1 and #2

If it's #1, here's a paper that might start addressing your question:
"Technical issues: flow cytometry and rare event analysis"
Hedley and Keeney
http://dx.doi.org/10.1111/ijlh.12068


For our practical sample running purposes, we have typically been told that you should have *at least* 100 events in your rarest population of interest. So, the number of acquired events therefore depends on the rarity of your rarest population of interest: if you're looking at Naive CD4+, you don't need that many events. If you're looking at Tfh T cells, or Plasmablasts, then you'll have to acquire a lot of events. And in some cases (rare antigen-specific T cells), you might *never* really be able to practically meet that benchmark.

You probably also want to look at what the range of that population is, in terms of biological variability, just to ensure that at least most of your samples will reach the cell count cutoff.


Hope this helps,
Mike
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Ofir

Master

Posts: 75

Joined: Thu Nov 07, 2013 12:46 pm

Location: US, CA

Post Thu Feb 09, 2017 12:53 am

Re: Statistical power calculation

Hi Brian,
The consideration of number of cells required for analysis and statistical significance is not unique to mass cytometry of course.
Dimensionality reduction and other complex (at least in my book) algorithms may obfuscate some of the core principals and statistical assumptions that should guide a rigorous comparison. If you wish to compare well defined or "canonical" populations, you may be best served by using conventional gating as your initial analysis step.

As Mike noted, the first thing to consider is the number of cells you expect to be able to measure in your populations of interest and the biological variability of these populations. These two quanta may be impractical to measure ahead of time, but you can use population studies to estimate your populations' frequency and generally applicable / acceptable methods to estimate biological variability.

For references on some high-level lineages see:
Intra-day and inter-day biological variations of peripheral blood lymphocytes. Here.
Reference values for peripheral blood lymphocyte phenotypes applicable to the healthy adult population in Switzerland. Here.
For consideration on applying biological variability to your measurement see:
Proposals for setting generally applicable quality goals solely based on biology. Here.

If anyone else in the community would like to suggest additional resources that would be great!
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bc2zbUVA

Contributor

Posts: 22

Joined: Thu Nov 19, 2015 4:23 pm

Post Thu Feb 09, 2017 12:58 am

Re: Statistical power calculation

Thank you both, that was exactly what I was interested in.

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