# Longitudinal feature-volatility: same data, different results each time
**URL:** https://forum.qiime2.org/t/longitudinal-feature-volatility-same-data-different-results-each-time/7533
**Category:** User Support
**Tags:** longitudinal
**Created:** [December 19, 2018, 4:24am UTC](https://forum.qiime2.org/t/longitudinal-feature-volatility-same-data-different-results-each-time/7533 "2018-12-19T04:24:53Z")
**Posts on this page:** 4
**Page:** 1
### Author:  [@JessJarett](https://forum.qiime2.org/u/JessJarett)
#### Post date: [December 19, 2018, 4:24am UTC](https://forum.qiime2.org/t/longitudinal-feature-volatility-same-data-different-results-each-time/7533/1 "2018-12-19T04:24:53Z")
Hi all,
I have some questions about feature-volatility and its requirements and limitations. I ended up re-running a dataset several times and noticed that I don’t get the same features in the same order, the same number of features, or the same numeric values for feature importance, every time that I run the same input data. Assuming that this is not the expected behavior of feature-volatility, I suspect this is because my dataset is too small, but I would like to know more about this issue so I can make a better design next time, and know when I should and shouldn’t use feature-volatility.
My current dataset consists of samples from 30 animals collected over 3 time points, and the animals comprise 4 treatment groups (2 groups of 8, 2 groups of 7), a total of 90 samples. Is this a totally insufficient number of samples/timepoints for feature-volatility? Is the key limitation here the total number of animals, the number of time points, or possibly the magnitude of differences in taxa that change between time points? If I have a small number of samples and timepoints, is it better to use —p-parameter-tuning or turn it off, or does it not matter much?
I’ve attached 3 examples of the same data run with the same command for reference. I’ve also tried ANCOM on each of my 3 time points (see [Best approaches/model for longitudinal differences in taxa abundance?](https://forum.qiime2.org/t/best-approaches-model-for-longitudinal-differences-in-taxa-abundance/7488)) but I don’t get any taxa that are significantly different between my treatment groups with that method.
Any advice or suggestions would be great. Thanks!
The command I used to generate these results was:
qiime longitudinal feature-volatility --i-table feature-table-genus-collapse.qza --m-metadata-file metadata\_for\_q2\_anon.txt --p-state-column Day --p-individual-id-column Animal\_ID --p-parameter-tuning --verbose --output-dir volatility-genus
[volatility\_plot1.qzv](https://cdck-file-uploads-global.s3.dualstack.us-west-2.amazonaws.com/flex002/uploads/qiime21/original/2X/5/527f8f60df8176c5e5808e1cc72cd7bb24080025.qzv) (499.6 KB)
[volatility\_plot2.qzv](https://cdck-file-uploads-global.s3.dualstack.us-west-2.amazonaws.com/flex002/uploads/qiime21/original/2X/c/cf8292b0557ee5a3e7795196c5dea39acfcf33d4.qzv) (502.5 KB)
[volatility\_plot3.qzv](https://cdck-file-uploads-global.s3.dualstack.us-west-2.amazonaws.com/flex002/uploads/qiime21/original/2X/b/ba1f05da06c1fdd262d78a1689ab00c982217924.qzv) (416.4 KB)
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### Author:  [@Nicholas\_Bokulich](https://forum.qiime2.org/u/Nicholas_Bokulich)
#### Post date: [December 19, 2018, 4:34am UTC](https://forum.qiime2.org/t/longitudinal-feature-volatility-same-data-different-results-each-time/7533/2 "2018-12-19T04:34:50Z")
> [@JessJarett](#):
>
> I ended up re-running a dataset several times and noticed that I don’t get the same features in the same order, the same number of features, or the same numeric values for feature importance, every time that I run the same input data.
This behavior is expected because there are several random processes in this action. You can use the `--p-random-state` parameter to make this consistent. The fact that you see wide variation between runs indicates that sample size is probably smaller than you need and/or the temporal signal is not very strong.
> [@JessJarett](#):
>
> If I have a small number of samples and timepoints, is it better to use —p-parameter-tuning or turn it off, or does it not matter much?
It does not make a big difference.
Check out Blautia — that's the only feature I see with a somewhat clear difference between Protein groups. Could be worth testing with linear-mixed-effects or pairwise-differences.
Some other test options: use ANCOM directly in R, which will allow you do test a multi-way model, e.g., with Protein, time, and animal as factors. q2-gneiss could also be useful.
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### Author:  [@JessJarett](https://forum.qiime2.org/u/JessJarett)
#### Post date: [December 21, 2018, 8:28pm UTC](https://forum.qiime2.org/t/longitudinal-feature-volatility-same-data-different-results-each-time/7533/3 "2018-12-21T20:28:06Z")
Thanks for your help with this! I am trying out the --p-random-state parameter along with more trees to see if I can at least get the same 10-20 taxa every time I run it, even if they're in a slightly different order, which will help me believe the results more.
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### Author:  [@system](https://forum.qiime2.org/u/system)
#### Post date: [January 22, 2019, 2:28am UTC](https://forum.qiime2.org/t/longitudinal-feature-volatility-same-data-different-results-each-time/7533/4 "2019-01-22T02:28:07Z")
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