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MSstats - relaxing default parameters. #437

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MSstats - relaxing default parameters. #437

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ypriverol
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@ypriverol ypriverol commented Nov 12, 2024 •
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Description

Since the release of quantms, we have had multiple users complaining that with the current default parameters of MSstats, it always fails this step.

This is because default parameters remove poor features, like proteins with only one feature, features that are not across replicates, etc. The majority of the users and also the public data do not have too many replicates, and in some way, the data do not fill the requirements of MSstats for high-quality results. I have discussed this issue with @tonywu1999, and I think we should, by default, define more relaxed parameters, and users can then play with more stringent thresholds manually if they want to.

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@ypriverol ypriverol changed the base branch from master to dev November 12, 2024 11:00
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nf-core pipelines lint overall result: Passed ✅ ⚠️

Posted for pipeline commit 9bfbca5

+| ✅ 107 tests passed       |+
#| ❔  13 tests were ignored |#
!| ❗  11 tests had warnings |!

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Run details

  • nf-core/tools version 3.0.2
  • Run at 2024-11-12 11:29:01

@jpfeuffer
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This will heavily inflate and distort quantities for good experiments.
If people have trash data, they should actively be reminded of this by having to set these parameters accordingly

jpfeuffer
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I am against this. See my comment.

@ypriverol
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ypriverol commented Nov 12, 2024 •
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This will heavily inflate and distort quantities for good experiments. If people have trash data, they should actively be reminded of this by having to set these parameters accordingly

I agree with you 100%. However, the aim of the workflow is not to judge the data but to give some results for the data provided. We are defining default parameters in two different ways:

  • By the most common and well-known definition of it, for example, 0.01 q-value at PSM level is the most well-established metric for this.
  • By the most frequent runs we see from our users and the present in public datasets. In this case, I have seen that the majority of the data is in the public domain, and it also looks like our users do not have a lot of technical/biological replicates. I suggest that we relax the default thresholds because what is happening now is that for the majority of the datasets, I can't run this step. Then, I have to download the data to my side and play with the thresholds, etc.

This is why Im advocating to relax the thresholds here, especially because the majority of users use other packages, such as limma and others and not MSstats. I want more people to use MSstats and quantms and not find from the very beginning errors, and I need to take this step quickly because it is too stringent for the data they have.

Can you jump here @tonywu1999 and give your opinion? Do you know how many people disable removeFewMeasurements in MSstats?

@daichengxin
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Or can we catch this error and throw a warning, then try the relax parameter to run.

@jpfeuffer
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This will heavily inflate and distort quantities for good experiments. If people have trash data, they should actively be reminded of this by having to set these parameters accordingly

I agree with you 100%. However, the aim of the workflow is not to judge the data but to give some results for the data provided. We are defining default parameters in two different ways:

  • By the most common and well-known definition of it, for example, 0.01 q-value at PSM level is the most well-established metric for this.
  • By the most frequent runs we see from our users and the present in public datasets. In this case, I have seen that the majority of the data is in the public domain, and it also looks like our users do not have a lot of technical/biological replicates. I suggest that we relax the default thresholds because what is happening now is that for the majority of the datasets, I can't run this step. Then, I have to download the data to my side and play with the thresholds, etc.

This is why Im advocating to relax the thresholds here, especially because the majority of users use other packages, such as limma and others and not MSstats. I want more people to use MSstats and quantms and not find from the very beginning errors, and I need to take this step quickly because it is too stringent for the data they have.

Can you jump here @tonywu1999 and give your opinion? Do you know how many people disable removeFewMeasurements in MSstats?

My point is, that once the pipeline finishes, 90% of the users will be happy and take the data as-is. This means, that people which have good data and could heavily benefit from outlier removal, will never try this and publish bad results with our pipeline (which will shed a bad light on it).
It is as if you would set no FDR control just to please users that cannot set modifications or tolerances correctly. They will never realize that something is actually wrong.

@jpfeuffer
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Or can we catch this error and throw a warning, then try the relax parameter to run.

Better, but 90% of the people will ignore the warning.

@jpfeuffer
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The only thing I would agree with, is falling back to less stringent parameters if MSstats is forced to fail with those parameters ( i.e. if the dataset has one replicate only?)
But I don't know enough about how MSstats exactly behaves.

@jpfeuffer
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Also if the dataset has one condition only we should completely disable MSstats if we are not doing that already.

But we should not relax settings in general in my opinion.

@timosachsenberg
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I am in favor of the stricter settings as default. I think one already accumulates a lot of wrong features (e.g. through MBR) in large studies, and we should have a mechanism to flag studies were less stringent setting had to be set.

@ypriverol
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I will close this PR. I think we should document how when the parameters do not work, what could be happening.

@ypriverol ypriverol closed this Nov 14, 2024
@jpfeuffer
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I guess failing with good error messages would be sufficient.
I feel like MSstats could also improve the messages internally

@ypriverol
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I discuss it with them, but it will take some time. @tonywu1999 you can learn from this.

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@ypriverol @jpfeuffer @daichengxin @timosachsenberg