requesting a new QC feature

[fazeliniah]

Dear Sebastian and amica team,
Thanks again for all your work to make this useful tool publicly available.
I understand that the imputation is necessary for some of the features in amica, however this may affect the analysis for some of the proteomics data. This includes the analysis of the IP samples, where the bait protein and some potential binders are only present in one group. Or the proteomic analysis of the samples with the knockout genes. Is there any options in amica to count for these cases?
Can we have a new feature to generate new plots (maybe Venn?) for the samples before imputation?
Thanks

[tbaccata]

Hello! Thank you again!

Barplots for the number of id. proteins per sample, and the percentage of missing values per samples are available in the QC tab, both are produced with the uploaded intensities before imputation:

In the Analysis parameters you can specify to only include proteins that have been identified in at least n replicates of the same group (e.g 2 out of 3 replicates), and you can also apply this filtering on valid values filter on the bait samples only:

That being said, there are use cases were it would be beneficial to not perform the imputation at all.
This feature would interfere with the current functionality though, heatmaps (and to some degree profile plots) can not deal with missing values. I'll need to think about it, how to include this feature without breaking anything.

Best,
Sebastian

[fazeliniah]

Thanks for your quick response. I think it makes sense to keep the imputation. The bar plots for the protein groups is a wonderful way to show the summary. But maybe having additional tab that shows presence/absence for detected proteins across groups can add more useful information. something like this for proteins before imputation:

[tbaccata]

I agree, this would be a good QC feature.
I will implement this.

Best,
Sebastian

[fazeliniah]

Thanks! Look forward to the new feature.

[tbaccata]

Hello,

To keep this feature general for many samples, and because set comparisons become very difficult to visualize for many sets, I decided to opt for a heatmap.

You have 3 options to compare the overlap between samples in this heatmap (if you click on the wrench icons):

• Jaccard index (https://en.wikipedia.org/wiki/Jaccard_index)
• Overlap Coefficient (https://en.wikipedia.org/wiki/Overlap_coefficient)
• The number of shared proteins

In addition, you can inspect the values of these metrics in a data table.
These features are available in the "Protein Overlap" tab next to the Protein groups barplots.

Best,
Sebastian