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Update manta FILTER=='PASS' Part3 : oncoprint rerun #1116

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merged 9 commits into from
Jul 22, 2021

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kgaonkar6
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@kgaonkar6 kgaonkar6 commented Jul 21, 2021

Purpose/implementation Section

What scientific question is your analysis addressing?

Rerun with FILTER==PASS for manta calls used in copy_number_consensus_call

What was your approach?

Rerun

What GitHub issue does your pull request address?

Add deep deletion status in #1099
Update with manta FILTER==PASS #1113

Directions for reviewers. Tell potential reviewers what kind of feedback you are soliciting.

Which areas should receive a particularly close look?

I havedeep deletion and amplification in the oncoprint.
There seems to have been an issue with Varian_Classification update not going into master from #1088 so I've added back in this PR.

Is there anything that you want to discuss further?

Is the analysis in a mature enough form that the resulting figure(s) and/or table(s) are ready for review?

Yes

Results

What types of results are included (e.g., table, figure)?

figures

What is your summary of the results?

Previous primary_only_hgat_goi_oncoprint.png with status=="loss" as Del and "amplification" as Amp
image

Current file primary_only_hgat_goi_oncoprint.png with status=="loss" as Del and "amplification" as Amp along with manta FILTER==PASS
image

Reproducibility Checklist

  • The dependencies required to run the code in this pull request have been added to the project Dockerfile.
  • This analysis has been added to continuous integration.

Documentation Checklist

  • This analysis module has a README and it is up to date.
  • This analysis is recorded in the table in analyses/README.md and the entry is up to date.
  • The analytical code is documented and contains comments.

@jharenza
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Thanks @kgaonkar6 - can you show the GOI plots? I don't think the top N gene plots are as useful here.

Also, I will say no to a stepwise review - we shouldn't call any non-pass variants.

@kgaonkar6
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Is deep deletion filter ok? I reverted back to use "loss" as Del in the last commit to make it comparable to master

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@jharenza jharenza left a comment

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This 100% looks much better. I checked the LGAT plots as well and those unexpected amplifications across the fusion genes are also gone (which they should be).

For plotting Del - we tend to use deep deletions in these plots, so that is what we should do here and can specify in the manuscript.

@jaclyn-taroni
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For plotting Del - we tend to use deep deletions in these plots, so that is what we should do here and can specify in the manuscript.

My interpretation of this comment is that analyses/oncoprint-landscape/01-map-to-sample_id.R needs to be revised before this goes in - is that correct?

@jharenza
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My interpretation of this comment is that analyses/oncoprint-landscape/01-map-to-sample_id.R needs to be revised before this goes in - is that correct?

yes, I misunderstood some offline discussion with @kgaonkar6 thinking this current version already had that update.

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Okay no problem, I am going to hit this with request changes so I don't accidentally merge this before that happens!

@kgaonkar6
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kgaonkar6 commented Jul 21, 2021

Sorry about the confusion!

In the last commit status "deep deletion" is labeled as Del and "amplification" as Amp

primary_only_hgat_goi_oncoprint.png in this PR with "deep deletion" is labeled as Del and "amplification" as Amp and manta FILTER==PASS
image

Comparing here to master primary_only_hgat_goi_oncoprint.png with status=="loss" as Del and "amplification" as Amp
image

@jharenza also pointed out TTYH1 amplifications that needed to be verified. I've spot checked the TTYH1 in the 2 EMTR samples and they are supported by both cnvkit and freec so we consider these as high confidence calls :

> check_cnvkit %>%  filter(gene_symbol=="TTYH1",status=="amplification") 
# A tibble: 2 x 4
  biospecimen_id gene_symbol copy_number_cnvkit status       
  <chr>          <chr>                    <dbl> <chr>        
1 BS_TE8QFF7T    TTYH1                        7 amplification
2 BS_69VS8PS1    TTYH1                        7 amplification
> check_freec %>%  filter(gene_symbol=="TTYH1",status=="amplification") 
# A tibble: 2 x 4
  biospecimen_id gene_symbol copy_number_freec status       
  <chr>          <chr>                   <dbl> <chr>        
1 BS_K07KNTFY    TTYH1                      12 amplification
2 BS_TE8QFF7T    TTYH1                       8 amplification

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3 participants