Consensus seg update for oncoplot

[kgaonkar6]

Purpose/implementation Section

What scientific question is your analysis addressing?

We are adding CNV calls from consensus seg files for autosomes , X and Y chromosomes as inputs for oncoplots.

What was your approach?

Update the input files to :

consensus_seg_autosomes_cnv_file=${focal_directory}/consensus_seg_annotated_cn_autosomes.tsv.gz
consensus_seg_cnv_xy_cnv_file=${focal_directory}/consensus_seg_annotated_cn_x_and_y.tsv.gz

What GitHub issue does your pull request address?

#1087

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

Which areas should receive a particularly close look?

The figures need careful review , the only update in consensus seg files and we are currently coding loss as "Del" and amplification and gain as "Amp" to match the maftools requirements as in #1009

Is there anything that you want to discuss further?

Na

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?

ATRX calls are now added with use of X and Y chromosome calls and all CNV overlapping gene locations are being plotted.

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]

Thanks @kgaonkar6 for these changes. It looks like you just did a basic swap of the consensus seg in place of the focal CN file, but we also need to code the amps appropriately (I think we had used CN*2(?) throughout this repository). Can you make that adjustment with this PR? Thanks!

[jharenza]

per this comment, we need to separate out gains and amplifications here and only carry through amps to the oncopolot - can you update this?

[jaclyn-taroni]

To give a little more context (and to interject with my opinion about how to do this)...

Currently we recode this in 02-plot-oncoprint.R:

OpenPBTA-analysis/analyses/oncoprint-landscape/02-plot-oncoprint.R

Line 149 in c074457

dplyr::mutate(Variant_Classification = dplyr::case_when(Variant_Classification == "loss" ~ "Del",

I would remove that from 02-plot-oncoprint.R and move it into this script (01-map-to-sample_id.R), where you mutate Variant_Classification:

dplyr::case_when(
  Variant_Classification == "loss" ~ "Del",
  Variant_Classification  == "amplification" ~ "Amp",
  TRUE ~ as.character(Variant_Classification)
)

And then filter with Variant_Classification %in% c("Del", "Amp")

[kgaonkar6]

Updated now

[jaclyn-taroni]

Just re-requested @jharenza, I'm going to wait to review #1092 until this gets another look

[jharenza]

Thanks, I took a brief look yesterday, but think we also need a "deep deletion filter", as hemizygous deletions aren't usually shown in the oncoprints, but I need to think and read about this a little more before proposing a threshold.

[jaclyn-taroni]

Thanks, I took a brief look yesterday, but think we also need a "deep deletion filter", as hemizygous deletions aren't usually shown in the oncoprints, but I need to think and read about this a little more before proposing a threshold.

Is that filter necessary for this PR or can it be done later, in a separate pull request, once you have had a chance to read and digest some material?

[jharenza]

Thanks, I took a brief look yesterday, but think we also need a "deep deletion filter", as hemizygous deletions aren't usually shown in the oncoprints, but I need to think and read about this a little more before proposing a threshold.

Is that filter necessary for this PR or can it be done later, in a separate pull request, once you have had a chance to read and digest some material?

We can do later, thanks!

#1088 (comment)