#1144 part1: Interaction plot updated to cancer_groups

[kgaonkar6]

Purpose/implementation Section

What scientific question is your analysis addressing?

The barplot visualizing the distribution of the disease groups needs to be updated to cancer_group.

What was your approach?

The only update is changing

OpenPBTA-analysis/analyses/interaction-plots/scripts/02-process_mutations.R

Line 336 in 1d1ace9

dplyr::group_by(gene, disease = broad_histology) %>%

to

dplyr::group_by(gene, disease = cancer_group)

What GitHub issue does your pull request address?

#1144

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

Which areas should receive a particularly close look?

• We only want to update the barplot to cancer group, but the original cooccurrence analysis would remain at broad_histology level.
  The update will affect the following files, all other file changed in the PR it seems are from small decimal value changes.
  results/gene_disease_top50.tsv
  plots/gene_disease_top50.png

• In addition to updating disease group as cancer_group I'm also removing cancer_group == NA while plotting barplot because otherwise a NA factor with no color is added. These are samples with harmonized_diagnosis == Benign tumor, Dysplasia/Gliosis which will not have a cancer_group.
  Like:

• The code uses the top 7 disease group to add to the barplot, all remaining groups will be grouped as "Other" . For plotting it makes sense to me but just want to confirm because we specifically keep "Other" (and Benign) tumors as NA while assigning cancer_groups.

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 and tables

What is your summary of the results?

Updated diagnosis groups

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]

The code uses the top 7 disease group to add to the barplot, all remaining groups will be grouped as "Other" . For plotting it makes sense to me but just want to confirm because we specifically keep "Other" (and Benign) tumors as NA while assigning cancer_groups.

It looks like there are a lot of Other samples for NF2, and these include 7 meningiomas - can we do the top 10, but select by diseases with highest number of mutated samples per disease, as below? Right now, it is by highest number of genes mutated per disease. This will let us capture a few more.

dis <- read_tsv("gene_disease_top50.tsv")
ns <- dis %>%
  group_by(disease, mutant_samples) %>%
  tally() %>%
  arrange(-mutant_samples) %>%
  select(disease) %>%
  distinct()

The rest being Other in grey is OK.

[kgaonkar6]

Thanks for the review @jharenza ! In addition to including your suggestion above, I've also used the cancer_group_hex_code now so that the colors are consistent as you suggested in another PR ( that also was a to-do in the original script )

[jharenza]

looks good! thank you!