add Ns and percents to co-occurrence tables for paper

[jharenza]

Purpose/implementation Section

What scientific question is your analysis addressing?

Added additional stats to tables for ease of manuscript writing.

What was your approach?

I added the following calculations to the cooccur_functions.R script and reran the code to add this information to the results tables

• n_mutated_gene1
• n_mutated_gene2
• perc_mutated_gene1
• perc_mutated_gene2
• perc_cooccur_or_mutexcl

What GitHub issue does your pull request address?

NA

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

Which areas should receive a particularly close look?

Updates in cooccur_functions.R

Is there anything that you want to discuss further?

Added comment within the function R script:

#' @return A data frame summarizing the co-occurence of pairs of genes in the
#'   gene list with columns `gene1`; `gene2`; counts of each mutations in
#'   each category of sharing (`mut11`: both mutated; `mut10`: mutated in
#'   the first but not second gene, etc.) and summary (`n_mutated_gene1` and 
#'   `n_mutated_gene2`: number of samples with gene1 or gene2 mutated, ) `n_mutated_gene1`); 
#'   `odds_ratio` for co-occurence, `cooccur_sign`: 1 if co-occurence greater than by chance, 
#'   -1 if less frequent than expected; `p` the fisher's exact test p value; and a
#'   `cooccur_score` calculated as `cooccur_sign * -log10(p)`. Overall percent 
#'   of samples mutated denoted as `perc_mutated_gene1` and `perc_mutated_gene2`. 
#'   Percent of gene1-mutated samples in w#' @return A data frame summarizing the co-occurence of pairs of genes in the
#'   gene list with columns `gene1`; `gene2`; counts of each mutations in
#'   each category of sharing (`mut11`: both mutated; `mut10`: mutated in
#'   the first but not second gene, etc.) and summary (`n_mutated_gene1` and 
#'   `n_mutated_gene2`: number of samples with gene1 or gene2 mutated, ) `n_mutated_gene1`); 
#'   `odds_ratio` for co-occurence, `cooccur_sign`: 1 if co-occurence greater than by chance, 
#'   -1 if less frequent than expected; `p` the fisher's exact test p value; and a
#'   `cooccur_score` calculated as `cooccur_sign * -log10(p)`. Overall percent 
#'   of samples mutated denoted as `perc_mutated_gene1` and `perc_mutated_gene2`. 
#'   Percent of gene1-mutated samples in which gene2 mutations are co-occuring or mutually exclusive 
#'   denoted as `perc_cooccur_or_mutexcl`.

Is this clear?

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)?

Tables

What is your summary of the results?

Appended 5 columns to results tables. All other values and plots remain unchanged.

Reproducibility Checklist

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

Documentation Checklist

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

[jharenza]

thanks @jashapiro!