Description Usage Arguments Details Value Author(s) Examples
View source: R/sim_functions.R
Generate the values of the test statistic under the null, and also pvalues of the clonal exclusivity test under the null. Taking the average rates of clonal exclusivity, as well as sampling from the real data for each patient, in how many trees a pair occurs and is clonally exclusive.
1 2 3  generate_test_stat_hist(avg_rates_m, list_of_num_trees_all_pats,
list_of_clon_excl_all_pats, ecdf_list, num_pat_pair_max, num_pairs_sim,
beta_distortion = 1000)

avg_rates_m 
The average rates of clonal exclusivity from all the patients in the cohort, and averaged over several trees from the collection of tree inferences. 
list_of_num_trees_all_pats 
A named list that contains an entry
for each patient which is the vector with the values of the
information from each pair in a patient of how often it was mutated
across trees. The patient odering in the list has to be the
same as in 
list_of_clon_excl_all_pats 
A named list with an entry for each
patient that is a vector with the values of in how many trees
a pair was clonally exclusive. The patient ordering in the list has to
be the same as in 
ecdf_list 
The list with ECDF's as generated with

num_pat_pair_max 
The maximum number of patients a pair is mutated in. 
num_pairs_sim 
The number of simulated gene/pathway pairs to be generated, i.e. the number of times the test statistic is computed. 
beta_distortion 
The value 
This function takes the computed average rates of clonal exclusivity
from the data (m1, ... mN), which are specific to each
patient and averaged over several trees from the collection of tree
inferences. It also takes the histogram for each patient,
of the values of how often a pair was clonally exclusive over the number
of trees it was mutated in. It also takes the empirical
cumulative distribution function (ECDF) which was generated with
generate_ecdf_test_stat
. It then computes the pvalue of
the simulated pairs under the null.
The return value is a list of tibbles with a tibble for each number of patients, a pair can be mutated in. Each tibble contains the columns 'test_statistic', 'mle_delta', and then num_pat_pair columns of the rates of each patient 'pat1', 'pat2', ...; as well as num_pat_pair columns with the information about each patient, in how many trees the pair was occurring and in how many trees the pair was clonally exclusive. The tibble also contains a column 'pval' with the pvalue of the simulated pair. The list of tibbles is of length minnum_pat_pair_max, length(avg_rates_m).
Ariane L. Moore
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33  clone_tbl < dplyr::tibble("file_name" =
rep(c(rep(c("fn1", "fn2"), each=3)), 2),
"patient_id"=rep(c(rep(c("pat1", "pat2"), each=3)), 2),
"altered_entity"=c(rep(c("geneA", "geneB", "geneC"), 4)),
"clone1"=c(0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0),
"clone2"=c(1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1),
"tree_id"=c(rep(5, 6), rep(10, 6)))
clone_tbl_pat1 < dplyr::filter(clone_tbl, patient_id == "pat1")
clone_tbl_pat2 < dplyr::filter(clone_tbl, patient_id == "pat2")
rates_exmpl_1 < compute_rates_clon_excl(clone_tbl_pat1)
rates_exmpl_2 < compute_rates_clon_excl(clone_tbl_pat2)
avg_rates_m < apply(cbind(rates_exmpl_1, rates_exmpl_2), 2, mean)
names(avg_rates_m) < c(names(rates_exmpl_1)[1], names(rates_exmpl_2)[1])
values_clon_excl_num_trees_pat1 < get_hist_clon_excl(clone_tbl_pat1)
values_clon_excl_num_trees_pat2 < get_hist_clon_excl(clone_tbl_pat2)
list_of_num_trees_all_pats <
list(pat1=values_clon_excl_num_trees_pat1[[1]],
pat2=values_clon_excl_num_trees_pat2[[1]])
list_of_clon_excl_all_pats <
list(pat1=values_clon_excl_num_trees_pat1[[2]],
pat2=values_clon_excl_num_trees_pat2[[2]])
num_pat_pair_max < 2
num_pairs_sim < 10
ecdf_list < generate_ecdf_test_stat(avg_rates_m,
list_of_num_trees_all_pats,
list_of_clon_excl_all_pats,
num_pat_pair_max, num_pairs_sim)
sim_res < generate_test_stat_hist(avg_rates_m,
list_of_num_trees_all_pats,
list_of_clon_excl_all_pats,
ecdf_list,
num_pat_pair_max,
num_pairs_sim)

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