R/gen_all_possible_vprs.r

In paularstrpo/GeTalleleR: A method for integrative analysis and visualization of DNA and RNA allele frequencies

# gen_all_possible_vprs <- function(p,a,ad_mix_res){
# # The authors make no representations about the suitability of this software for any purpose.
# # It is provided "as is" without express or implied warranty.
# #
# # p - maximal number of populations (for memory reasons max(p)=4 could be changed in future)
# # a - maximal number of alleles' 'multiplicity' (for memory reasons max(a)=5 could to be changed in future)
# #
# # output:
# # dictionaries - structure with all the possible dictionaries, e.g.
# #                dictionaries(4,3) contains dictionary for mixtures of 4
# #                populations with up to 3 alleles, and dictionaries(3,2)
# #                contains dictionary for mixtures of 3 populations with
# #                up to 2 alleles.
# # populations - structure with all the possible population proportions,
# #               e.g. populations(2) contains all the considered proportions
# #               of 2 populations
# # alleles - structure with all the alleles combinations for different
# #           numbers of populations, e.g. alleles(4,3) contains 4 elements combinations
# #           (4 populations) of 3 element set (3 alleles)
# #
# # structures for 1 popuation are empty
# #
# # If you have any questions please contact: p.m.slowinski@exeter.ac.uk
#
#
# alleles <- struct([])
# populations <- struct([])
# all_vprs <- struct([])
#
# if (a>5){
#     warning('a - alleles multiplicity is to high')
#     return
# }
#
# if (p>4){
#     warning('to many populations')
#     return
# }
#
# for (i_al in 1:a){
#     for (i_pop in 1:(p-1)){
#         alleles(i_pop+1,i_al).a <- combinator(i_al+1,i_pop,'c','r')-1
#         alleles(i_pop+1,i_al).b <- combinator(i_al+1,i_pop,'c','r')-1
#     }
# }
#
#
# for (i in 2:p){
#     p_pre <- (combinator(round(1/ad_mix_res+1),i-1,'p','r')-1)*ad_mix_res
#     p_pre <- round(p_pre,2)
#     p_pre <- p_pre(sum(p_pre,2)<1,:)
#     p_all <- [p_pre 1-sum(p_pre,2)]
#     p_all <- round(p_all,2)
#     p_all <- p_all(2:},:)
#     i0=(p_all==0)
#     populations(i).p=p_all(sum(i0,2)==0,:)
# }
#
# for (i_al in 1:a){
#     for (i_pop in 2:p){
#         disp(['all vprs for: ' num2str(i_al) ' events, ' num2str(i_pop) ' populations'])){
#         sz_alA <- size(alleles(i_pop,i_al).a,1)
#         sz_alB <- size(alleles(i_pop,i_al).b,1)
#         sz_pop <- size(populations(i_pop).p,1)
#         all_vprs(i_pop,i_al).vprs <- NaN(sz_alA,sz_alB,sz_pop)
#
#         for (iA in 1:sz_alA){
#             for (iB in 1:sz_alB){
#                 for (i_pop_prop in 1:sz_pop){
#                     A <- sum([1 alleles(i_pop,i_al).a(iA,:)].*populations(i_pop).p(i_pop_prop,:))
#                     B <- sum([1 alleles(i_pop,i_al).b(iB,:)].*populations(i_pop).p(i_pop_prop,:))
#                     all_vprs(i_pop,i_al).vprs(iA,iB,i_pop_prop) <- B/(A+B)
#                 }
#             }
#         }
#
#         if (i_pop==2){
#             all_vprs(i_pop,i_al).vprs(1,1,i_pop_prop) <- 0
#         } else if (i_pop==3){
#             if (i_al==1){
#                 all_vprs(i_pop,i_al).vprs(1:2,1:2,i_pop_prop) <- 0
#             } else if (i_al==2){
#                 all_vprs(i_pop,i_al).vprs(1:3,1:3,i_pop_prop) <- 0
#             } else if (i_al==3){
#                 all_vprs(i_pop,i_al).vprs(1:4,1:4,i_pop_prop) <- 0
#             } else if (i_al==4){
#                 all_vprs(i_pop,i_al).vprs(1:5,1:5,i_pop_prop) <- 0
#             }
#         } else if (i_pop==4){
#             if (i_al==1){
#                 all_vprs(i_pop,i_al).vprs(1:3,1:3,i_pop_prop) <- 0
#             } else if (i_al==2){
#                 all_vprs(i_pop,i_al).vprs(1:6,1:6,i_pop_prop) <- 0
#             } else if (i_al==3){
#                 all_vprs(i_pop,i_al).vprs(1:10,1:10,i_pop_prop) <- 0
#             } else if (i_al==4){
#                 all_vprs(i_pop,i_al).vprs(1:15,1:15,i_pop_prop) <- 0
#             }
#         }
#     }
# }