R/all_vpr_to_matrix.r

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

# all_vpr_to_matrix <- function(chrm,CNA,swt){
# #chr start_pos end_pos mode_l1 mode_l2 mode_l3 mode_l4 ...
# k <- 1
# switch swt
#     case 'Tex'
#         for (chr in 1:22){
#             nb_modes <- size(chrm(chr).data_and_stats_Tex.fitted_vprs,2)
#
#             CNAchr=CNA(CNA(:,1)==chr,:)
#             [pos_CNA,ims_CNA] <- unique([CNAchr(:,2); CNAchr(:,3)+eps])
#             all_CNA <- [CNAchr(:,5); CNAchr(:,5)]
#             all_CNA <- all_CNA(ims_CNA)
#
#             for (m in 1:nb_modes){
#                 win_idx <- chrm(chr).data_and_stats_Tex.vpr_data(m).window
#
#                 s_window <- sort(win_idx,'asc}')
#                 idx_jump <- find(diff(s_window)>1)){
#
#                 nb_jumps <- numel(idx_jump)
#                 win_edges_val <- []
#
#                 if (nb_jumps>0){
#                     win_edges_idx <- sort([s_window(1) s_window(idx_jump) s_window(idx_jump+1)  s_window(length(s_window))])
#                     win_edges_val <- chrm(chr).data_and_stats_Tex.segment(win_edges_idx,1)
#                 } else {
#                     win_edges_idx <- [s_window(1) s_window(length(s_window))]
#                     win_edges_val <- chrm(chr).data_and_stats_Tex.segment(win_edges_idx,1)
#                 }
#
#
#                 for (jmp in 1:2:numel(win_edges_val)){
#                     out(k,1) <- chr
#                     out(k,2) <- win_edges_val(jmp)
#                     out(k,3) <- win_edges_val(jmp+1)
#                     out(k,4:7) <- chrm(chr).data_and_stats_Tex.fitted_vprs(:,m)
#                     out(k,8:11) <- chrm(chr).data_and_stats_Tex.pv_ks_05(:,m)
#                     out(k,12:17) <- chrm(chr).data_and_stats_Tex.pv_ks_ll(:,m)
#
#                     mds_pos <- chrm(chr).data_and_stats_Tex.segment(win_edges_idx(jmp):win_edges_idx(jmp+1),1)
#
#                     interp_mds_CNA <- interp1(pos_CNA,all_CNA,mds_pos,'nearest','extrap')
#                     out(k,18) <- median(interp_mds_CNA)
#                     out(k,19) <- numel(mds_pos)
#                     out(k,20) <- win_edges_idx(jmp)
#                     k <- k+1
#                 }
#             }
#         }
#     case 'Ttr'
#         for (chr in 1:22){
#             nb_modes <- size(chrm(chr).data_and_stats_Ttr.fitted_vprs,2)
#
#             CNAchr=CNA(CNA(:,1)==chr,:)
#             [pos_CNA,ims_CNA] <- unique([CNAchr(:,2); CNAchr(:,3)+eps])
#             all_CNA <- [CNAchr(:,5); CNAchr(:,5)]
#             all_CNA <- all_CNA(ims_CNA)
#
#             for (m in 1:nb_modes){
#                 win_idx <- chrm(chr).data_and_stats_Ttr.vpr_data(m).window
#
#                 s_window <- sort(win_idx,'asc}')
#                 idx_jump <- find(diff(s_window)>1)){
#
#                 nb_jumps <- numel(idx_jump)
#                 win_edges_val <- []
#
#                 if (nb_jumps>0){
#                     win_edges_idx <- sort([s_window(1) s_window(idx_jump) s_window(idx_jump+1)  s_window(length(s_window))])
#                     win_edges_val <- chrm(chr).data_and_stats_Ttr.segment(win_edges_idx,1)
#                 } else {
#                     win_edges_idx <- [s_window(1) s_window(length(s_window))]
#                     win_edges_val <- chrm(chr).data_and_stats_Ttr.segment(win_edges_idx,1)
#                 }
#
#
#
#                 for (jmp in 1:2:numel(win_edges_val)){
#                     out(k,1) <- chr
#                     out(k,2) <- win_edges_val(jmp)
#                     out(k,3) <- win_edges_val(jmp+1)
#                     out(k,4:7) <- chrm(chr).data_and_stats_Ttr.fitted_vprs(:,m)
#                     out(k,8:11) <- chrm(chr).data_and_stats_Ttr.pv_ks_05(:,m)
#                     out(k,12:17) <- chrm(chr).data_and_stats_Ttr.pv_ks_ll(:,m)
#
#                     mds_pos <- chrm(chr).data_and_stats_Ttr.segment(win_edges_idx(jmp):win_edges_idx(jmp+1),1)
#
#                     interp_mds_CNA <- interp1(pos_CNA,all_CNA,mds_pos,'nearest','extrap')
#                     out(k,18) <- median(interp_mds_CNA)
#                     out(k,19) <- numel(mds_pos)
#                     out(k,20) <- win_edges_idx(jmp)
#
#                     k <- k+1
#                 }
#             }
#         }
# }