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02_run_sig_algos.m
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02_run_sig_algos.m
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%
% 1. Log-normal Poisson model {{{
%
%compile
% cd LNP
% mcc -m -d mcc \
% `find ref -maxdepth 1 -type f -exec echo -n "-a {} " \;` \
% `find src -type d -exec echo -n "-I {} " \;` \
% -I ../funcs \
% src/pois_LN_reg_wrapper
%
% generate run files {{{
%
% pan-cancer {{{
clear
mafpath = 'mutation_data/MC3.align75.ICE_PoN-uniqued.M';
basedir = 'LNP_posteriors/pancancer';
ede(basedir)
%base params file
load('LNP_posteriors/params.mat', 'P')
P.covar_dir = {'ref/cod_dnasehs_covars.zerofilled.align75_filtered/v3' ...
'ref/cod_nuc_covars.align75_filtered/Gm12878' ...
'ref/cod_mutsig_transformed_covars.align75_filtered/exprmax_transformed' ...
'ref/cod_mutsig_transformed_covars.align75_filtered/rt_transformed' ...
'ref/cod_c512_covars.align75_filtered/v1'};
P.compute_posterior_predictive = 1;
P.pp_recurrence_floor = 1;
P.compute_marginal_likelihood = 0;
P.init_from_MAP = 0;
P.tailor_hyperparameters = 1;
P.niter = 3e3;
P.random_effect_cat_covar_index = 'last';
P.skip_figures = 1;
P.log_exposure = log(9019); %original number of patients was 9023, but four patients had mutations
%solely in regions we filtered out.
P.outdir = [basedir '/output'];
ede(P.outdir)
%RC LuT
c1024 = dec2base(0:1023, 4) - 48;
c1024 = [(1:1024)' sum(bsxfun(@times, 3 - c1024(:, [1 3 2 5 4]), [4^4 4^3 4^2 4 1]), 2) + 1];
%generate ch96 names
bc = {'A->C' 'A->G' 'A->T' 'C->A' 'C->G' 'C->T'};
B = {'A' 'C' 'G' 'T'};
tmp = dec2base((1:96) - 1, 4) - 48;
name96 = strcat(B(tmp(:, end - 1) + 1), '(', bc(sum(bsxfun(@times, tmp(:, 1:end - 2), [4 1]), 2) + 1), ')', B(tmp(:, end) + 1))';
%enumerate pentamers associated with each trimer+basechange
[c512 nb] = meshgrid(1:512, 1:3);
c512_nb = [c512(:) nb(:)];
Y = [];
Y.c512 = c512_nb(:, 1);
b = dec2base(Y.c512 - 1, 4, 5) - 48;
Y.nb = c512_nb(:, 2);
Y.ch96 = 1 + [b(:, [2 3]) Y.nb - 1 b(:, 1)]*[1 4 16 48]';
Y = sort_struct(Y, {'ch96'});
[~, ui] = unique(Y.ch96);
%loop over ch96's, generate runfile
f = fopen([basedir '/runs.cmd'], 'w');
for x = [ui [ui(2:end) - 1; slength(Y)]]',
i = x(1); j = x(2);
P.output_name = sprintf('%d-%s_%d', Y.ch96(i), name96{Y.ch96(i)}, f);
paramspath = sprintf('%s/params_%d.mat', basedir, Y.ch96(i));
save(paramspath, 'P')
contexts = strjoin(num2cellstr([Y.c512(i:j); c1024(Y.c512(i:j), 2)]), ',');
fprintf(f, 'mcc/pois_LN_reg_wrapper %s %s c1025 %d %s\n', mafpath, contexts, Y.nb(i), paramspath);
end
fclose(f);
%}}}
%
% signature subcohorts {{{
clear
%
%index channels of interest
X = generate_signature_subcohort_definitions('mutation_data/MC3.align75.ICE_PoN-uniqued.sig_cohorts/*.M');
%RC LuT
c1024 = dec2base(0:1023, 4) - 48;
c1024 = [(1:1024)' sum(bsxfun(@times, 3 - c1024(:, [1 3 2 5 4]), [4^4 4^3 4^2 4 1]), 2) + 1];
%base params file
load('LNP_posteriors/params.mat', 'P')
covar_blocks = {{'ref/cod_dnasehs_covars.zerofilled.align75_filtered/v3' 'ref/cod_nuc_covars.align75_filtered/Gm12878'} ...
{'ref/cod_mutsig_transformed_covars.align75_filtered/exprmax_transformed' 'ref/cod_mutsig_transformed_covars.align75_filtered/rt_transformed'}};
P = rmfield(P, 'covar_names');
P.compute_posterior_predictive = 0;
P.compute_marginal_likelihood = 0;
P.init_from_MAP = 0;
P.interval_list = 'ref/target_list.align75_filtered_tier4.txt';
P.tailor_hyperparameters = 1;
P.niter = 3e3;
P.skip_figures = 1;
%generate ch96 names
bc = {'A->C' 'A->G' 'A->T' 'C->A' 'C->G' 'C->T'};
B = {'A' 'C' 'G' 'T'};
tmp = dec2base((1:96) - 1, 4) - 48;
name96 = strcat(B(tmp(:, end - 1) + 1), '(', bc(sum(bsxfun(@times, tmp(:, 1:end - 2), [4 1]), 2) + 1), ')', B(tmp(:, end) + 1))';
%
%generate runfiles (trimers) {{{
%
% runfiles for each signature (NMF) {{{
%overwrite previous definition of X with one tailored to NMF output
X = generate_signature_subcohort_definitions('mutation_data/MC3.align75.ICE_PoN-uniqued.sig_cohorts_NMF/*.M');
%loop over signature subcohorts
for i = 1:slength(X),
mafpath = X.mafpath{i};
M = loadM(mafpath);
basedir = ['LNP_posteriors/signature_subcohorts/' X.names{i}];
ede(basedir)
ede([basedir '/params'])
%P.log_exposure = log(X.npat(i));
P.outdir = [basedir '/output/'];
ede(P.outdir)
%8 runfiles: for each combination of covariate blocks/whether we use pentamers
fs = NaN(8, 1);
for f = 1:8,
fs(f) = fopen(sprintf('%s/runs_%d.cmd', basedir, f - 1), 'w');
end
for j = 1:length(X.output{i}),
Y = [];
Y.c512 = X.output{i}{j}(:, 1);
b = dec2base(Y.c512 - 1, 4, 5) - 48;
Y.nb = X.output{i}{j}(:, 2);
Y.ch96 = 1 + [b(:, [2 3]) Y.nb - 1 b(:, 1)]*[1 4 16 48]';
Y = sort_struct(Y, {'ch96'});
[~, ui] = unique(Y.ch96);
for x = [ui [ui(2:end) - 1; slength(Y)]]',
k = x(1); l = x(2);
%skip if too few mutations for meaningful fit
%50 is empirically chosen cutoff.
if nnz(M.mut.count_nb(M.mut.tier == 4 & ismember(M.mut.c512, Y.c512(k:l)), Y.nb(k)) >= 2) < 50, continue; end
contexts = strjoin(num2cellstr([Y.c512(k:l); c1024(Y.c512(k:l), 2)]), ',');
%number of patients for this channel
P.log_exposure = log(full(M.npat(Y.ch96(k))));
%write output files
for f = 0:7,
fidx = logical(dec2base(mod(f, 4), 2, 2) - 48);
P.covar_dir = cat(2, covar_blocks{fidx});
%whether we use context as covariates
if f <= 3,
P.covar_dir = cat(2, P.covar_dir, 'ref/cod_c512_covars.align75_filtered/v1');
P.random_effect_cat_covar_index = 'last';
else
if isfield(P, 'random_effect_cat_covar_index'),
P = rmfield(P, 'random_effect_cat_covar_index');
end
end
if isempty(P.covar_dir), P.covar_dir = 'none'; end
paramspath = sprintf('%s/params/params_%d_%d.mat', basedir, Y.ch96(k), f);
P.output_name = sprintf('%d-%s_%d', Y.ch96(k), name96{Y.ch96(k)}, f);
save(paramspath, 'P')
fprintf(fs(f + 1), 'mcc/pois_LN_reg_wrapper %s %s c1025 %d %s\n', mafpath, contexts, Y.nb(k), paramspath);
end
end
end
for f = 1:8, fclose(fs(f)); end
end
% }}}
%
% runfiles for UV signature subcohort redux {{{
% while we're at it, why not use the NMF-defined UV subcohort just to be consistent
% this also keeps the output path names unique
X = generate_signature_subcohort_definitions('mutation_data/MC3.align75.ICE_PoN-uniqued.sig_cohorts_NMF/*.M');
i = 7;
mafpath = X.mafpath{i};
M = loadM(mafpath);
basedir = ['LNP_posteriors/signature_subcohorts/' X.names{i}];
ede(basedir)
ede([basedir '/params'])
P.outdir = [basedir '/output/'];
ede(P.outdir)
%get context512 names
LuT = load_struct('ref/context_1025_categs.txt');
LuT = makeapn(LuT);
LuT = reorder_struct_exclude(LuT, 1025);
LuT.name = cellfun(@(x) [x(6:7) x(1) x(9:10)], LuT.name, 'unif', 0);
%only one regex necessary for UV signature
j = 1;
Y = [];
Y.c512 = X.output{i}{j}(:, 1);
b = dec2base(Y.c512 - 1, 4, 5) - 48;
Y.nb = X.output{i}{j}(:, 2);
Y.ch96 = 1 + [b(:, [2 3]) Y.nb - 1 b(:, 1)]*[1 4 16 48]';
Y = sort_struct(Y, {'ch96'});
[~, ui] = unique(Y.ch96);
%init the two runfiles' handles
fs = NaN(9, 1);
for f = 8:9,
fs(f) = fopen(sprintf('%s/runs_%d.cmd', basedir, f), 'w');
end
for x = [ui [ui(2:end) - 1; slength(Y)]]',
k = x(1); l = x(2);
contexts = [Y.c512(k:l) c1024(Y.c512(k:l), 2)];
%number of patients for this channel
P.log_exposure = log(full(M.npat(Y.ch96(k))));
%write to both output files (XR + intrinsic covars, intrinsic covars only)
for f = 8:9,
if f == 8,
P.covar_dir = cat(2, covar_blocks{:}, direc('ref/cod_XR_covars.align75_filtered/XPC*')');
else
P.covar_dir = cat(2, covar_blocks{:});
end
%loop over pentamer contexts
for q = 1:size(contexts, 1),
c1024s = contexts(q, :);
%skip if too few mutations for meaningful fit
%50 is empirically chosen cutoff.
if nnz(M.mut.count_nb(M.mut.tier == 4 & ismember(M.mut.c512, c1024s(1)), Y.nb(k)) >= 2) < 50, continue; end
paramspath = sprintf('%s/params/params_%d_%d.mat', basedir, c1024s(1), f);
%note that this output nomenclature is inconsistent with everything else, since a separate
%output is saved for each pentamer. since there is only one newbase for UV, we will index
%by c512 only.
%P.output_name = sprintf('%d-%s_%d', Y.ch96(k), name96{Y.ch96(k)}, 8);
P.output_name = sprintf('%d-%s_%d', c1024s(1), LuT.name{c1024s(1)}, f);
save(paramspath, 'P')
fprintf(fs(f), 'mcc/pois_LN_reg_wrapper %s %s c1025 %d %s\n', mafpath, strjoin(num2cellstr(c1024s), ','), Y.nb(k), paramspath);
end
end
end
fclose(fs(8));
fclose(fs(9));
% }}}
%
% runfiles for APOBEC hairpin {{{
X = generate_signature_subcohort_definitions('mutation_data/MC3.align75.ICE_PoN-uniqued.sig_cohorts_NMF/*.M');
i = 1;
mafpath = X.mafpath{i};
M = loadM(mafpath);
basedir = ['LNP_posteriors/signature_subcohorts/' X.names{i}];
ede(basedir)
ede([basedir '/params'])
P.outdir = [basedir '/output/'];
ede(P.outdir)
%get context512 names
LuT = load_struct('ref/context_1025_categs.txt');
LuT = makeapn(LuT);
LuT = reorder_struct_exclude(LuT, 1025);
LuT.name = cellfun(@(x) [x(6:7) x(1) x(9:10)], LuT.name, 'unif', 0);
%only one regex necessary for APOBEC signature
j = 1;
Y = [];
Y.c512 = X.output{i}{j}(:, 1);
b = dec2base(Y.c512 - 1, 4, 5) - 48;
Y.nb = X.output{i}{j}(:, 2);
Y.ch96 = 1 + [b(:, [2 3]) Y.nb - 1 b(:, 1)]*[1 4 16 48]';
Y = sort_struct(Y, {'ch96'});
[u, ui] = unique(Y.ch96);
%add hairpin covariates
P.covar_dir = cat(2, covar_blocks{:}, 'ref/cod_APOBEC.lawrence.hairpin_covars.align75_filtered/v2', 'ref/cod_c512_covars.align75_filtered/v1');
P.random_effect_cat_covar_index = 'last';
%init the runfile's handle
f = 9;
fs = fopen(sprintf('%s/runs_%d.cmd', basedir, f), 'w');
for x = [ui [ui(2:end) - 1; slength(Y)]]',
k = x(1); l = x(2);
%skip if too few mutations for meaningful fit
%50 is empirically chosen cutoff.
if nnz(M.mut.count_nb(M.mut.tier == 4 & ismember(M.mut.c512, Y.c512(k:l)), Y.nb(k)) >= 2) < 50, continue; end
contexts = strjoin(num2cellstr([Y.c512(k:l); c1024(Y.c512(k:l), 2)]), ',');
%number of patients for this channel
P.log_exposure = log(full(M.npat(Y.ch96(k))));
paramspath = sprintf('%s/params/params_%d_%d.mat', basedir, Y.ch96(k), f);
P.output_name = sprintf('%d-%s_%d', Y.ch96(k), name96{Y.ch96(k)}, f);
save(paramspath, 'P')
fprintf(fs, 'mcc/pois_LN_reg_wrapper %s %s c1025 %d %s\n', mafpath, contexts, Y.nb(k), paramspath);
end
% manually add runs for channel 94 (allowing us to assess RXRA)
k = ui(find(u == 94)); l = ui(find(u == 94) + 1) - 1;
contexts = strjoin(num2cellstr([Y.c512(k:l); c1024(Y.c512(k:l), 2)]), ',');
P.log_exposure = log(full(M.npat(Y.ch96(k))));
paramspath = sprintf('%s/params/params_%d_%d.mat', basedir, Y.ch96(k), f);
P.output_name = sprintf('%d-%s_%d', Y.ch96(k), name96{Y.ch96(k)}, 9);
save(paramspath, 'P')
fprintf(fs, 'mcc/pois_LN_reg_wrapper %s %s c1025 %d %s\n', mafpath, contexts, Y.nb(k), paramspath);
% we also need to run this sans hairpin track (since it wasn't run in the first place)
P.covar_dir = cat(2, covar_blocks{:}, 'ref/cod_c512_covars.align75_filtered/v1');
f = 3;
paramspath = sprintf('%s/params/params_%d_%d.mat', basedir, Y.ch96(k), f);
P.output_name = sprintf('%d-%s_%d', Y.ch96(k), name96{Y.ch96(k)}, f);
save(paramspath, 'P')
fprintf(fs, 'mcc/pois_LN_reg_wrapper %s %s c1025 %d %s\n', mafpath, contexts, Y.nb(k), paramspath);
fclose(fs);
% }}}
%}}}
%}}}
%
% hypermutant split subcohorts {{{
clear
X = generate_signature_subcohort_definitions('mutation_data/MC3.align75.ICE_PoN-uniqued.sig_cohorts_NMF/*.M');
X = rmfield(X, 'mafpath');
%update MAFpath to reflect splits
F = [];
F.file = direc('mutation_data/MC3.align75.ICE_PoN-uniqued.mutratesplit_NMF/*');
F = parsein(F, 'file', '.*/(.*)_(hi|lo).M$', {'sig' 'rate'});
F = sort_struct(F, {'sig' 'rate'});
F_hi = reorder_struct(F, strcmp(F.rate, 'hi'));
X.mafpath_hi = mapacross(X.names, F_hi.sig, F_hi.file);
F_lo = reorder_struct(F, strcmp(F.rate, 'lo'));
X.mafpath_lo = mapacross(X.names, F_lo.sig, F_lo.file);
%RC LuT
c1024 = dec2base(0:1023, 4) - 48;
c1024 = [(1:1024)' sum(bsxfun(@times, 3 - c1024(:, [1 3 2 5 4]), [4^4 4^3 4^2 4 1]), 2) + 1];
%base params file
load('LNP_posteriors/params.mat', 'P')
P.covar_dir = {'ref/cod_dnasehs_covars.zerofilled.align75_filtered/v3' ...
'ref/cod_nuc_covars.align75_filtered/Gm12878' ...
'ref/cod_mutsig_transformed_covars.align75_filtered/exprmax_transformed' ...
'ref/cod_mutsig_transformed_covars.align75_filtered/rt_transformed' ...
'ref/cod_c512_covars.align75_filtered/v1'};
P.random_effect_cat_covar_index = 'last';
P = rmfield(P, 'covar_names');
P.compute_posterior_predictive = 0;
P.compute_marginal_likelihood = 0;
P.init_from_MAP = 0;
P.interval_list = 'ref/target_list.align75_filtered_tier4.txt';
P.tailor_hyperparameters = 1;
P.niter = 3e3;
P.skip_figures = 1;
%generate ch96 names
bc = {'A->C' 'A->G' 'A->T' 'C->A' 'C->G' 'C->T'};
B = {'A' 'C' 'G' 'T'};
tmp = dec2base((1:96) - 1, 4) - 48;
name96 = strcat(B(tmp(:, end - 1) + 1), '(', bc(sum(bsxfun(@times, tmp(:, 1:end - 2), [4 1]), 2) + 1), ')', B(tmp(:, end) + 1))';
%loop over signature subcohorts
for i = 1:slength(X),
for rates = {'lo' 'hi'},
rate = rates{1};
field = ['mafpath_' rate];
mafpath = X.(field){i};
M = loadM(mafpath);
basedir = ['LNP_posteriors/signature_subcohorts_mutrate_split/' X.names{i} '_' rate];
ede(basedir)
ede([basedir '/params'])
P.outdir = [basedir '/output/'];
ede(P.outdir)
%open runfile
f = fopen([basedir '/runs.cmd'], 'w');
for j = 1:length(X.output{i}),
Y = [];
Y.c512 = X.output{i}{j}(:, 1);
b = dec2base(Y.c512 - 1, 4, 5) - 48;
Y.nb = X.output{i}{j}(:, 2);
Y.ch96 = 1 + [b(:, [2 3]) Y.nb - 1 b(:, 1)]*[1 4 16 48]';
Y = sort_struct(Y, {'ch96'});
[~, ui] = unique(Y.ch96);
for x = [ui [ui(2:end) - 1; slength(Y)]]',
k = x(1); l = x(2);
%skip if too few mutations for meaningful fit
%50 is empirically chosen cutoff.
if nnz(M.mut.count_nb(M.mut.tier == 4 & ismember(M.mut.c512, Y.c512(k:l)), Y.nb(k)) >= 2) < 50, continue; end
contexts = strjoin(num2cellstr([Y.c512(k:l); c1024(Y.c512(k:l), 2)]), ',');
%number of patients for this channel
P.log_exposure = log(full(M.npat(Y.ch96(k))));
paramspath = sprintf('%s/params/params_%d.mat', basedir, Y.ch96(k));
P.output_name = sprintf('%d-%s', Y.ch96(k), name96{Y.ch96(k)});
save(paramspath, 'P')
fprintf(f, 'mcc/pois_LN_reg_wrapper %s %s c1025 %d %s\n', mafpath, contexts, Y.nb(k), paramspath);
end
end
fclose(f);
end
end
%prune output to ensure that only channels with enough mutations for both hi/lo are represented
F = [];
F.params = direc('LNP_posteriors/signature_subcohorts_mutrate_split/*/params/*.mat');
F = parsein(F, 'params', '.*/([A-Z_]+)_(hi|lo).*params_(\d+)\.mat$', {'sig' 'rate' 'ch96'});
F = makeapn(F);
[~, ~, ruj] = unique(F.rate);
[~, ~, suj] = unique(F.sig);
F.uid = mod(F.ch96 + 768*(ruj - 1) + 96*(suj - 1), 768);
hilo_ch_idx = find(accumarray(F.uid, 1) == 2);
F.use = ismember(F.uid, hilo_ch_idx);
save_lines(F.params(F.use), 'LNP_posteriors/signature_subcohorts_mutrate_split/hilo_runs.txt')
%}}}
% }}}
%
% dispatch run files:
% running will take quite some time to finish, so it is recommended that they
% be dispatched to a cluster.
% dispatch the commands output by this bash command:
% find LNP_posteriors/ -name "*.cmd" -exec cat {} +
%}}}
%
% 2. Uniform Poisson {{{
clear
%we can pull covariates from LNP hierarchical runs
F = [];
F.file = direc('LNP_posteriors/pancancer/output/*.mat');
F = parsein(F, 'file', '.*output/(\d+)-(.*)_3.mat', {'ch96' 'context'});
F.ch96 = str2double(F.ch96);
F = sort_struct(F, 'ch96');
ch1536lut = load_struct('ref/1536_LuT.txt');
ch1536lut = makeapn(ch1536lut);
ch1536lut = sparse(ch1536lut.c512, ch1536lut.nbidx, ch1536lut.ch1536);
Beta = NaN(slength(F), 20);
M = cell(slength(F), 1);
pv = cell(slength(F), 1);
XX = cell(slength(F), 1);
n_extra = NaN(slength(F), 1);
pp = parpool(16);
parfor j = 1:slength(F),
X = load(F.file{j});
n_extra(j) = nnz(X.M == 0);
C = [X.C(:, 1:4) full(sparse(1:length(X.C), X.C(:, 5), 1))];
[BetaP, dBeta, stats] = glmfit(C, X.M, 'poisson', 'constant', 'off');
%compute p-values
pois_p = poisspdf(X.M, exp(C*BetaP));
pv{j} = pois_p + poisscdf(X.M, exp(C*BetaP), 'upper');
X.Mu.p_unif = pv{j}(X.M > 0);
X.Mu.prob_unif = pois_p(X.M > 0);
%add newbase information
nb = mod(ceil(F.ch96(j)/16) - 1, 3) + 1;
X.Mu.ch1536 = full(ch1536lut(X.Mu.c512, nb));
M{j} = X.Mu;
X.pois_prob = pois_p;
X.pois_p = pv{j};
XX{j} = X;
end
pp.delete
idx = ~cellfun(@isempty, XX);
%concat mutation structs and p-value list
L = concat_structs(M(idx));
P = cat(1, pv{:});
%save raw results
for i = find(idx)',
X = XX{i};
save(sprintf('pois_reg/output_v1/%d-%s.mat', F.ch96(i), F.context{i}), 'X')
end
%add FDR values to mutation struct
L.q_unif = fdr_jh(L.p_unif, sum(n_extra));
save('pois_reg/output_v1/loci_pvalues.mat', 'L', 'P')
% }}}
%
% Uniform-within-gene {{{
clear
%load territories
load('ref/gene_list.align75_filtered.territories.mat', 'G')
%lookup tables for ch96 -> c32 x 3
tmp = dec2base(0:95, 4) - 48;
c96_32map = [tmp(:, 1:2)*[4 1]' > 2 tmp(:, 3:4)]*[16 4 1]' + 1;
c96_bc = mod(ceil((1:96)/16) - 1, 3)' + 1;
%total number of trimers in exome
N32 = sum(G.terr32);
N96 = N32(c96_32map);
%load mutations
M = loadM('mutation_data/MC3.align75.ICE_PoN.M');
%overall rate of each base change
r96 = histc(M.mut.ch96, 1:96)./N96';
r96_ch = full(sparse(c96_32map, c96_bc, r96));
M.gene.gidx2 = listmap(M.gene.name, G.gene);
M.mut.gidx2 = M.gene.gidx2(M.mut.gene_idx);
%compute Fg's
G.terr96 = G.terr32(:, c96_32map);
G.Fg = accumarray(M.mut.gidx2, 1)./sum(bsxfun(@times, G.terr96, r96'), 2);
save('ref/gene_list.align75_filtered.territories_UWG-Fg.mat', 'G')
%load uniqued mutations
M = loadM('mutation_data/MC3.align75.ICE_PoN-uniqued.M');
M.gene.gidx2 = listmap(M.gene.name, G.gene);
M.mut.gidx2 = M.gene.gidx2(M.mut.gene_idx);
%compute Poisson p-values and individual probabilities
M.mut.p = NaN(slength(M.mut), 3);
M.mut.prob = NaN(slength(M.mut), 3);
%probabilities for each gene having zero mutations each context/change
G.prob0 = NaN(slength(G), 32, 3);
%counts of nonmutated positions in each gene, stratified by context/change
G.n0 = NaN(size(G.prob0));
n_extra = NaN(32, 1);
for i = 1:32,
idx = M.mut.c32 == i;
terr = N32(i);
n_extra(i) = 3*(terr - nnz(idx));
for j = 1:3,
ct = M.mut.count_nb(idx, j);
lams = sum(ct)/terr*G.Fg(M.mut.gidx2(idx));
M.mut.p(idx, j) = poisscdf(ct, lams, 'upper') + poisspdf(ct, lams);
M.mut.prob(idx, j) = poisspdf(ct, lams);
%also save prob for sites in gene with zero mutations
lams0 = sum(ct)/terr*G.Fg;
G.prob0(:, i, j) = poisspdf(0, lams0);
G.n0(:, i, j) = accumarray(M.mut.gidx2(idx), M.mut.count_nb(idx, j) > 0, [slength(G) 1]);
end
end
G.n0 = bsxfun(@plus, -G.n0, G.terr32);
save('ref/gene_list.align75_filtered.territories_UWG-Fg_zerocounts-and-probs.mat', 'G')
M.mut.q = reshape(fdr_jh(M.mut.p(:), sum(n_extra)), [], 3);
%
%convert to L struct format
C = load_struct('ref/1536_LuT.txt');
C = makeapn(C);
C = sparse(C.c512, C.nbidx, C.ch1536);
Mus = cell(3, 1);
for i = 1:3,
idx = M.mut.count_nb(:, i) > 0;
Mus{i} = reorder_struct(M.mut, idx);
Mus{i}.count = Mus{i}.count_nb(:, i);
Mus{i}.p = Mus{i}.p(:, i);
Mus{i}.prob = Mus{i}.prob(:, i);
Mus{i}.q = Mus{i}.q(:, i);
Mus{i}.ch1536 = full(C(sub2ind(size(C), Mus{i}.c512, i*ones(slength(Mus{i}), 1))));
end
L = concat_structs(Mus);
save('MC3.align75.ICE_PoN.UWG.results.mat', 'L')
% }}}
%
% 4. Gamma-Poisson (negative binomial) {{{
%
%run wrapper to extract covariates {{{
clear
c1024 = dec2base(0:1023, 4) - 48;
c1024 = [(1:1024)' sum(bsxfun(@times, 3 - c1024(:, [1 3 2 5 4]), [4^4 4^3 4^2 4 1]), 2) + 1];
P = [];
P.annotate_and_exit = 1;
P.pool_basechanges = 0;
P.tier = 1:6;
P.outdir = 'nb/covars';
P.covar_dir = {'ref/cod_dnasehs_covars.zerofilled.align75_filtered/v3' ...
'ref/cod_nuc_covars.align75_filtered/Gm12878' ...
'ref/cod_mutsig_transformed_covars.align75_filtered/exprmax_transformed' ...
'ref/cod_mutsig_transformed_covars.align75_filtered/rt_transformed'};
ede(P.outdir)
pp = parpool(16);
parfor i = 1:512,
for j = 1:3,
pois_LN_reg_wrapper('mutation_data/MC3.align75.ICE_PoN-uniqued.M', c1024(i, :), 'c1025', j, P)
end
end
pp.delete
%}}}
%
%run regressions {{{
clear
%we can pull covariates from LNP hierarchical runs
F = [];
F.file = direc('LNP_posteriors/pancancer/output/*.mat');
F = parsein(F, 'file', '.*output/(\d+)-(.*)_3.mat', {'ch96' 'context'});
F.ch96 = str2double(F.ch96);
F = sort_struct(F, 'ch96');
ch1536lut = load_struct('ref/1536_LuT.txt');
ch1536lut = makeapn(ch1536lut);
ch1536lut = sparse(ch1536lut.c512, ch1536lut.nbidx, ch1536lut.ch1536);
Beta = NaN(slength(F), 20);
alpha = NaN(slength(F), 1);
logL = NaN(slength(F), 1);
M = cell(slength(F), 1);
pv = cell(slength(F), 1);
XX = cell(slength(F), 1);
n_extra = NaN(slength(F), 1);
pp = parpool(16);
parfor j = 1:slength(F),
X = load(F.file{j});
n_extra(j) = nnz(X.M == 0);
if max(X.M) == 1, continue; end
C = [X.C(:, 1:4) full(sparse(1:length(X.C), X.C(:, 5), 1))];
r = nbreg(C, X.M);
Beta(j, :) = r.b;
alpha(j) = r.alpha;
logL(j) = r.logL;
%compute p-values
nb_p = nbinpdf(X.M, 1/alpha(j), 1./(1 + alpha(j)*exp(C*Beta(j, :)')));
pv{j} = nb_p + nbincdf(X.M, 1/alpha(j), 1./(1 + alpha(j)*exp(C*Beta(j, :)')), 'upper');
X.Mu.p_nb = pv{j}(X.M > 0);
X.Mu.prob_nb = nb_p(X.M > 0);
%add newbase information
nb = mod(ceil(F.ch96(j)/16) - 1, 3) + 1;
X.Mu.ch1536 = full(ch1536lut(X.Mu.c512, nb));
M{j} = X.Mu;
X.nb_prob = nb_p;
X.nb_p = pv{j};
XX{j} = X;
end
pp.delete
idx = ~cellfun(@isempty, XX);
%concat mutation structs and p-value list
L = concat_structs(M(idx));
P = cat(1, pv{:});
%save raw results
for i = find(idx)',
X = XX{i};
save(sprintf('nb/output_v2/%d-%s.mat', F.ch96(i), F.context{i}), 'X')
end
%add FDR values to mutation struct
L.q_nb = fdr_jh(L.p_nb, sum(n_extra));
save('nb/output_v2/loci_pvalues.mat', 'L', 'P')
%}}}
%}}}