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BranchSites_MNMSimulations_null.bf
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outfile_path = "knownGene.uc001hmo.1.1.testHyPhyBS.null.out";
filepath = "knownGene.uc001hmo.1.1.testHyPhyBS";
LoadFunctionLibrary("../../res/TemplateBatchFiles/TemplateModels/chooseGeneticCode.def", {"0" : "Universal"});
DataSet ds = ReadDataFile (filepath);
DataSetFilter filteredData = CreateFilter (ds,3,"","","TAA,TAG,TGA");
coding_path = LAST_FILE_PATH; /*save the last file path if you wish*/
outfile_path = filepath + ".null.includingdelta.out";
treeString = DATAFILE_TREE;
Tree givenTree = treeString;
COUNT_GAPS_IN_FREQUENCIES = 0; /*dont want gaps/Ns to contribute to codon frequency calculations*/
HarvestFrequencies (baseFreqs,filteredData,3,1,1);
modelKind = 0;
global P_0;
P_0:<1;
P_0:>0;
global P_1_aux;
global P_1 := Min(P_1_aux,1-P_0);
P_1:<1;
P_1:>0;
rateClasses = 4;
categFreqMatrix = {{P_0,P_1,(1-P_0-P_1)/(P_0+P_1)*P_0,(1-P_0-P_1)/(P_0+P_1)*P_1}} ;
categRateMatrix = {{1,2,3,4}};
category site_kind = (rateClasses, categFreqMatrix , MEAN, ,categRateMatrix, 1, 4);
global kappa_inv;
global delta;
ModelMatrixDimension = 64 - (+ _Genetic_Code["_MATRIX_ELEMENT_VALUE_==10"]);
GY_Matrix = {ModelMatrixDimension,ModelMatrixDimension};
hshift = 0;
for (h=0; h<64; h=h+1)
{
if (_Genetic_Code[h]==10)
{
hshift = hshift+1;
}
else
{
vshift = hshift;
for (v = h+1; v<64; v=v+1)
{
first1 = v$16;
second1 = v%16$4;
third1 = v%4;
first2 = h$16;
second2 = h%16$4;
third2 = h%4;
diff = v-h;
if (_Genetic_Code[v]==10)
{
vshift = vshift+1;
}
else
{
if ((h$4==v$4)||((diff%4==0)&&(h$16==v$16))||(diff%16==0) )
{
if (h$4==v$4)
{
transition = v%4;
transition2= h%4;
}
else
{
if(diff%16==0)
{
transition = v$16;
transition2= h$16;
}
else
{
transition = v%16$4;
transition2= h%16$4;
}
}
if (_Genetic_Code[0][h]==_Genetic_Code[0][v])
{
if (Abs(transition-transition2)%2)
{
GY_Matrix[h-hshift][v-vshift] := kappa_inv*synRate;
GY_Matrix[v-vshift][h-hshift] := kappa_inv*synRate;
}
else
{
GY_Matrix[h-hshift][v-vshift] := synRate;
GY_Matrix[v-vshift][h-hshift] := synRate;
}
}
else
{
if (Abs(transition-transition2)%2)
{
GY_Matrix[h-hshift][v-vshift] := kappa_inv*nonSynRate;
GY_Matrix[v-vshift][h-hshift] := kappa_inv*nonSynRate;
}
else
{
GY_Matrix[h-hshift][v-vshift] := nonSynRate;
GY_Matrix[v-vshift][h-hshift] := nonSynRate;
}
}
}
else
{
if ( (first1!=first2) && (second1 != second2) && (third1 == third2))
{
transition_pos1 = v$16;
transition2_pos1 = h$16;
transition_pos2 = v%16$4;
transition2_pos2 = h%16$4;
if (_Genetic_Code[0][h]==_Genetic_Code[0][v])
{
if ( ((Abs(transition_pos1-transition2_pos1)%2) && (Abs(transition_pos2-transition2_pos2)%2)) || ((((transition_pos1-transition2_pos1)%2) == 0) && (((transition_pos2-transition2_pos2)%2) == 0)) )
{
if ( (Abs(transition_pos1-transition2_pos1)%2) && (Abs(transition_pos2-transition2_pos2)%2) )
{
GY_Matrix[h-hshift][v-vshift] := synRate*delta*kappa_inv*kappa_inv;
GY_Matrix[v-vshift][h-hshift] := synRate*delta*kappa_inv*kappa_inv;
}
else
{
GY_Matrix[h-hshift][v-vshift] := synRate*delta;
GY_Matrix[v-vshift][h-hshift] := synRate*delta;
}
}
else
{
GY_Matrix[h-hshift][v-vshift] := synRate*delta*kappa_inv;
GY_Matrix[v-vshift][h-hshift] := synRate*delta*kappa_inv;
}
}
else
{
if ( ((Abs(transition_pos1-transition2_pos1)%2) && (Abs(transition_pos2-transition2_pos2)%2)) || (((transition_pos1-transition2_pos1)%2 == 0) && ((transition_pos2-transition2_pos2)%2 == 0)) )
{
if( (Abs(transition_pos1-transition2_pos1)%2) && (Abs(transition_pos2-transition2_pos2)%2))
{
GY_Matrix[h-hshift][v-vshift] := nonSynRate*delta*kappa_inv*kappa_inv;
GY_Matrix[v-vshift][h-hshift] := nonSynRate*delta*kappa_inv*kappa_inv;
}
else
{
GY_Matrix[h-hshift][v-vshift] := nonSynRate*delta;
GY_Matrix[v-vshift][h-hshift] := nonSynRate*delta;
}
}
else
{
GY_Matrix[h-hshift][v-vshift] := nonSynRate*delta*kappa_inv;
GY_Matrix[v-vshift][h-hshift] := nonSynRate*delta*kappa_inv;
}
}
}
if ( (first1 == first2) && (second1 != second2) && (third1 != third2))
{
transition_pos3 = v%4;
transition2_pos3 = h%4;
transition_pos2 = v%16$4;
transition2_pos2 = h%16$4;
if (_Genetic_Code[0][h]==_Genetic_Code[0][v])
{
if ( ((Abs(transition_pos3-transition2_pos3)%2) && (Abs(transition_pos2-transition2_pos2)%2)) || (((transition_pos3-transition2_pos3)%2 == 0) && ((transition_pos2-transition2_pos2)%2 == 0)) )
{
if( (Abs(transition_pos3-transition2_pos3)%2) && (Abs(transition_pos2-transition2_pos2)%2) )
{
GY_Matrix[h-hshift][v-vshift] := synRate*delta*kappa_inv*kappa_inv;
GY_Matrix[v-vshift][h-hshift] := synRate*delta*kappa_inv*kappa_inv;
}
else
{
GY_Matrix[h-hshift][v-vshift] := synRate*delta;
GY_Matrix[v-vshift][h-hshift] := synRate*delta;
}
}
else
{
GY_Matrix[h-hshift][v-vshift] := synRate*delta*kappa_inv;
GY_Matrix[v-vshift][h-hshift] := synRate*delta*kappa_inv;
}
}
else
{
if ( ((Abs(transition_pos3-transition2_pos3)%2) && (Abs(transition_pos2-transition2_pos2)%2)) || (((transition_pos3-transition2_pos3)%2 == 0) && ((transition_pos2-transition2_pos2)%2 == 0)) )
{
if( (Abs(transition_pos3-transition2_pos3)%2) && (Abs(transition_pos2-transition2_pos2)%2) )
{
GY_Matrix[h-hshift][v-vshift] := nonSynRate*delta*kappa_inv*kappa_inv;
GY_Matrix[v-vshift][h-hshift] := nonSynRate*delta*kappa_inv*kappa_inv;
}
else
{
GY_Matrix[h-hshift][v-vshift] := nonSynRate*delta;
GY_Matrix[v-vshift][h-hshift] := nonSynRate*delta;
}
}
else
{
GY_Matrix[h-hshift][v-vshift] := nonSynRate*delta*kappa_inv;
GY_Matrix[v-vshift][h-hshift] := nonSynRate*delta*kappa_inv;
}
}
}
if ( (first1 != first2) && (second1 == second2) && (third1 != third2))
{
transition_pos1 = v$16;
transition2_pos1 = h$16;
transition_pos3 = v%4;
transition2_pos3 = h%4;
if (_Genetic_Code[0][h]==_Genetic_Code[0][v])
{
if ( ((Abs(transition_pos1-transition2_pos1)%2) && (Abs(transition_pos3-transition2_pos3)%2)) || (((transition_pos1-transition2_pos1)%2 == 0) && ((transition_pos3-transition2_pos3)%2 == 0)) )
{
if( (Abs(transition_pos1-transition2_pos1)%2) && (Abs(transition_pos3-transition2_pos3)%2) )
{
GY_Matrix[h-hshift][v-vshift] := synRate*delta*kappa_inv*kappa_inv;
GY_Matrix[v-vshift][h-hshift] := synRate*delta*kappa_inv*kappa_inv;
}
else
{
GY_Matrix[h-hshift][v-vshift] := synRate*delta;
GY_Matrix[v-vshift][h-hshift] := synRate*delta;
}
}
else
{
GY_Matrix[h-hshift][v-vshift] := synRate*delta*kappa_inv;
GY_Matrix[v-vshift][h-hshift] := synRate*delta*kappa_inv;
}
}
else
{
if ( ((Abs(transition_pos1-transition2_pos1)%2) && (Abs(transition_pos3-transition2_pos3)%2)) || ( ((transition_pos1-transition2_pos1)%2 == 0) && ( (transition_pos3-transition2_pos3)%2 == 0)) )
{
if((Abs(transition_pos1-transition2_pos1)%2) && (Abs(transition_pos3-transition2_pos3)%2) )
{
GY_Matrix[h-hshift][v-vshift] := nonSynRate*delta*kappa_inv*kappa_inv;
GY_Matrix[v-vshift][h-hshift] := nonSynRate*delta*kappa_inv*kappa_inv;
}
else
{
GY_Matrix[h-hshift][v-vshift] := nonSynRate*delta;
GY_Matrix[v-vshift][h-hshift] := nonSynRate*delta;
}
}
else
{
GY_Matrix[h-hshift][v-vshift] := nonSynRate*delta*kappa_inv;
GY_Matrix[v-vshift][h-hshift] := nonSynRate*delta*kappa_inv;
}
}
}
}
}
}
}
}
PIStop = 1.0;
codonFreqs = {ModelMatrixDimension,1};
hshift = 0;
for (h=0; h<64; h=h+1)
{
first = h$16;
second = h%16$4;
third = h%4;
if (_Genetic_Code[h]==10)
{
hshift = hshift+1;
PIStop = PIStop-baseFreqs[first][0]*baseFreqs[second][1]*baseFreqs[third][2];
continue;
}
codonFreqs[h-hshift]=baseFreqs[first][0]*baseFreqs[second][1]*baseFreqs[third][2];
}
codonFreqs = codonFreqs*(1.0/PIStop);
Model GY_Model = (GY_Matrix,codonFreqs);
Tree givenTree = treeString;
LikelihoodFunction test_lf = (filteredData, givenTree);
USE_LAST_RESULTS = 0; /*no values to re-use, start fresh for optimization*/
OPTIMIZATION_METHOD = 4; /*the only robust optimization methods are 0 and 4*/
HKY85_Matrix = {{*,t*kappa_inv,t,t*kappa_inv}
{t*kappa_inv,*,kappa_inv*t,t}
{t,t*kappa_inv,*,kappa_inv*t}
{t*kappa_inv,t,kappa_inv*t,*}};
HarvestFrequencies (nucFreqs,ds,1,1,1);
Model HKY85_Model = (HKY85_Matrix,nucFreqs);
Tree nucTree = treeString;
DataSetFilter nucData = CreateFilter (ds,1);
LikelihoodFunction nuc_lf = (nucData,nucTree);
LIKELIHOOD_FUNCTION_OUTPUT = 5;
Optimize(nuc_mle,nuc_lf);
USE_LAST_RESULTS = 1;
LIKELIHOOD_FUNCTION_OUTPUT = 3;
global omega_0;
omega_0 :< 1;
ClearConstraints (givenTree);
global omega_FG := ((site_kind==1)*omega_0+(site_kind==2)+(site_kind>2));
global omega_BG := (((site_kind==1)+(site_kind==3))*omega_0+(site_kind==2)+(site_kind==4));
ExecuteCommands ("givenTree."+"hg18"+".nonSynRate:=omega_FG*givenTree."+"hg18"+".synRate;");
ReplicateConstraint ("this1.?.nonSynRate:=omega_BG*this2.?.synRate",givenTree,givenTree);/*handily impose constraints for all branches*/
bNames = BranchName (givenTree,-1);
nucBL = BranchLength (nucTree,-1);
for (bc=0; bc<Columns(bNames)-1; bc=bc+1)
{
ExecuteCommands ("givenTree."+bNames[bc]+".synRate=nucTree."+bNames[bc]+".t;");
}
codBL = BranchLength (givenTree,-1);
for (bc=0; bc<Columns(bNames)-1; bc=bc+1)
{
if (nucBL[bc]>0)
{
codBL[bc]=0;
ExecuteCommands ("givenTree."+bNames[bc]+".synRate=nucTree."+bNames[bc]+".t*"+nucBL[bc]/codBL[bc]+";");
}
}
OPTIMIZATION_PRECISION = 0.001;
LikelihoodFunction lf = (filteredData, givenTree);
while (1)
{
Optimize (mles,lf);
LIKELIHOOD_FUNCTION_OUTPUT = 5;
GetString (lfParameters, lf, -1);
glV = lfParameters["Local Independent"];
stashedValues = {};
for (glVI = 0; glVI < Columns (glV); glVI = glVI + 1)
{
ExecuteCommands ("stashedValues[\""+glV[glVI]+"\"] = " + glV[glVI] + ";\n");
}
glV = lfParameters["Global Independent"];
for (glVI = 0; glVI < Columns (glV); glVI = glVI + 1)
{
ExecuteCommands ("stashedValues[\""+glV[glVI]+"\"] = " + glV[glVI] + ";\n");
}
mlBL = BranchLength (givenTree,-1);
samples = 500;
fprintf (outfile_path, "\nChecking for convergence by Latin Hypercube Sampling (this may take a bit of time...)\n");
steps = 50;
vn = {{"P_0","P_1_aux","omega_0", "omega_2"}};
ranges = {{0.0001,1}{0.0001,1}{0.0001,1}{1,10}};
LFCompute (lf,LF_START_COMPUTE);
for (sample = 0; sample < samples; sample = sample + 1)
{
rv = Random({1,steps}["_MATRIX_ELEMENT_COLUMN_"],0);
for (vid = 0; vid < Columns (vn); vid = vid + 1)
{
ctx = vn[vid] + "=" + (ranges[vid][0] + (ranges[vid][1]-ranges[vid][0])/steps*rv[vid]);
ExecuteCommands (ctx);
}
currentBL = BranchLength (givenTree,-1);
for (bc=0; bc<Columns(bNames)-1; bc=bc+1)
{
if (currentBL[bc]>0)
{
ExecuteCommands ("givenTree."+bNames[bc]+".synRate=givenTree."+bNames[bc]+".synRate*"+mlBL[bc]/currentBL[bc]+";");
}
}
LFCompute (lf,sample_value);
if (sample_value>mles[1][0])
{
fprintf (outfile_path, "\nFound a better likelihood score. Restarting the optimization routine.\n");
break;
}
}
LFCompute (lf,LF_DONE_COMPUTE);
if (sample < samples)
{
continue;
}
storedV = Rows (stashedValues);
for (k=0; k<Columns (storedV); k=k+1)
{
ExecuteCommands (storedV[k] + "=" + stashedValues[storedV[k]]);
}
fprintf (outfile_path, "\nThe estimation procedure appears to have converged.\n");
break;
}
LIKELIHOOD_FUNCTION_OUTPUT = 5;
fprintf (outfile_path, lf);
fprintf(outfile_path, "mles:", mles);
for (simCounter = 1; simCounter<51; simCounter = simCounter+1)
{
sim_outfile_path = filepath + ".null.SimSeq." + simCounter;
/*Note: change the following line to test on alignment of any length, instead of 5000*/
DataSet simulatedData = Simulate (givenTree,codonFreqs,characters,5000);
DataSetFilter filtered_simulatedData = CreateFilter (simulatedData,3,"","","TAA,TAG,TGA");
DATA_FILE_PRINT_FORMAT = VALUE;
fprintf(sim_outfile_path, filtered_simulatedData);
fprintf(sim_outfile_path, simulatedTree);
}
fprintf (outfile_path, "\nInferred rate distribution:",
"\n\tClass 0. omega_0 = ", Format (omega_0, 5,3), " weight = ", Format (P_0,5,3),
"\n\tClass 1. omega := ", Format (1, 5,3), " weight = ", Format (P_1,5,3),
"\n\tClass 2a. Background omega_0 = ", Format (omega_0, 5,3), " foreground omega_2 = ", Format (1, 5,3), " weight = ", Format (P_0(1-P_0-P_1)/(P_0+P_1),5,3),
"\n\tClass 2b. Background omega := ", Format (1, 5,3), " foreground omega_2 = ", Format (1, 5,3), " weight = ", Format (P_1(1-P_0-P_1)/(P_0+P_1),5,3), "\n"
);