static public bool?HasHla(Hla hlaGoal, Dictionary <string, string> row, HlaResolution hlaResolution)
{
bool hasNull = false;
for (int i = 1; i <= 2; ++i)
{
string column = hlaGoal.ToString().Substring(0, 1) + i.ToString();
HlaToLength hlaToLengthOrNull = HlaAssignmentParams.GetHlaToLengthValueOrNull(row, column, hlaResolution);
if (hlaToLengthOrNull == null || hlaToLengthOrNull.HlaNumberToLength >= 9000)
{
hasNull = true;
continue;
}
if (hlaGoal.ToString() == hlaToLengthOrNull.ToString())
{
return(true);
}
}
if (hasNull)
{
return(null);
}
else
{
return(false);
}
}
public static void BioQuickTestInternal(Quickscore <Hla, int> quickscore, string fileName, string header)
{
List <int> patientList = quickscore.EffectList();
List <QmrJob <Hla, int> > jobList = new List <QmrJob <Hla, int> >();
foreach (List <Dictionary <string, string> > tableByPeptide in HlaAssignmentParams.QuickScoreOptimalsGroupByPeptide(fileName, header))
{
Debug.Assert(tableByPeptide.Count > 0); // real assert
string peptide = tableByPeptide[0]["peptide"];
List <int> patientsWhoDoNotRespond;
List <int> patientsWhoRespond;
QmrAlgorithms.FindPatientsWhoRespondAndWhoDoNot(patientList, tableByPeptide, out patientsWhoRespond, out patientsWhoDoNotRespond);
QmrJob <Hla, int> aQuickScoreJob = QmrJob <Hla, int> .GetInstance(peptide, patientsWhoRespond, patientsWhoDoNotRespond, quickscore);
jobList.Add(aQuickScoreJob);
}
jobList.Sort(delegate(QmrJob <Hla, int> x, QmrJob <Hla, int> y) { return(x.PresentEffectCollection.Count.CompareTo(y.PresentEffectCollection.Count)); });
foreach (QmrJob <Hla, int> job in jobList)
{
Debug.WriteLine(job);
}
foreach (QmrJob <Hla, int> job in jobList)
{
//if (job.Name != "RIRTWKSLVK")
//{
// continue;
//}
Console.WriteLine(job);
//job.Quickscore.Probability("B58", job.PresentEffectCollection, job.AbsentEffectCollection);
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
Dictionary <Hla, double> posteriorOfEveryCause = job.PosteriorOfEveryCause();
stopwatch.Stop();
Console.WriteLine("{0}\t{1}\t{2}\t{3}", job.Name, job.PresentEffectCollection.Count, job.AbsentEffectCollection.Count, stopwatch.Elapsed);
foreach (KeyValuePair <Hla, double> causeAndPosterior in posteriorOfEveryCause)
{
Debug.Assert(0 <= causeAndPosterior.Value && causeAndPosterior.Value <= 1);
Console.WriteLine("{0}\t{1}\t{2}\t{3}\t{4}\t{5}", job.Name, job.PresentEffectCollection.Count, job.AbsentEffectCollection.Count, stopwatch.Elapsed
, causeAndPosterior.Key, causeAndPosterior.Value);
}
}
}
public static void FindPatientsWhoRespondAndWhoDoNot(IList <int> patientList, List <Dictionary <string, string> > tableByPeptide, out List <int> patientsWhoRespond, out List <int> patientsWhoDoNotRespond)
{
Dictionary <int, bool> patientsWhoRespondDictionary = new Dictionary <int, bool>();
foreach (Dictionary <string, string> row in tableByPeptide)
{
int patient = HlaAssignmentParams.GetPatient(row);
patientsWhoRespondDictionary[patient] = true;
}
patientsWhoDoNotRespond = new List <int>();
foreach (int patient in patientList)
{
if (!patientsWhoRespondDictionary.ContainsKey(patient))
{
patientsWhoDoNotRespond.Add(patient);
}
}
patientsWhoRespond = new List <int>(patientsWhoRespondDictionary.Keys);
}
static public IEnumerable <Hla> FindAllHla(List <Dictionary <string, string> > expandedTable, HlaResolution hlaResolution, string header)
{
Qmrr.HlaFactory hlaFactory = Qmrr.HlaFactory.GetFactory("noConstraint");
Dictionary <Hla, bool> seenIt = new Dictionary <Hla, bool>();
foreach (Dictionary <string, string> row in expandedTable)
{
foreach (string column in HlaAssignmentParams.CreateHlaColumns(header))
{
HlaToLength hlaToLengthOrNull = HlaAssignmentParams.GetHlaToLengthValueOrNull(row, column, hlaResolution);
if (hlaToLengthOrNull == null || hlaToLengthOrNull.HlaNumberToLength >= 9000)
{
continue;
}
Hla hla = hlaFactory.GetGroundInstance(hlaToLengthOrNull.ToString());
if (!seenIt.ContainsKey(hla))
{
seenIt.Add(hla, true);
yield return(hla);
}
}
}
}
public static void SearchHlaAssignmentsRelaxation(string fileName, string header, string solutionFileName, string solutionHeader, double causePrior, double linkProbability, double leakProbability, int howManyBest, string outputFilename, HlaResolution hlaResolution)
{
Debug.Assert(Math.Exp(double.NegativeInfinity) == 0);
//Create the structure of patients and their HLAs
Quickscore <Hla, int> quickscore = HlaAssignmentParams.CreateQuickscore(fileName, header, causePrior, linkProbability, leakProbability, hlaResolution);
// Get the list of patients, Hlas, and patient weights
List <int> patientList = quickscore.EffectList();
IEnumerable <Hla> hlaList = quickscore.CauseList();
//The structure file can contain a "weight" column. If it does, weight the patients, otherwise give them all weight 1.0
Dictionary <int, double> patientWeightTable = HlaAssignmentParams.CreatePatientWeightTable(fileName, header);
//The cause assignment table is a mapping from
// peptide to HLA assignments for that peptide
Dictionary <string, List <Dictionary <string, string> > > causeAssignmentTable = CreateCauseAssignmentTable(solutionFileName, solutionHeader);
//We start the report
using (StreamWriter streamwriterOutputFile = File.CreateText(outputFilename))
{
//The report is the same as the input "solution" except that we now list new HLA, a note, and a p(assignment)
streamwriterOutputFile.WriteLine(SpecialFunctions.CreateTabString(solutionHeader,
"newHLA", "rank", "p(assignment)", "note", "Peptide", "LogLikelihood", "TrueHlas.Count", "TrueHlas", "TrueHlasAndRespondingPatients", "UnexplainedPatients.Count"));
//Now we go through the model file, one peptide at a time. For each peptide, we know the patientID (did) of
// every patient who responded. (It also tells the HLAs of the patient, but we already have that info in the structure)
/*
* peptide did a1 a2 b1 b2 c1 c2
*
* ACQGVGGPGHK 10 2 11 38 44 12 16
* ACQGVGGPGHK 14 2 24 1517 58 3 7
* ACQGVGGPGHK 41 11 33 35 40 6 7
* ACQGVGGPGHK 102 2 11 35 44 4 5
*
* AENLWVTVY 25 24 66 35 39 4 12
* AENLWVTVY 36 23 32 8 44 7 7
* AENLWVTVY 45 3 31 39 44 5 12
* AENLWVTVY 46 2 29 41 52 16 16
* AENLWVTVY 59 2 33 7 44 3 15
*
* [...]
*/
foreach (List <Dictionary <string, string> > tableByPeptide in HlaAssignmentParams.QuickScoreOptimalsGroupByPeptide(fileName, header))
{
Debug.Assert(tableByPeptide.Count > 0); // real assert
string peptide = tableByPeptide[0]["peptide"];
//Create lists of patients who responded and assume everyone else didn't.
List <int> patientsWhoRespond;
List <int> patientsWhoDoNotRespond;
FindPatientsWhoRespondAndWhoDoNot(patientList, tableByPeptide, out patientsWhoRespond, out patientsWhoDoNotRespond);
//If we get a peptide with no row in the solution file, skip it.
if (!causeAssignmentTable.ContainsKey(peptide))
{
streamwriterOutputFile.WriteLine("{0}\t\t{1}", peptide, "explained by noise");
continue;
}
//We assign every HLA mentioned by the solution file with this peptide to TRUE and all others to FALSE
//An assignment of TRUE means that this HLA is a cause of this response.
List <Dictionary <string, string> > rowsOfThisPeptide = causeAssignmentTable[peptide];
HlaAssignmentWithResponses hlaAssignmentBase = CreateHlaAssignment(rowsOfThisPeptide, hlaList, hlaResolution, quickscore, patientsWhoRespond);
//Find the likelihood of this structure, with these patient responses, and the solution's HLA assignment.
double baseLogLikelihood = quickscore.LogLikelihoodOfModelWithCompleteAssignments(patientsWhoRespond, patientsWhoDoNotRespond, hlaAssignmentBase.AsDictionary, patientWeightTable);
//Consider each HLA assigned to TRUE (for this peptide) by the solution
foreach (Dictionary <string, string> row in rowsOfThisPeptide)
{
Hla hla = GetHlaFromRow(row, hlaResolution);
//If we already really know that this HLA is a cause of this reponse, just report that.
if (IsKnown(row))
{
streamwriterOutputFile.WriteLine("{0}\t{1}\t{2}", row[""], hla, "known");
continue;
}
//Set it to false as a way to see measure the probability that it is true.
HlaAssignmentWithResponses hlaAssignmentL0 = CreateHlaAssignment(rowsOfThisPeptide, hlaList, hlaResolution, quickscore, patientsWhoRespond, hla, null);
double logL0 = quickscore.LogLikelihoodOfModelWithCompleteAssignments(patientsWhoRespond, patientsWhoDoNotRespond, hlaAssignmentL0.AsDictionary, patientWeightTable);
Debug.Assert(hlaAssignmentL0.TrueCount + 1 == hlaAssignmentBase.TrueCount); // real assert
string noteNote = "";
if (logL0 > baseLogLikelihood)
{
noteNote = string.Format("\tlogL0 > baseLogLikelihood ({0}>{1})", logL0, baseLogLikelihood);
}
double probability = Math.Exp(baseLogLikelihood - SpecialFunctions.LogSum(baseLogLikelihood, logL0));
streamwriterOutputFile.WriteLine(SpecialFunctions.CreateTabString(
row[""], hla, "1 best", probability, noteNote,
peptide, baseLogLikelihood, hlaAssignmentBase.TrueCount, hlaAssignmentBase.TrueToString(), hlaAssignmentBase.TrueToListString(), hlaAssignmentBase.UnexplainedPatients.Count));
streamwriterOutputFile.WriteLine(SpecialFunctions.CreateTabString(
row[""], hla, "remove 1 best", "", noteNote,
peptide, logL0, hlaAssignmentL0.TrueCount, hlaAssignmentL0.TrueToString(), hlaAssignmentL0.TrueToListString(), hlaAssignmentL0.UnexplainedPatients.Count));
//Also, while we're in the world were it is false, let's measure how well each of the currently-set-to-false HLAs would do instead.
//!!!should this be one dictionary to a new class instead of three dictionaries?
List <KeyValuePair <Hla, double> > listOfhla1AndProbability1 = new List <KeyValuePair <Hla, double> >();
Dictionary <Hla, double> loglikelihoodCollection = new Dictionary <Hla, double>();
Dictionary <Hla, HlaAssignmentWithResponses> hla1ToHlaAssignment = new Dictionary <Hla, HlaAssignmentWithResponses>();
foreach (Hla hla1 in hlaList)
{
if (hla1 == hla || hlaAssignmentBase.AsDictionary[hla1])
{
continue;
}
HlaAssignmentWithResponses hlaAssignmentL1 = CreateHlaAssignment(rowsOfThisPeptide, hlaList, hlaResolution, quickscore, patientsWhoRespond, hla, hla1);
if (hlaAssignmentL1.TrueCount == hlaAssignmentBase.TrueCount)
{
double logL1 = quickscore.LogLikelihoodOfModelWithCompleteAssignments(patientsWhoRespond, patientsWhoDoNotRespond, hlaAssignmentL1.AsDictionary, patientWeightTable);
double probability1 = Math.Exp(logL1 - SpecialFunctions.LogSum(logL1, logL0));
loglikelihoodCollection[hla1] = logL1;
hla1ToHlaAssignment.Add(hla1, hlaAssignmentL1);
listOfhla1AndProbability1.Add(new KeyValuePair <Hla, double>(hla1, probability1));
}
}
//Report on the best of these
listOfhla1AndProbability1.Sort(delegate(KeyValuePair <Hla, double> x, KeyValuePair <Hla, double> y) { return(y.Value.CompareTo(x.Value)); });
for (int i = 0; i < howManyBest - 1; ++i)
{
Hla hla1 = listOfhla1AndProbability1[i].Key;
streamwriterOutputFile.WriteLine(SpecialFunctions.CreateTabString(
row[""], hla1, string.Format("{0} best", i + 2), listOfhla1AndProbability1[i].Value,
"", peptide, loglikelihoodCollection[hla1], hla1ToHlaAssignment[hla1].TrueCount, hla1ToHlaAssignment[hla1].TrueToString(), hla1ToHlaAssignment[hla1].TrueToListString(), hla1ToHlaAssignment[hla1].UnexplainedPatients.Count));
}
}
}
}
}
public static HlaAssignmentParams GetInstance(string nameForNumbers, string prefix, int limit, bool doPlus, bool doOutput)
{
HlaAssignmentParams aHlaAssignmentParams = new HlaAssignmentParams();
aHlaAssignmentParams.Limit = limit;
aHlaAssignmentParams.DoPlus = doPlus;
aHlaAssignmentParams.FileName = string.Format(@"{0}-model.txt", prefix);
//aHlaAssignmentParams.Header = @"peptide did a1 a2 b1 b2 c1 c2";
aHlaAssignmentParams.OutputFileOrNull = doOutput ? string.Format(@"exhaustivePlus{0}-abinitio.{1}.{2}.new.txt", prefix, limit, nameForNumbers) : null; //!!!const
//string solutionFileName = string.Format(@"{0}-solution-abinitio-leak0.003.txt", prefix);
//string solutionHeader = @"peptide HLA p(assignment) isKnown knownHLAs";
if (nameForNumbers == "")
{
if (prefix == "HIVOptimals")
{
aHlaAssignmentParams.CausePrior = 0.011498;
aHlaAssignmentParams.LinkProbability = 0.48795;
aHlaAssignmentParams.LeakProbability = 0.051818;
}
else
{
SpecialFunctions.CheckCondition(prefix == "EBVOptimals");
aHlaAssignmentParams.CausePrior = 0.0068892;
aHlaAssignmentParams.LinkProbability = 0.4597;
aHlaAssignmentParams.LeakProbability = 0.042766;
}
}
//else if (nameForNumbers == "1in300")
//{
// if (prefix == "HIVOptimals")
// {
// linkProbability = 0.30816;
// causePrior = 0.040854;
// leakProbability = 0.0033333;
// }
// else
// {
// CheckCondition(prefix == "EBVOptimals");
// linkProbability = 0.30056;
// causePrior = 0.028322;
// leakProbability = 0.0033333;
// }
//}
else if (nameForNumbers == "leak0")
{
if (prefix == "HIVOptimals")
{
aHlaAssignmentParams.CausePrior = 0.041551;
aHlaAssignmentParams.LinkProbability = 0.33478;
aHlaAssignmentParams.LeakProbability = 1.0 / 300.0;
}
else
{
SpecialFunctions.CheckCondition(prefix == "EBVOptimals");
aHlaAssignmentParams.CausePrior = 0.028628;
aHlaAssignmentParams.LinkProbability = 0.29874;
aHlaAssignmentParams.LeakProbability = 1.0 / 300.0;
}
}
else
{
aHlaAssignmentParams.CausePrior = double.NaN;
aHlaAssignmentParams.LinkProbability = double.NaN;
aHlaAssignmentParams.LeakProbability = double.NaN;
}
aHlaAssignmentParams.HlaResolution = HlaResolution.ABMixed;
return(aHlaAssignmentParams);
}
