private void EstimateQScores(ISampleMap <CanvasSegment> canvasSegments, ISampleMap <SampleMetrics> pedigreeMembersInfo,
PedigreeInfo pedigreeInfo, ISampleMap <Dictionary <Genotype, double> > singleSampleLikelihoods, JointLikelihoods copyNumberLikelihoods, ISampleMap <Genotype> copyNumbers)
{
foreach (var sampleId in canvasSegments.SampleIds)
{
canvasSegments[sampleId].QScore = GetSingleSampleQualityScore(singleSampleLikelihoods[sampleId], copyNumbers[sampleId]);
canvasSegments[sampleId].CopyNumber = copyNumbers[sampleId].TotalCopyNumber;
if (canvasSegments[sampleId].QScore < _qualityFilterThreshold)
{
canvasSegments[sampleId].Filter = CanvasFilter.Create(new[] { $"q{_qualityFilterThreshold}" });
}
}
if (pedigreeInfo.HasFullPedigree())
{
SetDenovoQualityScores(canvasSegments, pedigreeMembersInfo, pedigreeInfo.ParentsIds, pedigreeInfo.OffspringIds, copyNumberLikelihoods);
}
}
/// <summary>
/// Identify variant with the highest likelihood at a given setPosition and assign relevant scores
/// </summary>
public void CallVariant(ISampleMap <CanvasSegment> canvasSegments, ISampleMap <SampleMetrics> samplesInfo,
ISampleMap <ICopyNumberModel> copyNumberModel, PedigreeInfo pedigreeInfo)
{
var singleSampleLikelihoods = _copyNumberLikelihoodCalculator.GetCopyNumbersLikelihoods(canvasSegments, samplesInfo, copyNumberModel, _callerParameters.NumberOfTrimmedBins);
(var pedigreeCopyNumbers, var pedigreeLikelihoods) = GetPedigreeCopyNumbers(pedigreeInfo, singleSampleLikelihoods);
var nonPedigreeCopyNumbers = CanvasPedigreeCaller.GetNonPedigreeCopyNumbers(canvasSegments, pedigreeInfo, singleSampleLikelihoods);
var mergedCopyNumbers = pedigreeCopyNumbers.Concat(nonPedigreeCopyNumbers).OrderBy(canvasSegments.SampleIds);
EstimateQScores(canvasSegments, samplesInfo, pedigreeInfo, singleSampleLikelihoods, pedigreeLikelihoods, mergedCopyNumbers);
// TODO: this will be integrated with GetCopyNumbers* on a model level as a part of https://jira.illumina.com/browse/CANV-404
if (CanvasPedigreeCaller.UseAlleleCountsInformation(canvasSegments, _callerParameters.MinAlleleCountsThreshold, _callerParameters.MinAlleleNumberInSegment) &&
pedigreeInfo.HasFullPedigree())
{
AssignMccWithPedigreeInfo(canvasSegments, copyNumberModel, pedigreeInfo);
}
if (CanvasPedigreeCaller.UseAlleleCountsInformation(canvasSegments, _callerParameters.MinAlleleCountsThreshold, _callerParameters.MinAlleleNumberInSegment) &&
pedigreeInfo.HasOther())
{
AssignMccNoPedigreeInfo(canvasSegments.Where(segment => pedigreeInfo.OtherIds.Contains(segment.SampleId)).ToSampleMap(), copyNumberModel, _genotypes);
}
}
public void CallVariant(ISampleMap <CanvasSegment> canvasSegments, ISampleMap <SampleMetrics> samplesInfo,
ISampleMap <ICopyNumberModel> copyNumberModel, PedigreeInfo pedigreeInfo)
{
var coverageLikelihoods = _copyNumberLikelihoodCalculator.GetCopyNumbersLikelihoods(canvasSegments, samplesInfo, copyNumberModel);
// if number and properties of SNPs in the segment are above threshold, calculate likelihood from SNPs and merge with
// coverage likelihood to form merged likelihoods
int nBalleles = canvasSegments.Values.First().Balleles.Size();
// If allele information is available (i.e. segment has enough SNPs) merge coverage and allele likelihood obtained by GetGenotypeLogLikelihoods
// into singleSampleLikelihoods using JoinLikelihoods function.
var singleSampleLikelihoods = CanvasPedigreeCaller.UseAlleleCountsInformation(canvasSegments,
_callerParameters.MinAlleleCountsThreshold, _callerParameters.MinAlleleNumberInSegment)
? JoinLikelihoods(GetGenotypeLogLikelihoods(canvasSegments, copyNumberModel, _PhasedGenotypes), coverageLikelihoods, nBalleles)
: ConvertToLogLikelihood(coverageLikelihoods);
// estimate joint likelihood across pedigree samples from singleSampleLikelihoods using either only coverage or coverage + allele counts
(var pedigreeCopyNumbers, var pedigreeLikelihoods) = GetPedigreeCopyNumbers(pedigreeInfo, singleSampleLikelihoods);
var nonPedigreeCopyNumbers = CanvasPedigreeCaller.GetNonPedigreeCopyNumbers(canvasSegments, pedigreeInfo, singleSampleLikelihoods);
var mergedCopyNumbers = nonPedigreeCopyNumbers.Concat(pedigreeCopyNumbers).OrderBy(canvasSegments.SampleIds);
AssignCNandScores(canvasSegments, samplesInfo, pedigreeInfo, singleSampleLikelihoods,
pedigreeLikelihoods, mergedCopyNumbers);
}
internal int CallVariants(List <string> variantFrequencyFiles, List <string> segmentFiles,
IFileLocation outVcfFile, string ploidyBedPath, string referenceFolder, List <string> sampleNames, string commonCnvsBedPath, List <SampleType> sampleTypes)
{
// load files
// initialize data structures and classes
var fileCounter = 0;
var samplesInfo = new SampleMap <SampleMetrics>();
var sampleSegments = new SampleMap <Segments>();
var copyNumberModels = new SampleMap <ICopyNumberModel>();
var variantFrequencyFilesSampleList = new SampleMap <string>();
var kinships = new SampleMap <SampleType>();
foreach (string sampleName in sampleNames)
{
var sampleId = new SampleId(sampleName);
var segment = Segments.ReadSegments(_logger, new FileLocation(segmentFiles[fileCounter]));
segment.AddAlleles(CanvasIO.ReadFrequenciesWrapper(_logger, new FileLocation(variantFrequencyFiles[fileCounter]), segment.IntervalsByChromosome));
sampleSegments.Add(sampleId, segment);
var sampleInfo = SampleMetrics.GetSampleInfo(segment.AllSegments, ploidyBedPath, _callerParameters.NumberOfTrimmedBins, sampleId);
var copyNumberModel = _copyNumberModelFactory.CreateModel(_callerParameters.MaximumCopyNumber, sampleInfo.MaxCoverage, sampleInfo.MeanCoverage, sampleInfo.MeanMafCoverage);
samplesInfo.Add(sampleId, sampleInfo);
copyNumberModels.Add(sampleId, copyNumberModel);
variantFrequencyFilesSampleList.Add(sampleId, variantFrequencyFiles[fileCounter]);
kinships.Add(sampleId, sampleTypes[fileCounter]);
fileCounter++;
}
var segmentSetsFromCommonCnvs = CreateSegmentSetsFromCommonCnvs(variantFrequencyFilesSampleList,
_callerParameters.MinAlleleCountsThreshold, commonCnvsBedPath, sampleSegments);
var segmentsForVariantCalling = GetHighestLikelihoodSegments(segmentSetsFromCommonCnvs, samplesInfo, copyNumberModels).ToList();
PedigreeInfo pedigreeInfo = PedigreeInfo.GetPedigreeInfo(kinships, _callerParameters);
Parallel.ForEach(
segmentsForVariantCalling,
new ParallelOptions
{
MaxDegreeOfParallelism = Math.Min(Environment.ProcessorCount, _callerParameters.MaxCoreNumber)
},
segments => _variantCaller.CallVariant(segments, samplesInfo, copyNumberModels, pedigreeInfo)
);
var variantCalledSegments = new SampleMap <List <CanvasSegment> >();
foreach (var key in samplesInfo.SampleIds)
{
variantCalledSegments.Add(key, segmentsForVariantCalling.Select(segment => segment[key]).ToList());
}
var mergedVariantCalledSegments = MergeSegments(variantCalledSegments, _callerParameters.MinimumCallSize, _qualityFilterThreshold);
FilterExcessivelyShortSegments(mergedVariantCalledSegments);
var outputFolder = outVcfFile.Directory;
foreach (var sampleId in samplesInfo.SampleIds)
{
var coverageOutputPath = SingleSampleCallset.GetCoverageAndVariantFrequencyOutput(outputFolder,
sampleId.ToString());
CanvasSegment.WriteCoveragePlotData(mergedVariantCalledSegments[sampleId], samplesInfo[sampleId].MeanCoverage,
samplesInfo[sampleId].Ploidy, coverageOutputPath, referenceFolder);
}
bool isPedigreeInfoSupplied = pedigreeInfo != null && pedigreeInfo.HasFullPedigree();
var denovoQualityThreshold = isPedigreeInfoSupplied ? (int?)_deNovoQualityFilterThreshold : null;
var ploidies = samplesInfo.Select(info => info.Value.Ploidy).ToList();
var diploidCoverage = samplesInfo.Select(info => info.Value.MeanCoverage).ToList();
var names = samplesInfo.SampleIds.Select(id => id.ToString()).ToList();
CanvasSegmentWriter.WriteMultiSampleSegments(outVcfFile.FullName, mergedVariantCalledSegments, diploidCoverage, referenceFolder, names,
null, ploidies, _qualityFilterThreshold, denovoQualityThreshold, CanvasFilter.SegmentSizeCutoff, isPedigreeInfoSupplied);
foreach (var sampleId in samplesInfo.SampleIds)
{
var outputVcfPath = SingleSampleCallset.GetVcfOutput(outputFolder, sampleId.ToString());
var sampleMetrics = samplesInfo[sampleId];
var segments = mergedVariantCalledSegments[sampleId];
CanvasSegmentWriter.WriteSegments(outputVcfPath.FullName, segments,
sampleMetrics.MeanCoverage, referenceFolder, sampleId.ToString(), null,
sampleMetrics.Ploidy, _qualityFilterThreshold, isPedigreeInfoSupplied, denovoQualityThreshold, null);
var visualizationTemp = outputFolder.CreateSubdirectory($"VisualizationTemp{sampleId}");
var normalizationFactor = NormalizationCalculator.ComputeNormalizationFactor(segments);
var bigWig = _coverageBigWigWriter.Write(segments, visualizationTemp, normalizationFactor);
bigWig?.MoveTo(SingleSampleCallset.GetCoverageBigWig(outputFolder, sampleId.ToString()));
var copyNumberBedGraph = SingleSampleCallset.GetCopyNumberBedGraph(outputFolder, sampleId.ToString());
_copyNumberBedGraphWriter.Write(segments, sampleMetrics.Ploidy, copyNumberBedGraph);
var partitionBedgraphHeader = "track type=bedGraph visibility=full autoScale=on graphType=points";
var originalSegments = sampleSegments[sampleId];
_partitionCoverageBedGraphWriter.Write(originalSegments.AllSegments, SingleSampleCallset.GetPartitionBedGraph(outputFolder, sampleId.ToString()), normalizationFactor, partitionBedgraphHeader);
}
return(0);
}
public static SampleMap <Genotype> GetNonPedigreeCopyNumbers(ISampleMap <CanvasSegment> canvasSegments, PedigreeInfo pedigreeInfo,
ISampleMap <Dictionary <Genotype, double> > singleSampleCopyNumberLogLikelihoods)
{
bool IsOther(SampleId sampleId) => pedigreeInfo.OtherIds.Contains(sampleId);
var nonPedigreeMemberSegments = canvasSegments.WhereSampleIds(IsOther);
var nonPedigreeMemberLikelihoods = singleSampleCopyNumberLogLikelihoods.WhereSampleIds(IsOther);
(var nonPedigreeMemberCopyNumbers, _) = GetCopyNumbersNoPedigreeInfo(nonPedigreeMemberSegments, nonPedigreeMemberLikelihoods);
return(nonPedigreeMemberCopyNumbers);
}
/// <summary>
/// Estimate joint likelihood and most likely CN assignment within a pedigree using total CN Genotype likelihoods and transition matrix
/// </summary>
/// <param name="pedigreeInfo"></param>
/// <param name="copyNumbersLikelihoods"></param>
/// <returns></returns>
private (ISampleMap <Genotype> copyNumbersGenotypes, JointLikelihoods jointLikelihood) GetPedigreeCopyNumbers(PedigreeInfo pedigreeInfo, ISampleMap <Dictionary <Genotype, double> > copyNumbersLikelihoods)
{
int nHighestLikelihoodGenotypes = pedigreeInfo != null && pedigreeInfo.OffspringIds.Count >= 2 ? 3 : _callerParameters.MaximumCopyNumber;
copyNumbersLikelihoods = copyNumbersLikelihoods.SelectValues(l => l.OrderByDescending(kvp => kvp.Value).Take(nHighestLikelihoodGenotypes).ToDictionary());
var sampleCopyNumbersGenotypes = new SampleMap <Genotype>();
var jointLikelihood = new JointLikelihoods();
if (!pedigreeInfo.HasFullPedigree())
{
return(sampleCopyNumbersGenotypes, jointLikelihood);
}
// parent 1 total CNs and likelihoods
foreach (var copyNumberParent1 in copyNumbersLikelihoods[pedigreeInfo.ParentsIds.First()])
{
// parent 2 total CNs and likelihoods
foreach (var copyNumberParent2 in copyNumbersLikelihoods[pedigreeInfo.ParentsIds.Last()])
{
// for offspring in addition to querying likelihoods using total CNs, iterate over all possible genotype combination (CopyNumberA/B) for a given
// CN and estimate likely transition probabilities using TransitionMatrix
foreach (var offspringGtStates in pedigreeInfo.OffspringPhasedGenotypes)
{
if (!pedigreeInfo.OffspringIds.All(id => copyNumbersLikelihoods[id].ContainsKey(
Genotype.Create(Math.Min(offspringGtStates[pedigreeInfo.OffspringIds.IndexOf(id)].PhasedGenotype.CopyNumberA + offspringGtStates[pedigreeInfo.OffspringIds.IndexOf(id)].PhasedGenotype.CopyNumberB,
_callerParameters.MaximumCopyNumber - 1)))))
{
// unavailable total CN
continue;
}
// For a given combination of offspring copy numbers, only the genotypes that result in the maximum likelihood contribute to the final result."
double currentLikelihood = copyNumberParent1.Value * copyNumberParent2.Value;
var totalCopyNumberGenotypes = new List <Genotype>();
for (var counter = 0; counter < pedigreeInfo.OffspringIds.Count; counter++)
{
var child = pedigreeInfo.OffspringIds[counter];
var copyNumberGenotypeChild = Genotype.Create(Math.Min(offspringGtStates[counter].PhasedGenotype.CopyNumberA + offspringGtStates[counter].PhasedGenotype.CopyNumberB,
_callerParameters.MaximumCopyNumber - 1));
totalCopyNumberGenotypes.Add(copyNumberGenotypeChild);
currentLikelihood *= pedigreeInfo.TransitionMatrix[copyNumberParent1.Key.TotalCopyNumber][offspringGtStates[counter].PhasedGenotype.CopyNumberA] *
pedigreeInfo.TransitionMatrix[copyNumberParent2.Key.TotalCopyNumber][offspringGtStates[counter].PhasedGenotype.CopyNumberB] *
copyNumbersLikelihoods[child][copyNumberGenotypeChild];
}
currentLikelihood = Double.IsNaN(currentLikelihood) || Double.IsInfinity(currentLikelihood) ? 0 : currentLikelihood;
var genotypesInPedigree = new SampleMap <Genotype>
{
{ pedigreeInfo.ParentsIds.First(), copyNumberParent1.Key },
{ pedigreeInfo.ParentsIds.Last(), copyNumberParent2.Key }
};
pedigreeInfo.OffspringIds.Zip(totalCopyNumberGenotypes).ForEach(sampleIdGenotypeKvp => genotypesInPedigree.Add(sampleIdGenotypeKvp.Item1, sampleIdGenotypeKvp.Item2));
genotypesInPedigree = genotypesInPedigree.OrderBy(pedigreeInfo.AllSampleIds);
jointLikelihood.AddJointLikelihood(genotypesInPedigree, currentLikelihood);
double currentLogLikelihood = Math.Log(currentLikelihood);
if (currentLogLikelihood > jointLikelihood.MaximalLogLikelihood)
{
jointLikelihood.MaximalLogLikelihood = currentLogLikelihood;
sampleCopyNumbersGenotypes = genotypesInPedigree;
}
}
}
}
if (sampleCopyNumbersGenotypes.Empty())
{
throw new IlluminaException("Maximal likelihood was not found");
}
return(sampleCopyNumbersGenotypes, jointLikelihood);
}
/// <summary>
/// Calculates maximal likelihood for genotypes given a copy number call. Updated MajorChromosomeCount.
/// </summary>
private void AssignMccWithPedigreeInfo(ISampleMap <CanvasSegment> canvasSegments, ISampleMap <ICopyNumberModel> model, PedigreeInfo pedigreeInfo)
{
double maximalLogLikelihood = Double.NegativeInfinity;
int parent1CopyNumber = canvasSegments[pedigreeInfo.ParentsIds.First()].CopyNumber;
int parent2CopyNumber = canvasSegments[pedigreeInfo.ParentsIds.Last()].CopyNumber;
foreach (var parent1GtStates in _genotypes[parent1CopyNumber])
{
foreach (var parent2GtStates in _genotypes[parent2CopyNumber])
{
var bestChildGtStates = new List <PhasedGenotype>();
double currentLogLikelihood = 0;
foreach (SampleId child in pedigreeInfo.OffspringIds)
{
int childCopyNumber = canvasSegments[child].CopyNumber;
bool isInheritedCnv = !canvasSegments[child].DqScore.HasValue;
double bestLogLikelihood = Double.NegativeInfinity;
PhasedGenotype bestGtState = null;
bestLogLikelihood = GetProbandLogLikelihood(model[child], childCopyNumber,
parent1GtStates, parent2GtStates, isInheritedCnv, canvasSegments[child], bestLogLikelihood, ref bestGtState);
bestChildGtStates.Add(bestGtState);
currentLogLikelihood += bestLogLikelihood;
}
currentLogLikelihood += GetCurrentGtLogLikelihood(model[pedigreeInfo.ParentsIds.First()], canvasSegments[pedigreeInfo.ParentsIds.First()], parent1GtStates) +
GetCurrentGtLogLikelihood(model[pedigreeInfo.ParentsIds.Last()], canvasSegments[pedigreeInfo.ParentsIds.Last()], parent2GtStates);
currentLogLikelihood = Double.IsNaN(currentLogLikelihood) || Double.IsInfinity(currentLogLikelihood)
? Double.NegativeInfinity
: currentLogLikelihood;
if (currentLogLikelihood > maximalLogLikelihood)
{
maximalLogLikelihood = currentLogLikelihood;
AssignMcc(canvasSegments[pedigreeInfo.ParentsIds.First()], model[pedigreeInfo.ParentsIds.First()], parent1GtStates, parent1CopyNumber);
AssignMcc(canvasSegments[pedigreeInfo.ParentsIds.Last()], model[pedigreeInfo.ParentsIds.Last()], parent2GtStates, parent2CopyNumber);
for (int childIndex = 0; childIndex < pedigreeInfo.OffspringIds.Count; childIndex++)
{
var childId = pedigreeInfo.OffspringIds[childIndex];
var bestChildGtState = bestChildGtStates[childIndex];
if (bestChildGtState == null)
{
continue;
}
var childSegment = canvasSegments[childId];
AssignMcc(childSegment, model[childId], bestChildGtState, childSegment.CopyNumber);
}
}
}
}
}
