public void RemoveConflictSpectrum(List <IIdentifiedSpectrum> conflicted)
{
if (conflicted.Count > 0)
{
IFalseDiscoveryRateCalculator calc = Options.Parent.FalseDiscoveryRate.GetFalseDiscoveryRateCalculator();
var map = OptimalResults.ToDictionary(m => m.Condition);
var bin = Options.Parent.Classification.BuildSpectrumBin(conflicted);
foreach (var oi in bin)
{
var oldOi = map[oi.Condition];
foreach (var s in oi.Spectra)
{
if (s.FromDecoy)
{
oldOi.Result.PeptideCountFromDecoyDB--;
}
else
{
oldOi.Result.PeptideCountFromTargetDB--;
}
oldOi.Spectra.Remove(s);
}
oldOi.Result.FalseDiscoveryRate = calc.Calculate(oldOi.Result.PeptideCountFromDecoyDB, oldOi.Result.PeptideCountFromTargetDB);
}
}
}
/// <summary>
/// 获取筛选后的谱图列表。
/// </summary>
/// <returns></returns>
public List <IIdentifiedSpectrum> GetOptimalSpectra()
{
List <IIdentifiedSpectrum> result = new List <IIdentifiedSpectrum>();
OptimalResults.ForEach(m => result.AddRange(m.Spectra));
return(result);
}
public void CalculateToleranceScore()
{
var scoreFunc = this.Options.ScoreFunction;
OptimalResults.ForEach(m => m.CalculateToleranceScore(scoreFunc));
}
public void KeepOptimalResultInSetOnly(HashSet <IIdentifiedSpectrum> spectra)
{
OptimalResults.ForEach(m => m.KeepOptimalResultInSetOnly(spectra));
CalculateToleranceScore();
}
