/// <summary>
/// Aligns features to the baseline correcting for drift time.
/// </summary>
/// <param name="fullObservedEnumerable">All features.</param>
/// <param name="observedEnumerable">Filtered features to use for drift time correction.</param>
/// <param name="targetEnumerable">Expected features that should be filtered.</param>
/// <param name="massTolerance">PPM Mass Tolerance.</param>
/// <param name="netTolerance">Normalized Elution Time Tolerance.</param>
public static DriftTimeAlignmentResults <TTarget, TObserved> AlignObservedEnumerable(IEnumerable <UMCLight> fullObservedEnumerable, IEnumerable <TTarget> observedEnumerable, IEnumerable <TObserved> targetEnumerable, double massTolerance, double netTolerance)
{
// Setup Tolerance for Feature Matching
var featureMatcherParameters = new FeatureMatcherParameters();
featureMatcherParameters.SetTolerances(massTolerance, netTolerance, DRIFT_TIME_TOLERANCE);
featureMatcherParameters.UseDriftTime = true;
// Find all matches based on defined tolerances
var featureMatcher = new FeatureMatcher <TTarget, TObserved>(observedEnumerable.ToList(), targetEnumerable.ToList(), featureMatcherParameters);
var matchList = featureMatcher.FindMatches(observedEnumerable.ToList(), targetEnumerable.ToList(), featureMatcherParameters.UserTolerances, 0);
// Create <ObservedDriftTime, TargetDriftTime> XYData List
var xyDataList = new List <XYData>();
foreach (var featureMatch in matchList)
{
var xyData = new XYData(featureMatch.ObservedFeature.DriftTime, featureMatch.TargetFeature.DriftTime);
xyDataList.Add(xyData);
}
var linearRegression = new LinearRegressionModel();
// Find the Linear Equation for the <ObservedDriftTime, TargetDriftTime> XYData List
var linearEquation = linearRegression.CalculateRegression(xyDataList);
// Set the Aligned Drift Time value for each of the observed Features, even if they were not found in matching
foreach (var observedT in fullObservedEnumerable)
{
observedT.DriftTimeAligned = linearRegression.Transform(linearEquation, observedT.DriftTime);
}
var results = new DriftTimeAlignmentResults <TTarget, TObserved>(matchList, linearEquation);
return(results);
}
/// <summary>
/// Does a zero mean drift time correction.
/// </summary>
/// <param name="observedEnumerable">All observed features to shift that should already be drift time aligned.</param>
/// <param name="targetEnumerable">Expected features</param>
/// <param name="massTolerance">PPM Mass Tolerance</param>
/// <param name="netTolerance">Normalized Elution Time tolerance.</param>
/// <param name="driftTimeTolerance">Drift time tolerance to use.</param>
public static DriftTimeAlignmentResults <TTarget, TObserved> CorrectForOffset(IEnumerable <TTarget> observedEnumerable, IEnumerable <TObserved> targetEnumerable, double massTolerance, double netTolerance, double driftTimeTolerance)
{
// Setup Tolerance for Feature Matching
var featureMatcherParameters = new FeatureMatcherParameters();
featureMatcherParameters.SetTolerances(massTolerance, netTolerance, (float)driftTimeTolerance);
featureMatcherParameters.UseDriftTime = true;
// Find all matches based on defined tolerances
var featureMatcher = new FeatureMatcher <TTarget, TObserved>(observedEnumerable.ToList(), targetEnumerable.ToList(), featureMatcherParameters);
var matchList = featureMatcher.FindMatches(observedEnumerable.ToList(), targetEnumerable.ToList(), featureMatcherParameters.UserTolerances, 0);
// Create List of Drift Time differences
var differenceList = new List <double>(matchList.Count);
foreach (var featureMatch in matchList)
{
var observedFeature = featureMatch.ObservedFeature;
var targetFeature = featureMatch.TargetFeature;
double observedDriftTime;
if (observedFeature.DriftTimeAligned != double.NaN && observedFeature.DriftTimeAligned > 0.0)
{
observedDriftTime = observedFeature.DriftTimeAligned;
}
else
{
observedDriftTime = observedFeature.DriftTime;
}
double targetDriftTime;
if (!double.IsNaN(targetFeature.DriftTimeAligned) && targetFeature.DriftTimeAligned > 0.0)
{
targetDriftTime = targetFeature.DriftTimeAligned;
}
else
{
targetDriftTime = targetFeature.DriftTime;
}
differenceList.Add(observedDriftTime - targetDriftTime);
}
// Create bins for histogram
var bins = new List <double>();
for (var i = -driftTimeTolerance; i <= driftTimeTolerance; i += (driftTimeTolerance / 100.0))
{
bins.Add(i);
}
bins.Add(driftTimeTolerance);
// Group drift time differences into the bins
var groupings = differenceList.GroupBy(difference => bins.First(bin => bin >= difference));
// Order the groupings by their count, so the group with the highest count will be first
var orderGroupingsByCount = from singleGroup in groupings
orderby singleGroup.Count() descending
select singleGroup;
// Grab the drift time from the group with the most counts
var driftTimeOffset = orderGroupingsByCount.First().Key;
// Update all of the observed features with the new drift time
foreach (var observedFeature in observedEnumerable)
{
if (!double.IsNaN(observedFeature.DriftTimeAligned) && observedFeature.DriftTimeAligned > 0.0)
{
observedFeature.DriftTimeAligned -= driftTimeOffset;
}
else
{
observedFeature.DriftTime -= (float)driftTimeOffset;
}
}
var linearEquation = new LinearRegressionResult {
Slope = 0, Intercept = driftTimeOffset
};
var results = new DriftTimeAlignmentResults <TTarget, TObserved>(matchList, linearEquation);
return(results);
}