protected static void AlignMatches(SpectralAnalysis analysis, ISpectralAnalysisWriter writer)
{
var netXvalues = new List <double>();
var netYvalues = new List <double>();
var massXvalues = new List <double>();
var massYvalues = new List <double>();
var matches =
analysis.Matches.OrderBy(x => x.AnchorPointX.Net);
// 1. Find the best matches
// 2. Find only matches that have been made once.
var bestMatches = new Dictionary <int, SpectralAnchorPointMatch>();
foreach (var match in matches)
{
var scan = match.AnchorPointX.Scan;
if (bestMatches.ContainsKey(scan))
{
if (bestMatches[scan].SimilarityScore < match.SimilarityScore)
{
bestMatches[scan] = match;
}
}
else
{
bestMatches.Add(scan, match);
}
}
// 2. Find only those matched once
var all = new Dictionary <int, SpectralAnchorPointMatch>();
foreach (var match in bestMatches.Values)
{
var scan = match.AnchorPointY.Scan;
if (all.ContainsKey(scan))
{
if (all[scan].SimilarityScore < match.SimilarityScore)
{
all[scan] = match;
}
}
else
{
all.Add(scan, match);
}
}
// Write the analysis
writer.Write(analysis);
// Then generate the NET Alignment using R1
var anchorPoints =
all.Values.OrderBy(x => x.AnchorPointX.Net).ToList();
foreach (var match in anchorPoints)
{
netXvalues.Add(match.AnchorPointX.Net);
netYvalues.Add(match.AnchorPointY.Net);
}
Func <double, double, double> netFunc = (x, y) => x - y;
Func <double, double, double> massFunc = FeatureLight.ComputeMassPPMDifference;
InterpolateDimension("NET-R1", writer, netXvalues, netYvalues, anchorPoints, netFunc);
// Then generate the Mass Alignment using R1
// We also have to resort the matches based on mass now too
anchorPoints = all.Values.OrderBy(x => x.AnchorPointX.Mz).ToList();
foreach (var match in anchorPoints)
{
massXvalues.Add(match.AnchorPointX.Mz);
massYvalues.Add(match.AnchorPointY.Mz);
}
InterpolateDimension("Mass-R1", writer, massXvalues, massYvalues, anchorPoints, massFunc);
}
protected static void AlignMatches(SpectralAnalysis analysis, ISpectralAnalysisWriter writer)
{
var netXvalues = new List<double>();
var netYvalues = new List<double>();
var massXvalues = new List<double>();
var massYvalues = new List<double>();
var matches =
analysis.Matches.OrderBy(x => x.AnchorPointX.Net);
// 1. Find the best matches
// 2. Find only matches that have been made once.
var bestMatches = new Dictionary<int, SpectralAnchorPointMatch>();
foreach (var match in matches)
{
var scan = match.AnchorPointX.Scan;
if (bestMatches.ContainsKey(scan))
{
if (bestMatches[scan].SimilarityScore < match.SimilarityScore)
{
bestMatches[scan] = match;
}
}
else
{
bestMatches.Add(scan, match);
}
}
// 2. Find only those matched once
var all = new Dictionary<int, SpectralAnchorPointMatch>();
foreach (var match in bestMatches.Values)
{
var scan = match.AnchorPointY.Scan;
if (all.ContainsKey(scan))
{
if (all[scan].SimilarityScore < match.SimilarityScore)
{
all[scan] = match;
}
}
else
{
all.Add(scan, match);
}
}
// Write the analysis
writer.Write(analysis);
// Then generate the NET Alignment using R1
var anchorPoints =
all.Values.OrderBy(x => x.AnchorPointX.Net).ToList();
foreach (var match in anchorPoints)
{
netXvalues.Add(match.AnchorPointX.Net);
netYvalues.Add(match.AnchorPointY.Net);
}
Func<double, double, double> netFunc = (x, y) => x - y;
Func<double, double, double> massFunc = FeatureLight.ComputeMassPPMDifference;
InterpolateDimension("NET-R1", writer, netXvalues, netYvalues, anchorPoints, netFunc);
// Then generate the Mass Alignment using R1
// We also have to resort the matches based on mass now too
anchorPoints = all.Values.OrderBy(x => x.AnchorPointX.Mz).ToList();
foreach (var match in anchorPoints)
{
massXvalues.Add(match.AnchorPointX.Mz);
massYvalues.Add(match.AnchorPointY.Mz);
}
InterpolateDimension("Mass-R1", writer, massXvalues, massYvalues, anchorPoints, massFunc);
}
