//this "Grouping" function performs the grouping.
private List<ResultsGroup> Groupings(String filename, ParametersForm.ParameterSettings modelParameters, Double Mas, List<CompositionHypothesisEntry> comhyp)
{
GetDeconData DeconDATA1 = new GetDeconData();
List<string> elementIDs = new List<string>();
List<string> molename = new List<string>();
for (int i = 0; i < comhyp.Count(); i++ )
{
if (comhyp[i].ElementNames.Count > 0)
{
for (int j = 0; j < comhyp[i].ElementNames.Count(); j++)
{
elementIDs.Add(comhyp[i].ElementNames[j]);
}
for (int j = 0; j < comhyp[i].MoleculeNames.Count(); j++)
{
molename.Add(comhyp[i].MoleculeNames[j]);
}
break;
}
}
List<DeconRow> sortedDeconData = new List<DeconRow>();;
sortedDeconData = DeconDATA1.getdata(filename);
//First, sort the list descendingly by its abundance.
sortedDeconData = sortedDeconData.OrderByDescending(a => a.abundance).ToList();
//################Second, create a new list to store results from the first grouping.###############
List<ResultsGroup> fgResults = new List<ResultsGroup>();
ResultsGroup GR2 = new ResultsGroup();
Int32 currentMaxBin = new Int32();
currentMaxBin = 1;
GR2.DeconRow = sortedDeconData[0];
GR2.MostAbundant = true;
GR2.NumOfScan = 1;
GR2.MinScanNum = sortedDeconData[0].ScanNum;
GR2.MaxScanNum = sortedDeconData[0].ScanNum;
GR2.ChargeStateList = new List<int>();
GR2.ChargeStateList.Add(sortedDeconData[0].charge);
GR2.AvgSigNoiseList = new List<Double>();
GR2.AvgSigNoiseList.Add(sortedDeconData[0].SignalNoiseRatio);
GR2.AvgAA2List = new List<double>();
GR2.AvgAA2List.Add(sortedDeconData[0].MonoisotopicAbundance / (sortedDeconData[0].MonoisotopicPlus2Abundance + 1));
GR2.ScanNumList = new List<Int32>();
GR2.ScanNumList.Add(sortedDeconData[0].ScanNum);
GR2.NumModiStates = 1;
GR2.TotalVolume = sortedDeconData[0].abundance * sortedDeconData[0].fwhm;
GR2.ListAbundance = new List<double>();
GR2.ListAbundance.Add(sortedDeconData[0].abundance);
GR2.ListMonoMassWeight = new List<double>();
GR2.ListMonoMassWeight.Add(sortedDeconData[0].MonoisotopicMassWeight);
fgResults.Add(GR2);
for (int j = 1; j < sortedDeconData.Count; j++)
{
for (int i = 0; i < fgResults.Count; i++)
{
//Obtain grouping error. Note: its in ppm, so it needs to be multiplied by 0.000001.
Double GroupingError = fgResults[i].DeconRow.MonoisotopicMassWeight * modelParameters.GroupingErrorEG * 0.000001;
if ((sortedDeconData[j].MonoisotopicMassWeight < (fgResults[i].DeconRow.MonoisotopicMassWeight + GroupingError) && (sortedDeconData[j].MonoisotopicMassWeight > (fgResults[i].DeconRow.MonoisotopicMassWeight - GroupingError))))
{
if (fgResults[i].MaxScanNum < sortedDeconData[j].ScanNum)
{
fgResults[i].MaxScanNum = sortedDeconData[j].ScanNum;
}
else if (fgResults[i].MinScanNum > sortedDeconData[j].ScanNum)
{
fgResults[i].MinScanNum = sortedDeconData[j].ScanNum;
}
fgResults[i].NumOfScan = fgResults[i].NumOfScan + 1;
fgResults[i].ScanNumList.Add(sortedDeconData[j].ScanNum);
fgResults[i].TotalVolume = fgResults[i].TotalVolume + sortedDeconData[j].abundance * sortedDeconData[j].fwhm;
fgResults[i].ChargeStateList.Add(sortedDeconData[j].charge);
fgResults[i].AvgSigNoiseList.Add(sortedDeconData[j].SignalNoiseRatio);
fgResults[i].AvgAA2List.Add(sortedDeconData[j].MonoisotopicAbundance / (sortedDeconData[j].MonoisotopicPlus2Abundance + 1));
fgResults[i].ListAbundance.Add(sortedDeconData[j].abundance);
fgResults[i].ListMonoMassWeight.Add(sortedDeconData[j].MonoisotopicMassWeight);
break;
}
if (i == fgResults.Count - 1)
{
ResultsGroup GR = new ResultsGroup();
currentMaxBin = currentMaxBin + 1;
GR.DeconRow = sortedDeconData[j];
GR.MostAbundant = true;
GR.NumOfScan = 1;
GR.MinScanNum = sortedDeconData[j].ScanNum;
GR.MaxScanNum = sortedDeconData[j].ScanNum;
GR.ChargeStateList = new List<int>();
GR.ChargeStateList.Add(sortedDeconData[j].charge);
GR.AvgSigNoiseList = new List<Double>();
GR.AvgSigNoiseList.Add(sortedDeconData[j].SignalNoiseRatio);
GR.AvgAA2List = new List<double>();
GR.AvgAA2List.Add(sortedDeconData[j].MonoisotopicAbundance / (sortedDeconData[j].MonoisotopicPlus2Abundance + 1));
GR.ScanNumList = new List<int>();
GR.ScanNumList.Add(sortedDeconData[j].ScanNum);
GR.NumModiStates = 1;
GR.TotalVolume = sortedDeconData[j].abundance * sortedDeconData[j].fwhm;
GR.ListAbundance = new List<double>();
GR.ListAbundance.Add(sortedDeconData[j].abundance);
GR.ListMonoMassWeight = new List<double>();
GR.ListMonoMassWeight.Add(sortedDeconData[j].MonoisotopicMassWeight);
fgResults.Add(GR);
}
}
}
//Lastly calculate the Average Weighted Abundance
for (int y = 0; y < fgResults.Count(); y++)
{
Double sumofTopPart = 0;
for (int z = 0; z < fgResults[y].ListMonoMassWeight.Count(); z++)
{
sumofTopPart = sumofTopPart + fgResults[y].ListMonoMassWeight[z] * fgResults[y].ListAbundance[z];
}
fgResults[y].DeconRow.MonoisotopicMassWeight = sumofTopPart / fgResults[y].ListAbundance.Sum();
}
//######################## Here is the second grouping. ################################
fgResults = fgResults.OrderBy(o => o.DeconRow.MonoisotopicMassWeight).ToList();
if (Mas != 0)
{
for (int i = 0; i < fgResults.Count - 1; i++)
{
if (fgResults[i].MostAbundant == true)
{
int numModStates = 1;
for (int j = i + 1; j < fgResults.Count; j++)
{
Double AdductTolerance = fgResults[i].DeconRow.MonoisotopicMassWeight * modelParameters.AdductToleranceEA * 0.000001;
if ((fgResults[i].DeconRow.MonoisotopicMassWeight >= (fgResults[j].DeconRow.MonoisotopicMassWeight - Mas * numModStates - AdductTolerance)) && (fgResults[i].DeconRow.MonoisotopicMassWeight <= (fgResults[j].DeconRow.MonoisotopicMassWeight - Mas * numModStates + AdductTolerance)))
{
//obtain max and min scan number
if (fgResults[i].MaxScanNum < fgResults[j].MaxScanNum)
{
fgResults[i].MaxScanNum = fgResults[j].MaxScanNum;
}
else
{
fgResults[i].MaxScanNum = fgResults[i].MaxScanNum;
}
if (fgResults[i].MinScanNum > fgResults[j].MinScanNum)
{
fgResults[i].MinScanNum = fgResults[j].MinScanNum;
}
else
{
fgResults[i].MinScanNum = fgResults[i].MinScanNum;
}
//numOfScan
fgResults[i].NumOfScan = fgResults[i].NumOfScan + fgResults[j].NumOfScan;
fgResults[i].ScanNumList.AddRange(fgResults[j].ScanNumList);
//ChargeStateList
for (int h = 0; h < fgResults[j].ChargeStateList.Count; h++)
{
fgResults[i].ChargeStateList.Add(fgResults[j].ChargeStateList[h]);
}
//avgSigNoiseList
for (int h = 0; h < fgResults[j].AvgSigNoiseList.Count; h++)
{
fgResults[i].AvgSigNoiseList.Add(fgResults[j].AvgSigNoiseList[h]);
}
//avgAA2List
for (int h = 0; h < fgResults[j].AvgAA2List.Count; h++)
{
fgResults[i].AvgAA2List.Add(fgResults[j].AvgAA2List[h]);
}
//numModiStates
numModStates++;
fgResults[i].NumModiStates = fgResults[i].NumModiStates + 1;
fgResults[j].MostAbundant = false;
//TotalVolume
fgResults[i].TotalVolume = fgResults[i].TotalVolume + fgResults[j].TotalVolume;
if (fgResults[i].DeconRow.abundance < fgResults[j].DeconRow.abundance)
{
fgResults[i].DeconRow = fgResults[j].DeconRow;
numModStates = 1;
}
}
else if (fgResults[i].DeconRow.MonoisotopicMassWeight < (fgResults[j].DeconRow.MonoisotopicMassWeight - (Mas + AdductTolerance * 2) * numModStates))
{
//save running time. Since the list is sorted, any other mass below won't match as an adduct.
break;
}
}
}
}
}
else
{
for (int i = 0; i < fgResults.Count; i++)
{
fgResults[i].NumModiStates = 0;
}
}
List<ResultsGroup> sgResults = new List<ResultsGroup>();
//Implement the scan number threshold
fgResults = fgResults.OrderByDescending(a => a.NumOfScan).ToList();
Int32 scanCutOff = fgResults.Count() + 1;
for (int t = 0; t < fgResults.Count(); t++)
{
if (fgResults[t].NumOfScan < modelParameters.MinScanNumber)
{
scanCutOff = t;
break;
}
}
if (scanCutOff != fgResults.Count() + 1)
{
fgResults.RemoveRange(scanCutOff, fgResults.Count() - scanCutOff);
}
//############# This is the matching part. It matches the composition hypothesis with the grouped decon data.############
String[] MolNames = new String[17];
//These numOfMatches and lists are used to fit the linear regression model for Expect A: A+2. They are put here to decrease the already-int running time.
Int32 numOfMatches = new Int32();
List<Double> moleWeightforA = new List<Double>();
List<Double> AARatio = new List<Double>();
//Used to obtain all available bins for centroid scan error.
//Read the other lines for compTable data.
fgResults = fgResults.OrderByDescending(a => a.DeconRow.MonoisotopicMassWeight).ToList();
comhyp = comhyp.OrderByDescending(b => b.MassWeight).ToList();
bool hasMatch = false;
int lastMatch = 0;
for (int j = 0; j < fgResults.Count; j++)
{
if (fgResults[j].MostAbundant == true)
{
lastMatch = lastMatch - 4;
if (lastMatch < 0)
lastMatch = 0;
for (int i = lastMatch; i < comhyp.Count; i++)
{
Double MatchingError = comhyp[i].MassWeight * modelParameters.MatchErrorEM * 0.000001;
if ((fgResults[j].DeconRow.MonoisotopicMassWeight <= (comhyp[i].MassWeight + MatchingError)) && (fgResults[j].DeconRow.MonoisotopicMassWeight >= (comhyp[i].MassWeight - MatchingError)))
{
ResultsGroup GR = new ResultsGroup();
GR = matchPassbyValue(fgResults[j], comhyp[i]);
sgResults.Add(GR);
//Stuffs for feature
numOfMatches++;
moleWeightforA.Add(fgResults[j].DeconRow.MonoisotopicMassWeight);
AARatio.Add(fgResults[j].AvgAA2List.Average());
lastMatch = i + 1;
hasMatch = true;
continue;
}
//Since the data is sorted, there are no more matches below that row, break it.
if (fgResults[j].DeconRow.MonoisotopicMassWeight > (comhyp[i].MassWeight + MatchingError))
{
if (hasMatch == false)
{
ResultsGroup GR = new ResultsGroup();
CompositionHypothesisEntry comhypi = new CompositionHypothesisEntry();
GR = fgResults[j];
GR.Match = false;
GR.PredictedComposition = comhypi;
sgResults.Add(GR);
lastMatch = i;
break;
}
else
{
hasMatch = false;
break;
}
}
}
}
}
//##############Last part, this is to calculate the feature data needed for logistic regression###################
//Expected A and Centroid Scan Error need linear regression. The models are built here separately.
//In the this model. output is the Y axis and input is X.
SimpleLinearRegression AA2regression = new SimpleLinearRegression();
List<double> aainput = new List<double>();
List<double> aaoutput = new List<double>();
//Centroid Scan Error
List<double> ccinput = new List<double>();
List<double> ccoutput = new List<double>();
if (numOfMatches > 3)
{
for (int i = 0; i < sgResults.Count; i++)
{
if (sgResults[i].Match == true)
{
if (sgResults[i].AvgAA2List.Average() != 0)
{
aainput.Add(sgResults[i].DeconRow.MonoisotopicMassWeight);
aaoutput.Add(sgResults[i].AvgAA2List.Average());
}
if (sgResults[i].DeconRow.abundance > 250)
{
ccoutput.Add(sgResults[i].DeconRow.ScanNum);
ccinput.Add(sgResults[i].DeconRow.MonoisotopicMassWeight);
}
}
}
}
else
{
for (int i = 0; i < sgResults.Count; i++)
{
if (sgResults[i].AvgAA2List.Average() != 0)
{
aainput.Add(sgResults[i].DeconRow.MonoisotopicMassWeight);
aaoutput.Add(sgResults[i].AvgAA2List.Average());
}
if (sgResults[i].DeconRow.abundance > 250)
{
ccoutput.Add(sgResults[i].ScanNumList.Average());
ccinput.Add(sgResults[i].DeconRow.MonoisotopicMassWeight);
}
}
}
SimpleLinearRegression CSEregression = new SimpleLinearRegression();
CSEregression.Regress(ccinput.ToArray(), ccoutput.ToArray());
AA2regression.Regress(aainput.ToArray(), aaoutput.ToArray());
//The remaining features and input them into the grouping results
for (int i = 0; i < sgResults.Count; i++)
{
//ScanDensiy is: Number of scan divided by (max scan number – min scan number)
Double ScanDensity = new Double();
Int32 MaxScanNumber = sgResults[i].MaxScanNum;
Int32 MinScanNumber = sgResults[i].MinScanNum;
Double NumOfScan = sgResults[i].NumOfScan;
List<Int32> numChargeStatesList = sgResults[i].ChargeStateList.Distinct().ToList();
Int32 numChargeStates = numChargeStatesList.Count;
Double numModiStates = sgResults[i].NumModiStates;
if ((MaxScanNumber - MinScanNumber) != 0)
ScanDensity = NumOfScan / (MaxScanNumber - MinScanNumber + 15);
else
ScanDensity = 0;
//Use this scandensity for all molecules in this grouping.
sgResults[i].NumChargeStates = numChargeStates;
sgResults[i].ScanDensity = ScanDensity;
sgResults[i].NumModiStates = numModiStates;
sgResults[i].CentroidScanLR = CSEregression.Compute(sgResults[i].DeconRow.MonoisotopicMassWeight);
sgResults[i].CentroidScan = Math.Abs(sgResults[i].ScanNumList.Average() - sgResults[i].CentroidScanLR);
sgResults[i].ExpectedA = Math.Abs(sgResults[i].AvgAA2List.Average() - AA2regression.Compute(sgResults[i].DeconRow.MonoisotopicMassWeight));
sgResults[i].AvgSigNoise = sgResults[i].AvgSigNoiseList.Average();
}
for (int i = 0; i < sgResults.Count(); i++ )
{
sgResults[i].PredictedComposition.ElementNames.Clear();
sgResults[i].PredictedComposition.MoleculeNames.Clear();
if (i == sgResults.Count() - 1)
{
sgResults[0].PredictedComposition.ElementNames = elementIDs;
sgResults[0].PredictedComposition.MoleculeNames = molename;
}
}
return sgResults;
}
//this Grouping function performs the grouping.
private List<ResultsGroup> Groupings(String filename, ParametersForm.ParameterSettings paradata)
{
GetDeconData DeconDATA1 = new GetDeconData();
List<DeconRow> sortedDeconData = new List<DeconRow>();
sortedDeconData = DeconDATA1.getdata(filename);
//First, sort the list descendingly by its abundance.
sortedDeconData = sortedDeconData.OrderByDescending(a => a.abundance).ToList();
//################Second, create a new list to store results from the first grouping.###############
List<ResultsGroup> fgResults = new List<ResultsGroup>();
ResultsGroup GR2 = new ResultsGroup();
GR2.PredictedComposition = new CompositionHypothesisEntry();
Int32 currentMaxBin = new Int32();
currentMaxBin = 1;
GR2.DeconRow = sortedDeconData[0];
GR2.MostAbundant = true;
GR2.NumOfScan = 1;
GR2.MinScanNum = sortedDeconData[0].ScanNum;
GR2.MaxScanNum = sortedDeconData[0].ScanNum;
GR2.ChargeStateList = new List<int>();
GR2.ChargeStateList.Add(sortedDeconData[0].charge);
GR2.AvgSigNoiseList = new List<Double>();
GR2.AvgSigNoiseList.Add(sortedDeconData[0].SignalNoiseRatio);
GR2.AvgAA2List = new List<double>();
GR2.AvgAA2List.Add(sortedDeconData[0].MonoisotopicAbundance / (sortedDeconData[0].MonoisotopicPlus2Abundance + 1));
GR2.ScanNumList = new List<Int32>();
GR2.ScanNumList.Add(sortedDeconData[0].ScanNum);
GR2.NumModiStates = 1;
GR2.TotalVolume = sortedDeconData[0].abundance * sortedDeconData[0].fwhm;
GR2.ListAbundance = new List<double>();
GR2.ListAbundance.Add(sortedDeconData[0].abundance);
GR2.ListMonoMassWeight = new List<double>();
GR2.ListMonoMassWeight.Add(sortedDeconData[0].MonoisotopicMassWeight);
fgResults.Add(GR2);
for (int j = 1; j < sortedDeconData.Count; j++)
{
for (int i = 0; i < fgResults.Count; i++)
{
//Obtain grouping error. Note: its in ppm, so it needs to be multiplied by 0.000001.
Double GroupingError = fgResults[i].DeconRow.MonoisotopicMassWeight * paradata.GroupingErrorEG * 0.000001;
if ((sortedDeconData[j].MonoisotopicMassWeight < (fgResults[i].DeconRow.MonoisotopicMassWeight + GroupingError) && (sortedDeconData[j].MonoisotopicMassWeight > (fgResults[i].DeconRow.MonoisotopicMassWeight - GroupingError))))
{
if (fgResults[i].MaxScanNum < sortedDeconData[j].ScanNum)
{
fgResults[i].MaxScanNum = sortedDeconData[j].ScanNum;
}
else if (fgResults[i].MinScanNum > sortedDeconData[j].ScanNum)
{
fgResults[i].MinScanNum = sortedDeconData[j].ScanNum;
}
fgResults[i].NumOfScan = fgResults[i].NumOfScan + 1;
fgResults[i].ScanNumList.Add(sortedDeconData[j].ScanNum);
fgResults[i].TotalVolume = fgResults[i].TotalVolume + sortedDeconData[j].abundance * sortedDeconData[j].fwhm;
fgResults[i].ChargeStateList.Add(sortedDeconData[j].charge);
fgResults[i].AvgSigNoiseList.Add(sortedDeconData[j].SignalNoiseRatio);
fgResults[i].AvgAA2List.Add(sortedDeconData[j].MonoisotopicAbundance / (sortedDeconData[j].MonoisotopicPlus2Abundance + 1));
fgResults[i].ListAbundance.Add(sortedDeconData[j].abundance);
fgResults[i].ListMonoMassWeight.Add(sortedDeconData[j].MonoisotopicMassWeight);
break;
}
if (i == fgResults.Count - 1)
{
ResultsGroup GR = new ResultsGroup();
GR.PredictedComposition = new CompositionHypothesisEntry();
currentMaxBin = currentMaxBin + 1;
GR.DeconRow = sortedDeconData[j];
GR.MostAbundant = true;
GR.NumOfScan = 1;
GR.MinScanNum = sortedDeconData[j].ScanNum;
GR.MaxScanNum = sortedDeconData[j].ScanNum;
GR.ChargeStateList = new List<int>();
GR.ChargeStateList.Add(sortedDeconData[j].charge);
GR.AvgSigNoiseList = new List<Double>();
GR.AvgSigNoiseList.Add(sortedDeconData[j].SignalNoiseRatio);
GR.AvgAA2List = new List<double>();
GR.AvgAA2List.Add(sortedDeconData[j].MonoisotopicAbundance / (sortedDeconData[j].MonoisotopicPlus2Abundance + 1));
GR.ScanNumList = new List<int>();
GR.ScanNumList.Add(sortedDeconData[j].ScanNum);
GR.NumModiStates = 1;
GR.TotalVolume = sortedDeconData[j].abundance * sortedDeconData[j].fwhm;
GR.ListAbundance = new List<double>();
GR.ListAbundance.Add(sortedDeconData[j].abundance);
GR.ListMonoMassWeight = new List<double>();
GR.ListMonoMassWeight.Add(sortedDeconData[j].MonoisotopicMassWeight);
fgResults.Add(GR);
}
}
}
//Lastly calculate the Average Weighted Abundance
for (int y = 0; y < fgResults.Count(); y++)
{
Double sumofTopPart = 0;
for (int z = 0; z < fgResults[y].ListMonoMassWeight.Count(); z++)
{
sumofTopPart = sumofTopPart + fgResults[y].ListMonoMassWeight[z] * fgResults[y].ListAbundance[z];
}
fgResults[y].DeconRow.MonoisotopicMassWeight = sumofTopPart / fgResults[y].ListAbundance.Sum();
}
//######################## Here is the second grouping for NH3. ################################
fgResults = fgResults.OrderBy(o => o.DeconRow.MonoisotopicMassWeight).ToList();
for (int i = 0; i < fgResults.Count - 1; i++)
{
if (fgResults[i].MostAbundant == true)
{
int numModStates = 1;
for (int j = i + 1; j < fgResults.Count; j++)
{
Double AdductTolerance = fgResults[i].DeconRow.MonoisotopicMassWeight * paradata.AdductToleranceEA * 0.000001;
if ((fgResults[i].DeconRow.MonoisotopicMassWeight >= (fgResults[j].DeconRow.MonoisotopicMassWeight - 17.02654911 * numModStates - AdductTolerance)) && (fgResults[i].DeconRow.MonoisotopicMassWeight <= (fgResults[j].DeconRow.MonoisotopicMassWeight - 17.02654911 * numModStates + AdductTolerance)))
{
//obtain max and min scan number
if (fgResults[i].MaxScanNum < fgResults[j].MaxScanNum)
{
fgResults[i].MaxScanNum = fgResults[j].MaxScanNum;
}
else
{
fgResults[i].MaxScanNum = fgResults[i].MaxScanNum;
}
if (fgResults[i].MinScanNum > fgResults[j].MinScanNum)
{
fgResults[i].MinScanNum = fgResults[j].MinScanNum;
}
else
{
fgResults[i].MinScanNum = fgResults[i].MinScanNum;
}
//numOfScan
fgResults[i].NumOfScan = fgResults[i].NumOfScan + fgResults[j].NumOfScan;
fgResults[i].ScanNumList.AddRange(fgResults[j].ScanNumList);
//ChargeStateList
for (int h = 0; h < fgResults[j].ChargeStateList.Count; h++)
{
fgResults[i].ChargeStateList.Add(fgResults[j].ChargeStateList[h]);
}
//avgSigNoiseList
for (int h = 0; h < fgResults[j].AvgSigNoiseList.Count; h++)
{
fgResults[i].AvgSigNoiseList.Add(fgResults[j].AvgSigNoiseList[h]);
}
//avgAA2List
for (int h = 0; h < fgResults[j].AvgAA2List.Count; h++)
{
fgResults[i].AvgAA2List.Add(fgResults[j].AvgAA2List[h]);
}
//numModiStates
numModStates++;
fgResults[i].NumModiStates = fgResults[i].NumModiStates + 1;
fgResults[j].MostAbundant = false;
//TotalVolume
fgResults[i].TotalVolume = fgResults[i].TotalVolume + fgResults[j].TotalVolume;
if (fgResults[i].DeconRow.abundance < fgResults[j].DeconRow.abundance)
{
fgResults[i].DeconRow = fgResults[j].DeconRow;
numModStates = 1;
}
}
else if (fgResults[i].DeconRow.MonoisotopicMassWeight < (fgResults[j].DeconRow.MonoisotopicMassWeight - (17.02654911 + AdductTolerance * 2) * numModStates))
{
//save running time. Since the list is sorted, any other mass below won't match as an adduct.
break;
}
}
}
}
//Implement the scan number threshold
fgResults = fgResults.OrderByDescending(a => a.NumOfScan).ToList();
Int32 scanCutOff = fgResults.Count() + 1;
for (int t = 0; t < fgResults.Count(); t++)
{
if (fgResults[t].NumOfScan < paradata.MinScanNumber)
{
scanCutOff = t;
break;
}
}
if (scanCutOff != fgResults.Count() + 1)
{
fgResults.RemoveRange(scanCutOff, fgResults.Count() - scanCutOff);
}
for (int i = 0; i < fgResults.Count(); i++)
{
fgResults[i].Match = false;
}
//##############Last part, this is to calculate the feature data needed for logistic regression###################
//Expected A and Centroid Scan Error need linear regression. The models are built here separately.
//In the this model. output is the Y axis and input is X.
SimpleLinearRegression AA2regression = new SimpleLinearRegression();
List<double> aainput = new List<double>();
List<double> aaoutput = new List<double>();
//Centroid Scan Error
List<double> ccinput = new List<double>();
List<double> ccoutput = new List<double>();
for (int i = 0; i < fgResults.Count; i++)
{
if (fgResults[i].AvgAA2List.Average() != 0)
{
aainput.Add(fgResults[i].DeconRow.MonoisotopicMassWeight);
aaoutput.Add(fgResults[i].AvgAA2List.Average());
}
if (fgResults[i].DeconRow.abundance > 250)
{
ccoutput.Add(fgResults[i].ScanNumList.Average());
ccinput.Add(fgResults[i].DeconRow.MonoisotopicMassWeight);
}
}
SimpleLinearRegression CSEregression = new SimpleLinearRegression();
CSEregression.Regress(ccinput.ToArray(), ccoutput.ToArray());
AA2regression.Regress(aainput.ToArray(), aaoutput.ToArray());
//The remaining features and input them into the grouping results
for (int i = 0; i < fgResults.Count; i++)
{
//ScanDensiy is: Number of scan divided by (max scan number – min scan number)
Double ScanDensity = new Double();
Int32 MaxScanNumber = fgResults[i].MaxScanNum;
Int32 MinScanNumber = fgResults[i].MinScanNum;
Double NumOfScan = fgResults[i].NumOfScan;
List<Int32> numChargeStatesList = fgResults[i].ChargeStateList.Distinct().ToList();
Int32 numChargeStates = numChargeStatesList.Count;
Double numModiStates = fgResults[i].NumModiStates;
if ((MaxScanNumber - MinScanNumber) != 0)
ScanDensity = NumOfScan / (MaxScanNumber - MinScanNumber + 15);
else
ScanDensity = 0;
//Use this scandensity for all molecules in this grouping.
fgResults[i].NumChargeStates = numChargeStates;
fgResults[i].ScanDensity = ScanDensity;
fgResults[i].NumModiStates = numModiStates;
fgResults[i].CentroidScanLR = CSEregression.Compute(fgResults[i].DeconRow.MonoisotopicMassWeight);
fgResults[i].CentroidScan = Math.Abs(fgResults[i].ScanNumList.Average() - fgResults[i].CentroidScanLR);
fgResults[i].ExpectedA = Math.Abs(fgResults[i].AvgAA2List.Average() - AA2regression.Compute(fgResults[i].DeconRow.MonoisotopicMassWeight));
fgResults[i].AvgSigNoise = fgResults[i].AvgSigNoiseList.Average();
}
return fgResults;
}