@Override
@SuppressWarnings(value = "unchecked")
public RunImportData constructRunImportData() {
// Instanciate the DOM document
DocumentBuilderFactory docFactory = DocumentBuilderFactory.newInstance();
docFactory.setIgnoringComments(true);
docFactory.setIgnoringElementContentWhitespace(true);
docFactory.setValidating(false);
DocumentBuilder docBuilder;
Document xmlDoc = null;
try {
docBuilder = docFactory.newDocumentBuilder();
xmlDoc = docBuilder.parse(new InputSource("./LC480.xml"));
} catch (Exception ex) {
Exceptions.printStackTrace(ex);
}
// Setup the objects need for the import
// Setup the Run object
// Run run = new RunImpl();
// Set the run name and date
Run run = new RunImpl();
// Retrieve the xml file
// For development purposes, just use an example xml file
// File lcRdmlFile = IOUtilities.openXmlFile("Lightcycler XML Data Import");
// if (lcRdmlFile == null) {
// return null;
// }
// Setup the profile arraylists
ArrayList<SampleProfile> sampleProfileList = Lists.newArrayList();
ArrayList<CalibrationProfile> calbnProfileList = Lists.newArrayList();
// Determine the strandedness of the majority of the Targets...too bad that this is not provided
// by the instrument
TargetStrandedness targetStrandedness = RunImportUtilities.isTheTargetSingleStranded();
// Get the all of the profile nodes
NodeList profileNodeList = xmlDoc.getElementsByTagName("series");
// Cycle through all of the profile nodes
for (int i = 0; i < profileNodeList.getLength(); i++) {
// TODO determine whether this profile is sample or calibration
Profile profile = createProfileType(run);
Element profileElement = (Element) profileNodeList.item(i);
String wellLabel = profileElement.getAttribute("title");
// TODO parse the well label, sample and amplicon name from the wellLabel
NodeList cycleList = profileElement.getElementsByTagName("point");
// Collect and set the Fc reading
profile.setFcReadings(retrieveFcReadings(cycleList));
if (CalibrationProfile.class.isAssignableFrom(profile.getClass())) {
CalibrationProfile calProfile = (CalibrationProfile) profile;
calbnProfileList.add(calProfile);
} else {
SampleProfile sampleProfile = (SampleProfile) profile;
sampleProfileList.add(sampleProfile);
}
}
return null;
}
/**
* This conducts both nonlinear regression-derived Fb subtraction and baseline slope correction.
*
* @param profile
*/
private void conductBaselineCorrection() {
// Start with correcting for the NR-derived Fb slope
double[] rawFcReadings = profile.getRawFcReadings();
double[] processedFcDataset = new double[rawFcReadings.length]; // The new optimized Fc dataset
// This assumes that nonlinear regression has been conducted AND it was successfully completed
double nrFb = profile.getNrFb(); // The regression derived Fb
double nrFbSlope = profile.getNrFbSlope(); // The regression-derived Fb slope
for (int i = 0; i < processedFcDataset.length; i++) {
processedFcDataset[i] = rawFcReadings[i] - nrFb - (nrFbSlope * (i + 1));
}
profile.setFcReadings(processedFcDataset);
}
/**
* Retrieves the current LRE parameters from the Profile
*
* @param profile
* @return
*/
private LreParameters getLreParameters() {
// Setup the initial parameters
LreParameters lreDerivedParam = new LreParameters();
// Testing indicates that if Fb=0 the NR fails
if (profile.getNrFb() == 0) {
lreDerivedParam.setFb(profile.getFb()); // This Fb is derived from the average of cycles 4-9
} else {
lreDerivedParam.setFb(profile.getNrFb());
}
lreDerivedParam.setEmax(profile.getEmax()); // Current LRE-derived Emax
lreDerivedParam.setFmax(profile.getFmax()); // Current LRE-derived Fmax
lreDerivedParam.setFo(profile.getAvFo()); // Current LRE-derived average Fo
lreDerivedParam.setFbSlope(profile.getNrFbSlope()); // Current Fb slope
return lreDerivedParam;
}
private boolean
testIfRegressionWasSuccessful() { // **** TODO Design a more effective scheme to test for NR
// success
// Test if the regression analysis was successful based on the LRE line R2
Double r2 = profile.getR2();
Double emaxNR = profile.getNrEmax();
// Very, very crude approach
if (r2 < 0.8 || r2.isNaN() || emaxNR < 0.3) {
// Error dialog DISABLED as it is extremely irrating
// Toolkit.getDefaultToolkit().beep();
// SimpleDateFormat sdf = new SimpleDateFormat("dMMMyy");
// JOptionPane.showMessageDialog(WindowManager.getDefault().getMainWindow(),
// "The nonlinear regression analysis has failed for well '"
// + sdf.format(profile.getRunDate()) + ": "
// + profile.getWellLabel() + "'.\n"
// + "LRE analysis will be conducted without baseline-slope correction",
// "Failed to Apply Nonlinear Regression.",
// JOptionPane.ERROR_MESSAGE);
// Reset the LRE window using Fb
profile.setWasNonlinearRegressionSuccessful(false);
profile.setNrFb(0);
profile.setNrFbSlope(0);
// Return to average Fb background subtraction
LreWindowSelector.substractBackgroundUsingAvFc(profile);
// Recalculate the LRE parameters
prfSum.update();
// Reinitialize the profile
List<LreWindowSelectionParameters> l =
prfSum.getDatabase().getAllObjects(LreWindowSelectionParameters.class);
LreWindowSelectionParameters parameters = l.get(0);
LreAnalysisService lreAnalService = Lookup.getDefault().lookup(LreAnalysisService.class);
lreAnalService.lreWindowInitialization(prfSum, parameters);
return false;
}
profile.setWasNonlinearRegressionSuccessful(true);
return true;
}
/**
* Generates an optimized working Fc dataset via nonlinear regression to derived Fb and Fb-slope
* using the current LRE window.
*
* <p>Nonlinear regression is conducted using Emax, Fmax and Fo derived from the current LRE
* window, from WHICH values for Fb and Fb-slope are determined. These are then used to calculate
* a new working Fc dataset, followed by recalculation of the LRE parameters. This process
* repeated 3 times from which an average Fb and Fb-slope are then determined and a final
* optimized working Fc dataset generated. This is followed by a final recalculation of the LRE
* parameters to determine final values for Emax, Fmax and Fo. THIS DOES THIS INCLUDE ANY
* MODIFICATION TO THE LRE WINDOW.
*
* <p>Note also that the LRE parameters are updated and the modified Profile is saved, which
* includes initialization of a new Cycle linked list, so any calling function must reset its
* runner.
*
* @param prfSum the ProfileSummary encapsulating the Profile
* @return true if nonlinear regression analysis was successful or false if it failed
*/
public boolean generateOptimizedFcDatasetUsingNonliearRegression(ProfileSummary prfSum) {
this.prfSum = prfSum;
profile = prfSum.getProfile();
// The profile must have a valid LRE window
if (!profile.hasAnLreWindowBeenFound()) {
return false;
}
// Need to trim the profile in order to avoid aberrancies within early cycles and within the
// plateau phase
// Exclude the first three cycles
int firstCycle = 4; // Start at cycle 4
// Use the top of the LRE window as the last cycle included in the regression analysis******THIS
// IS VERY IMPORTANT
double[] fcArray = profile.getRawFcReadings();
int lastCycle = 0;
if (prfSum.getLreWindowEndCycle() != null) {
lastCycle = prfSum.getLreWindowEndCycle().getCycNum();
} else {
return false;
}
int numberOfCycles = lastCycle - firstCycle + 1;
// Construct the trimmed Fc dataset TreeMap<cycle number, Fc reading>
TreeMap<Integer, Double> profileMap = new TreeMap<Integer, Double>();
for (int i = 0; i < numberOfCycles; i++) {
profileMap.put(firstCycle + i, fcArray[firstCycle - 1 + i]);
}
// Run NR once to grossly stablize the LRE-derived parameters
LreParameters lreDerivedParam = getLreParameters();
LreParameters optParam = nrService.conductNonlinearRegression(lreDerivedParam, profileMap);
// Reinitialize the LRE-derived parameters
// First Reset nonlinear regression-derived Fb and Fb-slope within the profile
profile.setNrFb(optParam.getFb());
profile.setNrFbSlope(optParam.getFbSlope());
// Generate a new optimized Fc dataset based on NR-derived Fb and Fb-slope
conductBaselineCorrection();
// Updating the ProfileSummary updates the LRE-derived parameters within the Profile with no
// change to the LRE window
// However, this assumes that the NR was successful
prfSum.update();
// Reset the LRE-derived paramaters
lreDerivedParam = getLreParameters();
// Run the regression analysis 10 times to determine the average and SD
// This is necessary due to the poor performance of Peter Abeles’s EJML implementation
int numberOfIterations = 3;
ArrayList<Double> emaxArray = new ArrayList<Double>();
ArrayList<Double> fbArray = new ArrayList<Double>();
ArrayList<Double> foArray = new ArrayList<Double>();
ArrayList<Double> fmaxArray = new ArrayList<Double>();
ArrayList<Double> fbSlopeArray = new ArrayList<Double>();
double emaxSum = 0;
double fbSum = 0;
double foSum = 0;
double fmaxSum = 0;
double fbSlopeSum = 0;
for (int i = 0; i < numberOfIterations; i++) {
optParam = nrService.conductNonlinearRegression(lreDerivedParam, profileMap);
emaxArray.add(optParam.getEmax());
emaxSum += optParam.getEmax();
fbArray.add(optParam.getFb());
fbSum += optParam.getFb();
foArray.add(optParam.getFo());
foSum += optParam.getFo();
fmaxArray.add(optParam.getFmax());
fmaxSum += optParam.getFmax();
fbSlopeArray.add(optParam.getFbSlope());
fbSlopeSum += optParam.getFbSlope();
// Reinitialize the LRE-derived parameters
// First reset nonlinear regression-derived Fb and Fb-slope within the profile
profile.setNrFb(optParam.getFb());
profile.setNrFbSlope(optParam.getFbSlope());
// Generate a new optimized Fc dataset
conductBaselineCorrection();
// Update the LRE-derived parameters within the Profile
prfSum.update();
// Retrieve the new LRE parameters
lreDerivedParam = getLreParameters();
}
// Set the average for each parameter into the Profile
// This allows the final recalculation of the LRE parameters based on the average Fb and
// Fb-slope
profile.setNrEmax(emaxSum / numberOfIterations);
profile.setNrFb(fbSum / numberOfIterations);
profile.setNrFo(foSum / numberOfIterations);
profile.setNrFmax(fmaxSum / numberOfIterations);
profile.setNrFbSlope(fbSlopeSum / numberOfIterations);
// Determine and set the parameter SD
profile.setNrEmaxSD(MathFunctions.calcStDev(emaxArray));
profile.setNrFbSD(MathFunctions.calcStDev(fbArray));
profile.setNrFoSD(MathFunctions.calcStDev(foArray));
profile.setNrFmaxSD(MathFunctions.calcStDev(fmaxArray));
profile.setNrFbSlopeSD(MathFunctions.calcStDev(fbSlopeArray));
// Recaculate the optimized Fc dataset using the average NR-derived Fb and Fb-slope
conductBaselineCorrection();
// Update the LRE parameters
prfSum.update();
return testIfRegressionWasSuccessful();
}
