CalculationThread(
int i,
LinkedBlockingQueue<WorkPackage> packageQueue,
LinkedBlockingQueue<WorkPackage> resultQueue,
TriTyperExpressionData[] expressiondata,
DoubleMatrixDataset<String, String>[] covariates,
IntMatrix2D probeTranslationTable,
int[][] expressionToGenotypeIds,
Settings settings,
EQTLPlotter plotter,
boolean binaryoutput,
boolean useAbsoluteZScores,
boolean testSNPsPresentInBothDatasets) {
// m_binaryoutput = binaryoutput;
m_name = i;
m_workpackage_queue = packageQueue;
m_result_queue = resultQueue;
m_probeTranslation = probeTranslationTable;
m_expressiondata = expressiondata;
boolean m_cis = settings.cisAnalysis;
boolean m_trans = settings.transAnalysis;
metaAnalyseInteractionTerms = settings.metaAnalyseInteractionTerms;
metaAnalyseModelCorrelationYHat = settings.metaAnalyseModelCorrelationYHat;
m_useAbsoluteZScores = useAbsoluteZScores;
m_name = i;
m_numProbes = m_probeTranslation.columns();
m_numDatasets = m_probeTranslation.rows();
m_expressionToGenotypeIds = expressionToGenotypeIds;
// probeVariance = new double[m_numDatasets][0];
// probeMean = new double[m_numDatasets][0];
// probeName = new String[m_numDatasets][0];
// for (int d = 0; d < m_numDatasets; d++) {
// probeVariance[d] = expressiondata[d].getProbeVariance();
// probeMean[d] = expressiondata[d].getProbeMean();
// probeName[d] = expressiondata[d].getProbes();
// }
m_covariates = covariates;
this.testSNPsPresentInBothDatasets = testSNPsPresentInBothDatasets;
cisOnly = false;
// cisTrans = false;
transOnly = false;
determinebeta = settings.provideBetasAndStandardErrors;
determinefoldchange = settings.provideFoldChangeData;
if (m_cis && !m_trans) {
cisOnly = true;
} else if (!m_cis && m_trans) {
transOnly = true;
}
// else if (m_cis && m_trans) {
// cisTrans = true;
// }
m_eQTLPlotter = plotter;
m_pvaluePlotThreshold = settings.plotOutputPValueCutOff;
// if (covariates != null) {
// try {
// rConnection = new RConnection();
// REXP x = rConnection.eval("R.version.string");
// System.out.println("Thread made R Connection: " + x.asString());
//// rConnection.voidEval("install.packages('sandwich')");
// rConnection.voidEval("library(sandwich)");
// } catch (RserveException ex) {
// Logger.getLogger(CalculationThread.class.getName()).log(Level.SEVERE, null,
// ex);
// rConnection = null;
// } catch (REXPMismatchException ex) {
// Logger.getLogger(CalculationThread.class.getName()).log(Level.SEVERE, null,
// ex);
// rConnection = null;
// }
// }
}
private void analyze(WorkPackage wp) {
testsPerformed = 0;
currentWP = wp;
wp.setNumTested(0);
// RunTimer t1 = new RunTimer();
// load SNP genotypes
SNP[] snps = wp.getSnps();
int[] probes = wp.getProbes();
Result dsResults = null;
double[] snpvariances = new double[m_numDatasets];
double[][] snpmeancorrectedgenotypes = new double[m_numDatasets][0];
double[][] originalgenotypes = new double[m_numDatasets][0];
boolean[][] includeExpressionSample = new boolean[m_numDatasets][0];
for (int d = 0; d < m_numDatasets; d++) {
SNP dSNP = snps[d];
if (dSNP != null) {
double[] x = dSNP.selectGenotypes(m_expressionToGenotypeIds[d], false, true);
originalgenotypes[d] = dSNP.selectGenotypes(m_expressionToGenotypeIds[d], false, false);
int xLen = x.length;
double meanX = JSci.maths.ArrayMath.mean(x);
snpmeancorrectedgenotypes[d] = new double[xLen];
for (int i = 0; i < xLen; i++) {
snpmeancorrectedgenotypes[d][i] = x[i] - meanX;
}
double varianceX = JSci.maths.ArrayMath.variance(x);
if (varianceX != 0) {
snpvariances[d] = varianceX;
int inds[] = m_expressionToGenotypeIds[d];
int sampleCount = m_expressionToGenotypeIds[d].length;
includeExpressionSample[d] = new boolean[sampleCount];
byte[] genotypes = dSNP.getGenotypes();
for (int s = 0; s < sampleCount; s++) {
int ind = inds[s];
double valX = genotypes[ind]; // loadedSNPGenotype[ind];
if (valX != -1) {
includeExpressionSample[d][s] = true;
} else {
includeExpressionSample[d][s] = false;
}
}
} else {
dSNP.clearGenotypes();
dSNP = null;
wp.getFlipSNPAlleles()[d] = null;
snps[d] = null;
}
}
}
if (cisOnly) {
dsResults = new Result(m_numDatasets, wp.getProbes().length, wp.getId());
for (int d = 0; d < m_numDatasets; d++) {
SNP dSNP = snps[d];
if (dSNP != null) {
dsResults.numSamples[d] = snpmeancorrectedgenotypes[d].length;
double[][] rawData = m_expressiondata[d].getMatrix();
double[] varY = m_expressiondata[d].getProbeVariance();
double[] meanY = m_expressiondata[d].getProbeMean();
int samplecount = m_expressiondata[d].getIndividuals().length;
double[][] covariates = null;
if (m_covariates != null) {
DoubleMatrixDataset<String, String> covariateData = m_covariates[d];
covariates = covariateData.rawData;
}
for (int p = 0; p < probes.length; p++) {
int pid = probes[p];
Integer probeId = m_probeTranslation.get(d, pid);
if (probeId != -9) {
test(
d,
p,
probeId,
snpmeancorrectedgenotypes[d],
originalgenotypes[d],
snpvariances[d],
varY[probeId],
meanY[probeId],
includeExpressionSample[d],
samplecount,
rawData,
covariates,
dsResults,
this.currentWP,
this.metaAnalyseModelCorrelationYHat,
this.metaAnalyseInteractionTerms,
this.determinefoldchange);
} else {
dsResults.correlations[d][p] = Double.NaN;
dsResults.zscores[d][p] = Double.NaN;
}
}
} else {
for (int p = 0; p < probes.length; p++) {
dsResults.correlations[d][p] = Double.NaN;
dsResults.zscores[d][p] = Double.NaN;
}
}
}
} else if (transOnly) {
HashSet<Integer> probestoExclude = null;
if (probes != null) {
probestoExclude = new HashSet<Integer>();
for (int p = 0; p < probes.length; p++) {
probestoExclude.add(probes[p]);
}
}
dsResults = new Result(m_numDatasets, m_numProbes, wp.getId());
for (int d = 0; d < m_numDatasets; d++) {
SNP dSNP = snps[d];
dsResults.numSamples[d] = snpmeancorrectedgenotypes[d].length;
double[][] rawData = m_expressiondata[d].getMatrix();
double[] varY = m_expressiondata[d].getProbeVariance();
double[] meanY = m_expressiondata[d].getProbeMean();
int samplecount = m_expressiondata[d].getIndividuals().length;
if (dSNP != null) {
dsResults.numSamples[d] = snpmeancorrectedgenotypes[d].length;
for (int pid = 0; pid < m_numProbes; pid++) {
if (probestoExclude == null || !probestoExclude.contains(pid)) {
Integer probeId = m_probeTranslation.get(d, pid);
if (probeId != -9) {
test(
d,
pid,
probeId,
snpmeancorrectedgenotypes[d],
originalgenotypes[d],
snpvariances[d],
varY[probeId],
meanY[probeId],
includeExpressionSample[d],
samplecount,
rawData,
null,
dsResults,
this.currentWP,
this.metaAnalyseModelCorrelationYHat,
this.metaAnalyseInteractionTerms,
this.determinefoldchange);
} else {
dsResults.correlations[d][pid] = Double.NaN;
dsResults.zscores[d][pid] = Double.NaN;
}
} else {
dsResults.correlations[d][pid] = Double.NaN;
dsResults.zscores[d][pid] = Double.NaN;
}
}
} else {
for (int p = 0; p < m_numProbes; p++) {
dsResults.correlations[d][p] = Double.NaN;
dsResults.zscores[d][p] = Double.NaN;
}
}
}
} else {
dsResults = new Result(m_numDatasets, m_numProbes, wp.getId());
for (int d = 0; d < m_numDatasets; d++) {
SNP dSNP = snps[d];
dsResults.numSamples[d] = snpmeancorrectedgenotypes[d].length;
double[][] rawData = m_expressiondata[d].getMatrix();
double[] varY = m_expressiondata[d].getProbeVariance();
double[] meanY = m_expressiondata[d].getProbeMean();
int samplecount = m_expressiondata[d].getIndividuals().length;
if (dSNP != null) {
dsResults.numSamples[d] = snpmeancorrectedgenotypes[d].length;
// RunTimer t2 = new RunTimer();
for (int pid = 0; pid < m_numProbes; pid++) {
Integer probeId = m_probeTranslation.get(d, pid);
if (probeId != -9) {
test(
d,
pid,
probeId,
snpmeancorrectedgenotypes[d],
originalgenotypes[d],
snpvariances[d],
varY[probeId],
meanY[probeId],
includeExpressionSample[d],
samplecount,
rawData,
null,
dsResults,
this.currentWP,
this.metaAnalyseModelCorrelationYHat,
this.metaAnalyseInteractionTerms,
this.determinefoldchange);
} else {
dsResults.correlations[d][pid] = Double.NaN;
dsResults.zscores[d][pid] = Double.NaN;
}
}
// System.out.println("Test: "+t2.getTimeDesc());
} else {
for (int p = 0; p < m_numProbes; p++) {
dsResults.correlations[d][p] = Double.NaN;
dsResults.zscores[d][p] = Double.NaN;
}
}
}
}
convertResultsToPValues(wp, dsResults);
if (m_eQTLPlotter != null) {
for (int p = 0; p < dsResults.pvalues.length; p++) {
double pval = dsResults.pvalues[p];
if (!Double.isNaN(pval)) {
if (pval < m_pvaluePlotThreshold) {
ploteQTL(wp, p);
}
}
}
}
snps = wp.getSnps();
if (snps != null) {
for (SNP snp : snps) {
if (snp != null) {
snp.clearGenotypes();
}
}
}
// if result output is binary, convert to bytes and deflate the set of bytes.
// if (m_binaryoutput) {
// deflateResults(wp);
// }
// now push the results in the queue..
try {
wp.setNumTested(testsPerformed);
m_result_queue.put(wp);
} catch (InterruptedException e) {
e.printStackTrace();
}
// System.out.println("Analyze: "+t1.getTimeDesc());
}
