@Override
public void run() throws IOException {
for (int d = 0; d < m_gg.length; d++) {
// now test all triples
SNPLoader snpLoader = m_gg[d].getGenotypeData().createSNPLoader();
int[] indWGA = m_gg[d].getExpressionToGenotypeIdArray();
for (int perm = 0; perm < m_settings.nrPermutationsFDR + 1; perm++) {
String outfile = null;
if (perm == 0) {
outfile = outDir + m_gg[d].getSettings().name + "_IVAnalysis-RealData.txt";
} else {
outfile =
outDir + m_gg[d].getSettings().name + "_IVAnalysis-PermutationRound-" + perm + ".txt";
m_gg[d].permuteSampleLables();
}
TextFile out = new TextFile(outfile, TextFile.W);
Iterator<Triple<String, String, String>> it = snpProbeCombos.iterator();
Triple<String, String, String> next = it.next();
ProgressBar pb =
new ProgressBar(
snpProbeCombos.size(), "Running Mediation Analysis - Permutation " + perm);
out.writeln(
"SNP\tSNP Chr\tSNP ChrPos\t"
+ "Alleles\tDirectionAllele\t"
+ "N\t"
+ "CisArrayAddress\tCisProbe Chr\tCisProbe ChrPos\t"
+ "CisGeneName\t"
+ "TransArrayAddress\tTransProbe Chr\tTransProbe ChrPos\t"
+ "TransGeneName\t"
+ "CisTrans-Correlation\t"
+ "Cis-eQTL-Beta\t"
+ "Cis-eQTL-SE\t"
+ "CisTrans-Beta\t"
+ "CisTrans-SE\t"
+ "Trans-eQTL-Beta\t"
+ "Trans-eQTL-SE\t"
+ "CisTrans-Residual-Correlation\t"
+ "CisTrans-Residual-Beta\t"
+ "CisTrans-Residual-SE\t"
+ "Trans-eQTL-Residual-Beta\t"
+ "Trans-eQTL-Residual-SE\t"
+ "Beta-Ratio");
while (next != null) {
String snp = next.getLeft();
String cisprobe = next.getMiddle();
String transprobe = next.getRight();
Integer snpId = m_gg[d].getGenotypeData().getSnpToSNPId().get(snp);
Integer cisProbeId = m_gg[d].getExpressionData().getProbeToId().get(cisprobe);
Integer transProbeId = m_gg[d].getExpressionData().getProbeToId().get(transprobe);
if (snpId == -9 || cisProbeId == null || transProbeId == null) {
// out.writeln(snp + "\t" + snpId + "\t" + cisprobe + "\t" +
// cisProbeId + "\t" + null + "\t" + transprobe + "\t" + transProbeId + "\t" + null +
// "\t" + null + "\t" + null + "\t" + null + "\t" + null +
// "\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA");
} else {
SNP snpObj = m_gg[d].getGenotypeData().getSNPObject(snpId);
snpLoader.loadGenotypes(snpObj);
if (snpLoader.hasDosageInformation()) {
snpLoader.loadDosage(snpObj);
}
double[] origCisVals = m_gg[d].getExpressionData().getMatrix()[cisProbeId];
double[] origTransVals = m_gg[d].getExpressionData().getMatrix()[transProbeId];
int calledGenotypes = 0;
for (int i = 0; i < m_gg[d].getExpressionData().getIndividuals().length; i++) {
int genotypeId = indWGA[i];
short gt = snpObj.getGenotypes()[genotypeId];
if (genotypeId > -1 && gt > -1) {
calledGenotypes++;
}
}
double[] genotypes = new double[calledGenotypes];
double[] cisvals = new double[calledGenotypes];
double[] transvals = new double[calledGenotypes];
calledGenotypes = 0;
for (int i = 0; i < m_gg[d].getExpressionData().getIndividuals().length; i++) {
int genotypeId = indWGA[i];
short gt = snpObj.getGenotypes()[genotypeId];
if (genotypeId > -1 && gt > -1) {
genotypes[calledGenotypes] = snpObj.getDosageValues()[genotypeId];
cisvals[calledGenotypes] = origCisVals[i];
transvals[calledGenotypes] = origTransVals[i];
calledGenotypes++;
}
}
// normalize genotype and cis + trans to get beta's equal to the correlation coefficient
genotypes = normalize(genotypes);
cisvals = normalize(cisvals);
transvals = normalize(transvals);
double corrCisTrans =
JSci.maths.ArrayMath.correlation(cisvals, transvals); // for code validation
double[] cisTransRCs =
Regression.getLinearRegressionCoefficients(
cisvals, transvals); // returns beta, alpha, se, t
double[] snpCisRCs =
Regression.getLinearRegressionCoefficients(
genotypes, cisvals); // returns beta, alpha, se, t
double[] snpTransRCs = Regression.getLinearRegressionCoefficients(genotypes, transvals);
// remove correlation between cis and trans probe
// double[] resCis = new double[cisvals.length];
double[] resTransVals = new double[cisvals.length];
for (int i = 0; i < resTransVals.length; i++) {
// resCis[i] = cisvals[i] - snpCisRCs[0] * genotypes[i];
resTransVals[i] = transvals[i] - cisTransRCs[0] * cisvals[i];
}
resTransVals = normalize(resTransVals);
double[] cisResTransRCs =
Regression.getLinearRegressionCoefficients(
cisvals, resTransVals); // returns beta, alpha, se, t
double[] snpResTransRCs =
Regression.getLinearRegressionCoefficients(genotypes, resTransVals);
double rescorr =
JSci.maths.ArrayMath.correlation(cisvals, resTransVals); // for code validation
out.writeln(
snp
+ "\t"
+ snpObj.getChr()
+ "\t"
+ snpObj.getChrPos()
+ "\t"
+ BaseAnnot.toString(snpObj.getAlleles()[0])
+ "/"
+ BaseAnnot.toString(snpObj.getAlleles()[1])
+ "\t"
+ BaseAnnot.toString(snpObj.getAlleles()[0])
+ "\t"
+ transvals.length
+ "\t"
+ cisprobe
+ "\t"
+ m_gg[d].getExpressionData().getChr()[cisProbeId]
+ "\t"
+ m_gg[d].getExpressionData().getChrStart()[cisProbeId]
+ ":"
+ m_gg[d].getExpressionData().getChrStop()[cisProbeId]
+ "\t"
+ m_gg[d].getExpressionData().getAnnotation()[cisProbeId]
+ "\t"
+ transprobe
+ "\t"
+ m_gg[d].getExpressionData().getChr()[transProbeId]
+ "\t"
+ m_gg[d].getExpressionData().getChrStart()[transProbeId]
+ ":"
+ m_gg[d].getExpressionData().getChrStop()[transProbeId]
+ "\t"
+ m_gg[d].getExpressionData().getAnnotation()[transProbeId]
+ "\t"
+ corrCisTrans
+ "\t"
+ snpCisRCs[0]
+ "\t"
+ snpCisRCs[2]
+ "\t"
+ cisTransRCs[0]
+ "\t"
+ cisTransRCs[2]
+ "\t"
+ snpTransRCs[0]
+ "\t"
+ snpTransRCs[2]
+ "\t"
+ rescorr
+ "\t"
+ cisResTransRCs[0]
+ "\t"
+ cisResTransRCs[2]
+ "\t"
+ snpResTransRCs[0]
+ "\t"
+ snpResTransRCs[2]
+ "\t"
+ (snpResTransRCs[0] / snpTransRCs[0]));
snpObj.clearGenotypes();
}
if (it.hasNext()) {
next = it.next();
} else {
next = null;
}
pb.iterate();
}
pb.close();
out.close();
}
snpLoader.close();
}
}
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());
}
