コード例 #1
0
  /**
   * Checks whether CEL files and CDF files are from the same platform.
   *
   * @param normalCelFileName Affymetrix CEL file of the paired normal sample
   * @param tumorCelFileName Affymetrix CEL file of the tumor sample
   * @param cdfFileName Affymetrix library file (CDF file) for the platform on which the samples are
   *     generated
   * @return <code>true</code> if the CEL files and CDF file are from the same platform, otherwise
   *     <code>false</code>
   */
  private boolean checkChipType(
      String normalCelFileName, String tumorCelFileName, String cdfFileName) {

    // Check whether the normal CEL file, tumor CEL file, and the CDF file are consistent, and set
    // the variable chipType.

    FusionCELData celNormal;
    FusionCELData celTumor;
    FusionCDFData cdf;

    celNormal = new FusionCELData();
    celNormal.setFileName(normalCelFileName);
    if (celNormal.read() == false) {
      isSuccessfulOpenFile = false;
      return false;
    }

    celTumor = new FusionCELData();
    celTumor.setFileName(tumorCelFileName);
    if (celTumor.read() == false) {
      isSuccessfulOpenFile = false;
      return false;
    }

    cdf = new FusionCDFData();
    cdf.setFileName(cdfFileName);
    if (cdf.read() == false) {
      isSuccessfulOpenFile = false;
      return false;
    }

    if (!celNormal.getChipType().equals(cdf.getChipType())) {
      isSuccessfulOpenFile = false;
      return false;
    }

    if (!celTumor.getChipType().equals(cdf.getChipType())) {
      isSuccessfulOpenFile = false;
      return false;
    }

    chipType = celNormal.getChipType();
    isSuccessfulOpenFile = true;

    return true;
  }
コード例 #2
0
  /**
   * Reads CEL files. <code>readCel</code> reads the intensity signals from the tumor sample and the
   * normal sample. The copy number at a particular locus is calculated using the ratio of the tumor
   * intensity at that locus and the correspoding normal intensity times 2.
   *
   * @param normalCelFileName Affymetrix CEL file of the paired normal sample
   * @param tumorCelFileName Affymetrix CEL file of the tumor sample
   * @param cdfFileName Affymetrix library file (CDF file) for the platform on which the samples are
   *     generated
   */
  private void readCel(String normalCelFileName, String tumorCelFileName, String cdfFileName) {

    FusionCELData celNormal;
    FusionCELData celTumor;
    FusionCDFData cdf;

    celNormal = new FusionCELData();
    celNormal.setFileName(normalCelFileName);
    if (celNormal.read() == false) {
      System.out.println("Failed to read the CEL file.");
      return;
    }

    celTumor = new FusionCELData();
    celTumor.setFileName(tumorCelFileName);
    if (celTumor.read() == false) {
      System.out.println("Failed to read the CEL file.");
      return;
    }

    cdf = new FusionCDFData();
    cdf.setFileName(cdfFileName);
    if (cdf.read() == false) {
      System.out.println("Failed to read the CDF file.");
      return;
    }

    int nsets = cdf.getHeader().getNumProbeSets();

    ProbeSetIntensityData[] probeSetDataNormal = new ProbeSetIntensityData[nsets];
    ProbeSetIntensityData[] probeSetDataTumor = new ProbeSetIntensityData[nsets];
    // SNP array structure:
    //      1.  Each probeset contains several groups (the number of groups varies among different
    // probeset)
    //      2.  Each group contains several cells (the number of cells also varies among different
    // groups)
    for (int iset = 0; iset < nsets; iset++) {

      String probeSetName = cdf.getProbeSetName(iset); // get the probeset name
      FusionCDFProbeSetInformation set = new FusionCDFProbeSetInformation();
      cdf.getProbeSetInformation(iset, set);
      int ngroups = set.getNumGroups();

      int numPmANormal = 0; // Pm : perfect match
      int numPmBNormal = 0;

      int numPmATumor = 0;
      int numPmBTumor = 0;

      int numMmANormal = 0; // Mm: Mis-Match
      int numMmBNormal = 0;

      int numMmATumor = 0;
      int numMmBTumor = 0;

      probeSetDataNormal[iset] = new ProbeSetIntensityData();
      probeSetDataTumor[iset] = new ProbeSetIntensityData();

      probeSetDataNormal[iset].probeSetType = set.getProbeSetType();
      probeSetDataTumor[iset].probeSetType = set.getProbeSetType();

      probeSetDataNormal[iset].probeSetID = cdf.getProbeSetName(iset) + "";
      probeSetDataTumor[iset].probeSetID = cdf.getProbeSetName(iset) + "";

      for (int igroup = 0; igroup < ngroups; igroup++) {

        FusionCDFProbeGroupInformation group = new FusionCDFProbeGroupInformation();
        set.getGroup(igroup, group);
        int ncells = group.getNumCells();

        for (int icell = 0; icell < ncells; icell++) {

          FusionCDFProbeInformation probe = new FusionCDFProbeInformation();
          group.getCell(icell, probe);

          try {

            char pBase = probe.getPBase();
            char tBase = probe.getTBase();
            //                       only If the match is perfect, the intensity of this cell
            // contributes
            if ((((pBase + tBase) == 213) || ((pBase + tBase) == 202))) {
              //            Perfect match is the match that with pBase:tBase = a:t or c:g
              if ((igroup % 2) == 0) {

                if (!celNormal.isOutlier(probe.getX(), probe.getY())) {
                  probeSetDataNormal[iset].pmA +=
                      celNormal.getIntensity(probe.getX(), probe.getY());
                  numPmANormal++;
                }

                if (!celTumor.isOutlier(probe.getX(), probe.getY())) {
                  probeSetDataTumor[iset].pmA += celTumor.getIntensity(probe.getX(), probe.getY());
                  numPmATumor++;
                }

              } else {

                if (!celNormal.isOutlier(probe.getX(), probe.getY())) {
                  probeSetDataNormal[iset].pmB +=
                      celNormal.getIntensity(probe.getX(), probe.getY());
                  numPmBNormal++;
                }

                if (!celTumor.isOutlier(probe.getX(), probe.getY())) {
                  probeSetDataTumor[iset].pmB += celTumor.getIntensity(probe.getX(), probe.getY());
                  numPmBTumor++;
                }
              }
            }
          } catch (Exception e) {
          }
        }
      }
      // using the average of intensity of the perfect match cells as the intensity for certain
      // probeset

      if (numPmANormal != 0) {
        probeSetDataNormal[iset].pmA = probeSetDataNormal[iset].pmA / numPmANormal;
      }
      if (numPmBNormal != 0) {
        probeSetDataNormal[iset].pmB = probeSetDataNormal[iset].pmB / numPmBNormal;
      }
      if (numPmATumor != 0) {
        probeSetDataTumor[iset].pmA = probeSetDataTumor[iset].pmA / numPmATumor;
      }
      if (numPmBTumor != 0) {
        probeSetDataTumor[iset].pmB = probeSetDataTumor[iset].pmB / numPmBTumor;
      }
    }

    try {
      genotypeCalling(probeSetDataNormal); //  to decide whether certain probe is AB type
    } catch (Exception e) {
    }

    Arrays.sort(probeSetDataNormal);
    Arrays.sort(probeSetDataTumor);

    ArrayList<Integer> indexFoundList = new ArrayList<Integer>(probeSetDataNormal.length);
    ArrayList<Integer> indexFoundListSNP = new ArrayList<Integer>(probeSetDataNormal.length);

    for (int i = 0; i < numAnnotatedProbeSet; i++) {
      int indexFound =
          Arrays.binarySearch(probeSetDataNormal, new ProbeSetIntensityData(probeSetID[i]));

      if (indexFound >= 0) {

        isGenotypeAB[i] = probeSetDataNormal[indexFound].isGenotypeAB;
        probeSetType[i] = probeSetDataNormal[indexFound].probeSetType;

        copyNumber[i] =
            2
                * (probeSetDataTumor[indexFound].pmA + probeSetDataTumor[indexFound].pmB)
                / (probeSetDataNormal[indexFound].pmA + probeSetDataNormal[indexFound].pmB);

        intensityNormal[i] =
            probeSetDataNormal[indexFound].pmA + probeSetDataNormal[indexFound].pmB;

        intensityTumor[i] = probeSetDataTumor[indexFound].pmA + probeSetDataTumor[indexFound].pmB;

        //                For 250k affymetrix chip, all the probes are SNP probes
        if (probeSetType[i] == FusionGeneChipProbeSetType.GenotypingProbeSetType) {
          alleleA[i] = probeSetDataTumor[indexFound].pmA / probeSetDataNormal[indexFound].pmA;
          alleleB[i] = probeSetDataTumor[indexFound].pmB / probeSetDataNormal[indexFound].pmB;

          if (alleleA[i] > 1E-10
              && alleleA[i] <= 30
              && alleleB[i] >= 1E-10
              && alleleB[i] <= 30
              && copyNumber[i] > 1E-10
              && copyNumber[i] <= 30) {
            isOutlier[i] = false;
            indexFoundList.add(i);
            indexFoundListSNP.add(i);
          }
        }
        //              For SNP6.0, there are half SNP probes and half CN probes
        if (probeSetType[i] == FusionGeneChipProbeSetType.CopyNumberProbeSetType) {
          if (copyNumber[i] > 1E-10 && copyNumber[i] <= 30) {
            isOutlier[i] = false;
            indexFoundList.add(i);
          }
        }
      }
    }

    /* Median filtering on the intensity data before calculating the copy numbers*/
    // Filters f1 = new Filters(intensityNormal);
    // f1.medianFilter(3);
    // Filters f2 = new Filters(intensityTumor);
    // f2.medianFilter(3);

    // for (int j = 0; j < indexFoundList.size(); j ++) {

    //     int i = indexFoundList.get(j);

    //     copyNumber[i] = intensityTumor[i] / intensityNormal[i] * 2;

    //     if (!(copyNumber[i] > 1E-10 && copyNumber[i] <=30)) {
    // 	copyNumber[i] = 2.0;
    // 	isOutlier[i] = true;
    //     }

    // }

    globalNormalization(indexFoundList, indexFoundListSNP);
  }