List of usage examples for org.apache.commons.math3.stat.descriptive.moment Kurtosis clear
@Override public void clear()
From source file:nl.systemsgenetics.genenetworkbackend.div.CalculateGenePredictability.java
/** * @param args the command line arguments *///from www . j a v a 2 s . c o m public static void main(String[] args) throws IOException { File predictionMatrixFile = new File( "C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\Data31995Genes05-12-2017\\PCA_01_02_2018\\predictions\\reactome_predictions.txt.gz"); File annotationMatrixFile = new File( "C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\Data31995Genes05-12-2017\\PCA_01_02_2018\\PathwayMatrix\\Ensembl2Reactome_All_Levels.txt_matrix.txt.gz"); File significantTermsFile = new File( "C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\Data31995Genes05-12-2017\\PCA_01_02_2018\\predictions\\reactome_predictions_bonSigTerms_alsoInGoP.txt"); File outputFile = new File( "C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\Data31995Genes05-12-2017\\PCA_01_02_2018\\predictions\\reactome_predictions_genePredictability_alsoInGoP.txt"); // File predictionMatrixFile = new File("C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\Data31995Genes05-12-2017\\PCA_01_02_2018\\predictions\\hpo_predictions.txt.gz"); // File annotationMatrixFile = new File("C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\Data31995Genes05-12-2017\\PCA_01_02_2018\\PathwayMatrix\\ALL_SOURCES_ALL_FREQUENCIES_phenotype_to_genes.txt_matrix.txt.gz"); // File significantTermsFile = new File("C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\Data31995Genes05-12-2017\\PCA_01_02_2018\\predictions\\hpo_predictions_bonSigTerms.txt"); // File outputFile = new File("C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\Data31995Genes05-12-2017\\PCA_01_02_2018\\predictions\\hpo_predictions_genePredictability.txt"); // // File predictionMatrixFile = new File("C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\Data31995Genes05-12-2017\\PCA_01_02_2018\\predictions\\go_F_predictions.txt.gz"); // File annotationMatrixFile = new File("C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\Data31995Genes05-12-2017\\PCA_01_02_2018\\PathwayMatrix\\goa_human.gaf_F_matrix.txt.gz"); // File significantTermsFile = new File("C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\Data31995Genes05-12-2017\\PCA_01_02_2018\\predictions\\go_F_predictions_bonSigTerms.txt"); // File outputFile = new File("C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\Data31995Genes05-12-2017\\PCA_01_02_2018\\predictions\\go_F_predictions_genePredictability.txt"); // // File predictionMatrixFile = new File("C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\Data31995Genes05-12-2017\\PCA_01_02_2018\\predictions\\kegg_predictions.txt.gz"); // File annotationMatrixFile = new File("C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\Data31995Genes05-12-2017\\PCA_01_02_2018\\PathwayMatrix\\c2.cp.kegg.v6.1.entrez.gmt_matrix.txt.gz"); // File significantTermsFile = new File("C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\Data31995Genes05-12-2017\\PCA_01_02_2018\\predictions\\kegg_predictions_bonSigTerms.txt"); // File outputFile = new File("C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\Data31995Genes05-12-2017\\PCA_01_02_2018\\predictions\\kegg_predictions_genePredictability.txt"); // // File predictionMatrixFile = new File("/groups/umcg-wijmenga/tmp04/umcg-svandam/GeneNetwork/Data31995Genes05-12-2017/GeneNetwork_V2_01-02-2018/Covariates/PCA/predictions/go_P_predictions.txt.gz"); // File annotationMatrixFile = new File("/groups/umcg-wijmenga/tmp04/umcg-svandam/GeneNetwork/Data31995Genes05-12-2017/GeneNetwork_V2_01-02-2018/Covariates/PCA/PathwayMatrix/goa_human.gaf_P_matrix.txt.gz"); // File significantTermsFile = new File("/groups/umcg-wijmenga/tmp04/umcg-svandam/GeneNetwork/Data31995Genes05-12-2017/GeneNetwork_V2_01-02-2018/Covariates/PCA/predictions/go_P_predictions_bonSigTerms_alsoInReactome.txt"); // File outputFile = new File("/groups/umcg-wijmenga/tmp04/umcg-svandam/GeneNetwork/Data31995Genes05-12-2017/GeneNetwork_V2_01-02-2018/Covariates/PCA/predictions/go_P_predictions_genePredictability_alsoInReactome.txt"); LinkedHashSet<String> significantTerms = loadSignificantTerms(significantTermsFile); DoubleMatrixDataset<String, String> predictionMatrix = DoubleMatrixDataset .loadDoubleData(predictionMatrixFile.getAbsolutePath()); DoubleMatrixDataset<String, String> annotationMatrix = DoubleMatrixDataset .loadDoubleData(annotationMatrixFile.getAbsolutePath()); DoubleMatrixDataset<String, String> predictionMatrixSignificant = predictionMatrix .viewColSelection(significantTerms); DoubleMatrixDataset<String, String> annotationMatrixSignificant = annotationMatrix .viewColSelection(significantTerms); if (!predictionMatrixSignificant.getColObjects().equals(annotationMatrixSignificant.getColObjects())) { System.err.println("Differnce in terms"); return; } if (!predictionMatrixSignificant.getRowObjects().equals(annotationMatrixSignificant.getRowObjects())) { System.err.println("Differnce in genes"); return; } MannWhitneyUTest2 uTest = new MannWhitneyUTest2(); Kurtosis kurtosisCalculator = new Kurtosis(); Skewness skewnessCalculator = new Skewness(); Mean annotatedMeanCalculator = new Mean(); Mean notAnnotatedMeanCalculator = new Mean(); double[] genePredictabilityZscores = new double[predictionMatrixSignificant.rows()]; int[] pathwayCount = new int[predictionMatrixSignificant.rows()]; double[] geneKurtosis = new double[predictionMatrixSignificant.rows()]; double[] geneSkewness = new double[predictionMatrixSignificant.rows()]; double[] geneAnnotatedMean = new double[predictionMatrixSignificant.rows()]; double[] geneNotAnnotatedMean = new double[predictionMatrixSignificant.rows()]; for (int g = 0; g < predictionMatrixSignificant.rows(); g++) { kurtosisCalculator.clear(); skewnessCalculator.clear(); annotatedMeanCalculator.clear(); notAnnotatedMeanCalculator.clear(); DoubleMatrix1D geneAnnotations = annotationMatrixSignificant.getRow(g); int geneAnnotationCount = geneAnnotations.cardinality(); pathwayCount[g] = geneAnnotationCount; double[] zScoresAnnotatedPathways = new double[geneAnnotationCount]; double[] zScoresOtherPathways = new double[annotationMatrixSignificant.columns() - geneAnnotationCount]; int x = 0; int y = 0; for (int p = 0; p < geneAnnotations.size(); p++) { double z = predictionMatrixSignificant.getElementQuick(g, p); if (geneAnnotations.getQuick(p) != 0) { annotatedMeanCalculator.increment(z); zScoresAnnotatedPathways[x++] = z; } else { notAnnotatedMeanCalculator.increment(z); zScoresOtherPathways[y++] = z; } kurtosisCalculator.increment(z); skewnessCalculator.increment(z); } if (geneAnnotationCount >= 10) { uTest.setData(zScoresOtherPathways, zScoresAnnotatedPathways); genePredictabilityZscores[g] = uTest.getZ(); } else { genePredictabilityZscores[g] = Double.NaN; } geneKurtosis[g] = kurtosisCalculator.getResult(); geneSkewness[g] = skewnessCalculator.getResult(); geneAnnotatedMean[g] = annotatedMeanCalculator.getResult(); geneNotAnnotatedMean[g] = notAnnotatedMeanCalculator.getResult(); } CSVWriter writer = new CSVWriter(new FileWriter(outputFile), '\t', '\0', '\0', "\n"); String[] outputLine = new String[7]; int c = 0; outputLine[c++] = "Gene"; outputLine[c++] = "Z-score"; outputLine[c++] = "Skewness"; outputLine[c++] = "Kurtosis"; outputLine[c++] = "MeanNotAnnotated"; outputLine[c++] = "MeanAnnotated"; outputLine[c++] = "Annoted_pathways"; writer.writeNext(outputLine); ArrayList<String> geneNames = predictionMatrixSignificant.getRowObjects(); for (int g = 0; g < predictionMatrixSignificant.rows(); g++) { c = 0; outputLine[c++] = geneNames.get(g); outputLine[c++] = String.valueOf(genePredictabilityZscores[g]); outputLine[c++] = String.valueOf(geneSkewness[g]); outputLine[c++] = String.valueOf(geneKurtosis[g]); outputLine[c++] = String.valueOf(geneNotAnnotatedMean[g]); outputLine[c++] = String.valueOf(geneAnnotatedMean[g]); outputLine[c++] = String.valueOf(pathwayCount[g]); writer.writeNext(outputLine); } writer.close(); }