Examples with NullTaskMonitor org.baderlab.csplugins.enrichmentmap.util.NullTaskMonitor used on opensource projects

Example 1 with NullTaskMonitor

use of org.baderlab.csplugins.enrichmentmap.util.NullTaskMonitor in project EnrichmentMapApp by BaderLab.

the class InitializeGenesetsOfInterestTask method initializeSets.

/**
	 * filter the genesets, restricting them to only those passing the user
	 * specified thresholds.
	 * 
	 * @return true if successful and false otherwise.
	 */
public boolean initializeSets(TaskMonitor tm) {
    if (tm == null)
        tm = new NullTaskMonitor();
    DiscreteTaskMonitor taskMonitor = new DiscreteTaskMonitor(tm, map.getDataSetCount());
    //create subset of genesets that contains only the genesets of interest with pvalue and qbalue less than values specified by the user.
    //Go through each Dataset populating the Gene set of interest in each dataset object
    Map<String, EMDataSet> datasets = map.getDataSets();
    // count how many experiments (DataSets) contain the geneset
    Optional<Integer> minExperiments = map.getParams().getMinExperiments();
    Map<String, Integer> occurrences = minExperiments.isPresent() ? new HashMap<>() : null;
    for (String datasetName : datasets.keySet()) {
        taskMonitor.inc();
        EMDataSet dataset = datasets.get(datasetName);
        // all these maps use the geneset name as key
        Map<String, EnrichmentResult> enrichmentResults = dataset.getEnrichments().getEnrichments();
        Map<String, GeneSet> genesets = dataset.getSetOfGeneSets().getGeneSets();
        Map<String, GeneSet> genesetsOfInterest = dataset.getGeneSetsOfInterest().getGeneSets();
        // If there are no genesets associated with this dataset then get the complete set assumption being that the gmt file applies to all datasets.
        if (genesets == null || genesets.isEmpty()) {
            genesets = map.getAllGeneSets();
        }
        //if there are no enrichment Results then do nothing
        if (enrichmentResults == null || enrichmentResults.isEmpty()) {
            return false;
        }
        //iterate through the GSEA Results to figure out which genesets we want to use
        for (String genesetName : enrichmentResults.keySet()) {
            EnrichmentResult result = enrichmentResults.get(genesetName);
            // update rank at max for leading edge calculation
            if (dataset.getMethod() == Method.GSEA) {
                Ranking ranks = dataset.getExpressionSets().getRanksByName(datasetName);
                updateRankAtMax((GSEAResult) result, ranks);
            }
            if (result.geneSetOfInterest(map.getParams())) {
                GeneSet geneset = genesets.get(genesetName);
                if (geneset != null) {
                    // while we are checking, update the size of the genesets based on post filtered data
                    result.setGsSize(geneset.getGenes().size());
                    if (occurrences != null) {
                        occurrences.merge(genesetName, 1, (v, d) -> v + 1);
                    }
                    genesetsOfInterest.put(genesetName, geneset);
                } else if (throwIfMissing) {
                    throw new IllegalThreadStateException("The Geneset: " + genesetName + " is not found in the GMT file.");
                }
            }
        }
    }
    // Remove gene-sets that don't pass the minimum occurrence cutoff
    if (occurrences != null) {
        for (EMDataSet dataset : datasets.values()) {
            Map<String, GeneSet> genesetsOfInterest = dataset.getGeneSetsOfInterest().getGeneSets();
            genesetsOfInterest.keySet().removeIf(geneset -> occurrences.getOrDefault(geneset, 0) < minExperiments.get());
        }
    }
    return true;
}

Example 2 with NullTaskMonitor

use of org.baderlab.csplugins.enrichmentmap.util.NullTaskMonitor in project EnrichmentMapApp by BaderLab.

the class ParseGREATEnrichmentResults method parseLines.

@Override
public void parseLines(List<String> lines, EMDataSet dataset, TaskMonitor taskMonitor) {
    if (taskMonitor == null)
        taskMonitor = new NullTaskMonitor();
    taskMonitor.setTitle("Parsing Enrichment Result file");
    boolean hasBackground = false;
    EMCreationParameters params = dataset.getMap().getParams();
    //Get the type of filter user specified on the GREAT results
    //If it is hyper use column 14 Hypergeometric p-value and 16 FDR for hyper
    //If it is binom use column 5 bionomial p-value and 7 FDR for binom
    //If they specify both use the highest p-value and q-value from the above columns
    GreatFilter filterType = dataset.getMap().getParams().getGreatFilter();
    Map<String, GeneSet> genesets = dataset.getSetOfGeneSets().getGeneSets();
    EnrichmentMap map = dataset.getMap();
    Map<String, EnrichmentResult> results = dataset.getEnrichments().getEnrichments();
    int currentProgress = 0;
    int maxValue = lines.size();
    taskMonitor.setStatusMessage("Parsing Great Results file - " + maxValue + " rows");
    //for great files there is an FDR
    dataset.getMap().getParams().setFDR(true);
    //skip the first l9 which just has the field names (start i=1)
    //check to see how many columns the data has
    //go through each line until we find the header line
    int k = 0;
    String line = lines.get(k);
    String[] tokens = line.split("\t");
    for (; k < lines.size(); k++) {
        line = lines.get(k);
        tokens = line.split("\t");
        int length = tokens.length;
        if ((length == 24) && tokens[3].equalsIgnoreCase("BinomRank")) {
            break;
        } else //There is not binom rank and no binomial data.
        if ((length == 20) && tokens[3].equalsIgnoreCase("Rank")) {
            hasBackground = true;
            break;
        }
    }
    //go through the rest of the lines
    for (int i = k + 1; i < lines.size(); i++) {
        line = lines.get(i);
        tokens = line.split("\t");
        //there are extra lines at the end of the file that should be ignored.
        if (!hasBackground && tokens.length != 24)
            continue;
        if (hasBackground && tokens.length != 20)
            continue;
        double pvalue = 1.0;
        double FDRqvalue = 1.0;
        GenericResult result;
        int gs_size = 0;
        double NES = 1.0;
        //details of export file
        //http://bejerano.stanford.edu/help/display/GREAT/Export
        //The second column of the file is the name of the geneset
        final String name = tokens[1].trim() + "-" + tokens[2].trim();
        //the first column of the file is the description
        final String description = tokens[2].trim();
        //when there are two different species it is possible that the gene set could
        //already exist in the set of genesets.  if it does exist then add the genes
        //in this set to the geneset
        ImmutableSet.Builder<Integer> builder = ImmutableSet.builder();
        if (genesets.containsKey(name))
            builder = builder.addAll(genesets.get(name).getGenes());
        String[] gene_tokens;
        if (!hasBackground)
            gene_tokens = tokens[23].split(",");
        else
            gene_tokens = tokens[18].split(",");
        //All subsequent fields in the list are the geneset associated with this geneset.
        for (int j = 0; j < gene_tokens.length; j++) {
            String gene = gene_tokens[j].toUpperCase();
            //if it is already in the hash then get its associated key and put it into the set of genes
            if (map.containsGene(gene)) {
                builder.add(map.getHashFromGene(gene));
            } else if (!gene.isEmpty()) {
                Integer hash = map.addGene(gene).get();
                builder.add(hash);
            }
        }
        //finished parsing that geneset
        //add the current geneset to the hashmap of genesets
        GeneSet gs = new GeneSet(name, description, builder.build());
        genesets.put(name, gs);
        //There are two tests run by GREAT, the binomial on regions and the hypergeometric based on genes
        //The first pass of results shows only those that are significant both
        //The user can then choose to use either or both together
        //
        //If it is hyper use column 14 Hypergeometric p-value and 16 FDR for hyper
        //If it is binom use column 5 bionomial p-value and 7 FDR for binom
        //If they specify both use the highest p-value and q-value from the above columns
        double hyper_pvalue = 1;
        double hyper_fdr = 1;
        double binom_pvalue = 1;
        double binom_fdr = 1;
        if (!hasBackground) {
            if (!tokens[4].equalsIgnoreCase(""))
                binom_pvalue = Double.parseDouble(tokens[4]);
            if (!tokens[6].equalsIgnoreCase(""))
                binom_fdr = Double.parseDouble(tokens[6]);
            if (!tokens[13].equalsIgnoreCase(""))
                hyper_pvalue = Double.parseDouble(tokens[13]);
            if (!tokens[15].equalsIgnoreCase(""))
                hyper_fdr = Double.parseDouble(tokens[15]);
        } else {
            if (!tokens[4].equalsIgnoreCase(""))
                hyper_pvalue = Double.parseDouble(tokens[4]);
            if (!tokens[6].equalsIgnoreCase(""))
                hyper_fdr = Double.parseDouble(tokens[6]);
        }
        if (filterType == GreatFilter.HYPER) {
            pvalue = hyper_pvalue;
            FDRqvalue = hyper_fdr;
        } else if (filterType == GreatFilter.BINOM) {
            pvalue = binom_pvalue;
            FDRqvalue = binom_fdr;
        } else if (filterType == GreatFilter.BOTH) {
            pvalue = Math.max(hyper_pvalue, binom_pvalue);
            FDRqvalue = Math.max(hyper_fdr, binom_fdr);
        } else if (filterType == GreatFilter.EITHER) {
            pvalue = Math.min(hyper_pvalue, binom_pvalue);
            FDRqvalue = Math.min(hyper_fdr, binom_fdr);
        } else {
            System.out.println("Invalid attribute setting for GREAT p-value specification");
        }
        //Keep track of minimum p-value to better calculate jslider
        if (pvalue < params.getPvalueMin())
            params.setPvalueMin(pvalue);
        if (FDRqvalue < params.getQvalueMin())
            params.setQvalueMin(FDRqvalue);
        //If this is a background set then it is in the 16th column
        if ((!hasBackground) && (!tokens[19].equalsIgnoreCase("")))
            gs_size = Integer.parseInt(tokens[19]);
        else if ((hasBackground) && (!tokens[15].equalsIgnoreCase("")))
            gs_size = Integer.parseInt(tokens[15]);
        result = new GenericResult(name, description, pvalue, gs_size, FDRqvalue);
        // Calculate Percentage.  This must be a value between 0..100.
        int percentComplete = (int) (((double) currentProgress / maxValue) * 100);
        taskMonitor.setProgress(percentComplete);
        currentProgress++;
        //check to see if the gene set has already been entered in the results
        //it is possible that one geneset will be in both phenotypes.
        //if it is already exists then we want to make sure the one retained is the result with the
        //lower p-value.
        //ticket #149
        GenericResult temp = (GenericResult) results.get(name);
        if (temp == null)
            results.put(name, result);
        else {
            if (result.getPvalue() < temp.getPvalue())
                results.put(name, result);
        }
    }
}

Example 3 with NullTaskMonitor

use of org.baderlab.csplugins.enrichmentmap.util.NullTaskMonitor in project EnrichmentMapApp by BaderLab.

the class ParseGSEAEnrichmentResults method parseLines.

@Override
public void parseLines(List<String> lines, EMDataSet dataset, TaskMonitor taskMonitor) {
    if (taskMonitor == null)
        taskMonitor = new NullTaskMonitor();
    taskMonitor.setTitle("Parsing Bingo Enrichment Result file");
    //skip the first line which just has the field names (start i=1)
    dataset.getMap().getParams().setFDR(true);
    int currentProgress = 0;
    int maxValue = lines.size();
    taskMonitor.setStatusMessage("Parsing Enrichment Results file - " + maxValue + " rows");
    Map<String, EnrichmentResult> results = dataset.getEnrichments().getEnrichments();
    for (int i = 1; i < lines.size(); i++) {
        String line = lines.get(i);
        String[] tokens = line.split("\t");
        int size = 0;
        double ES = 0.0;
        double NES = 0.0;
        double pvalue = 1.0;
        double FDRqvalue = 1.0;
        double FWERqvalue = 1.0;
        int rankAtMax = -1;
        double scoreAtMax = DefaultScoreAtMax;
        //The first column of the file is the name of the geneset
        String Name = tokens[0].toUpperCase().trim();
        //The fourth column is the size of the geneset
        if (!tokens[3].isEmpty()) {
            size = Integer.parseInt(tokens[3]);
        }
        //The fifth column is the Enrichment score (ES)
        if (!tokens[4].isEmpty()) {
            ES = Double.parseDouble(tokens[4]);
        }
        //The sixth column is the Normalize Enrichment Score (NES)
        if (!tokens[5].isEmpty()) {
            NES = Double.parseDouble(tokens[5]);
        }
        //The seventh column is the nominal p-value
        if (!tokens[6].isEmpty()) {
            pvalue = Double.parseDouble(tokens[6]);
        }
        //the eighth column is the FDR q-value
        if (!tokens[7].isEmpty()) {
            FDRqvalue = Double.parseDouble(tokens[7]);
        }
        //the ninth column is the FWER q-value
        if (!tokens[8].isEmpty()) {
            FWERqvalue = Double.parseDouble(tokens[8]);
        }
        //the tenth column is the rankatmax
        if (!tokens[9].isEmpty()) {
            rankAtMax = Integer.parseInt(tokens[9]);
        }
        GSEAResult result = new GSEAResult(Name, size, ES, NES, pvalue, FDRqvalue, FWERqvalue, rankAtMax, scoreAtMax);
        // Calculate Percentage.  This must be a value between 0..100.
        int percentComplete = (int) (((double) currentProgress / maxValue) * 100);
        taskMonitor.setProgress(percentComplete);
        currentProgress++;
        results.put(Name, result);
    }
}

Example 4 with NullTaskMonitor

use of org.baderlab.csplugins.enrichmentmap.util.NullTaskMonitor in project EnrichmentMapApp by BaderLab.

the class ParseGenericEnrichmentResults method parseLines.

@Override
public void parseLines(List<String> lines, EMDataSet dataset, TaskMonitor taskMonitor) {
    if (taskMonitor == null)
        taskMonitor = new NullTaskMonitor();
    taskMonitor.setTitle("Parsing Generic Result file");
    //Get the current genesets so we can check that all the results are in the geneset list
    //and put the size of the genesets into the visual style
    Map<String, GeneSet> genesets = dataset.getSetOfGeneSets().getGeneSets();
    int currentProgress = 0;
    int maxValue = lines.size();
    taskMonitor.setStatusMessage("Parsing Generic Results file - " + maxValue + " rows");
    boolean FDR = false;
    //skip the first line which just has the field names (start i=1)
    //check to see how many columns the data has
    String line = lines.get(0);
    String[] tokens = line.split("\t");
    int length = tokens.length;
    EnrichmentMap map = dataset.getMap();
    SetOfEnrichmentResults enrichments = dataset.getEnrichments();
    Map<String, EnrichmentResult> results = enrichments.getEnrichments();
    String upPhenotype = enrichments.getPhenotype1();
    String downPhenotype = enrichments.getPhenotype2();
    //check to see if there are genesets.
    //if there are no genesets then populate the genesets from the generic file
    //can only do this if the 6th column has a list of genes for that geneset.
    boolean populate_gs = false;
    if (genesets == null || genesets.isEmpty())
        populate_gs = true;
    else
        //as this is the default for gprofiler use the Description in the visual style instead of the formatted name
        //but only if there is a gmt supplied.  If using just the generic output file there is not field for description
        dataset.getMap().getParams().setEMgmt(true);
    for (int i = 1; i < lines.size(); i++) {
        line = lines.get(i);
        tokens = line.split("\t");
        //update the length each time because some line might have missing values
        length = tokens.length;
        double pvalue = 1.0;
        double FDRqvalue = 1.0;
        GenericResult result;
        int gs_size = 0;
        double NES = 1.0;
        //The first column of the file is the name of the geneset
        final String name = tokens[0].toUpperCase().trim();
        final String description = tokens[1].toUpperCase();
        if (genesets.containsKey(name)) {
            gs_size = genesets.get(name).getGenes().size();
        }
        //The third column is the nominal p-value
        if (tokens[2] == null || tokens[2].equalsIgnoreCase("")) {
        //do nothing
        } else {
            pvalue = Double.parseDouble(tokens[2]);
        }
        if (length > 3) {
            //the fourth column is the FDR q-value
            if (tokens[3] == null || tokens[3].equalsIgnoreCase("")) {
            //do nothing
            } else {
                FDRqvalue = Double.parseDouble(tokens[3]);
                FDR = true;
            }
            // and if it is a number the only important part is the sign
            if (length > 4) {
                if (tokens[4] == null || tokens[4].equalsIgnoreCase("")) {
                } else {
                    //check to see if the string matches the specified phenotypes
                    if (tokens[4].equalsIgnoreCase(upPhenotype))
                        NES = 1.0;
                    else if (tokens[4].equalsIgnoreCase(downPhenotype))
                        NES = -1.0;
                    else //try and see if the user has specified the phenotype as a number
                    {
                        try {
                            NES = Double.parseDouble(tokens[4]);
                        } catch (NumberFormatException nfe) {
                            throw new IllegalThreadStateException(tokens[4] + " is not a valid phenotype.  Phenotype specified in generic enrichment results file must have the same phenotype as specified in advanced options or must be a positive or negative number.");
                        }
                    }
                }
                //its enrichment
                if (length > 5 && populate_gs) {
                    //get all the genes in the field
                    String[] gene_tokens = tokens[5].split(",");
                    ImmutableSet.Builder<Integer> builder = ImmutableSet.builder();
                    //All subsequent fields in the list are the geneset associated with this geneset.
                    for (String token : gene_tokens) {
                        String gene = token.trim().toUpperCase();
                        //if it is already in the hash then get its associated key and put it into the set of genes
                        if (map.containsGene(gene)) {
                            builder.add(map.getHashFromGene(gene));
                        } else if (!gene.isEmpty()) {
                            Integer hash = map.addGene(gene).get();
                            builder.add(hash);
                        }
                    }
                    GeneSet gs = new GeneSet(name, description, builder.build());
                    gs_size = gs.getGenes().size();
                    //put the new or filtered geneset back into the set.
                    genesets.put(name, gs);
                }
                //end of tokens>5
                result = new GenericResult(name, description, pvalue, gs_size, FDRqvalue, NES);
            } else
                //end of tokens>4
                result = new GenericResult(name, description, pvalue, gs_size, FDRqvalue);
        } else {
            result = new GenericResult(name, description, pvalue, gs_size);
        }
        // Calculate Percentage.  This must be a value between 0..100.
        int percentComplete = (int) (((double) currentProgress / maxValue) * 100);
        taskMonitor.setProgress(percentComplete);
        currentProgress++;
        //check to see if the gene set has already been entered in the results
        //it is possible that one geneset will be in both phenotypes.
        //if it is already exists then we want to make sure the one retained is the result with the
        //lower p-value.
        //ticket #149
        GenericResult temp = (GenericResult) results.get(name);
        if (temp == null)
            results.put(name, result);
        else {
            if (result.getPvalue() < temp.getPvalue())
                results.put(name, result);
        }
    }
    if (FDR)
        dataset.getMap().getParams().setFDR(FDR);
}

Example 5 with NullTaskMonitor

use of org.baderlab.csplugins.enrichmentmap.util.NullTaskMonitor in project EnrichmentMapApp by BaderLab.

the class ParseBingoEnrichmentResults method parseLines.

@Override
public void parseLines(List<String> lines, EMDataSet dataset, TaskMonitor taskMonitor) {
    if (taskMonitor == null)
        taskMonitor = new NullTaskMonitor();
    taskMonitor.setTitle("Parsing Bingo Enrichment Result file");
    //with Bingo results there are no genesets defined.  first pass through the file
    // needs to parse the genesets
    //the bingo file has 20 lines of info at the top of the file before you get to results.
    //parameters that can be extracted from Bingo files:
    //GO-ID	p-value	corr p-value	x	n	X	N	Description	Genes in test set
    // (column 1 ) GO-Id - is just the numerical part of the GO term (does not contain GO:0000)
    //(column 2 ) p-value
    //(column 3 ) corr pvalue
    //(column 4 ) x - number of genes in the subset of interest with this annotation
    //(column 5 ) n - number of genes in the universe with this annotation
    //(column 6 ) X - number of genes in the subset
    //(column 7 ) N - number of genes in the universe
    //(column 8 ) Description - GO term name
    //(column 9 ) Gene in test set - a list of genes in the subset of interest that are annotated to this term
    // Column 8 is the geneset name
    // Column 9 is the list of genes in this geneset -- therefore pre-filtered.
    Map<String, GeneSet> genesets = dataset.getSetOfGeneSets().getGeneSets();
    //get the genes (which should also be empty
    EnrichmentMap map = dataset.getMap();
    Map<String, EnrichmentResult> results = dataset.getEnrichments().getEnrichments();
    int currentProgress = 0;
    int maxValue = lines.size();
    boolean FDR = true;
    taskMonitor.setStatusMessage("Parsing Generic Results file -" + maxValue + " rows");
    //skip the first l9 which just has the field names (start i=1)
    //check to see how many columns the data has
    //go through each line until we find the header line
    int k = 0;
    String line = lines.get(k);
    String[] tokens = line.split("\t");
    for (; k < lines.size(); k++) {
        line = lines.get(k);
        tokens = line.split("\t");
        int length = tokens.length;
        if ((length == 9) && tokens[0].equalsIgnoreCase("GO-ID") && tokens[8].equalsIgnoreCase("Genes in test set")) {
            break;
        }
    }
    if (k == lines.size())
        throw new IllegalThreadStateException("Bingo results file is missing data.");
    for (int i = k + 1; i < lines.size(); i++) {
        line = lines.get(i);
        tokens = line.split("\t");
        double pvalue = 1.0;
        double FDRqvalue = 1.0;
        GenericResult result;
        int gs_size = 0;
        double NES = 1.0;
        //The 8th column of the file is the name of the geneset
        final String name = tokens[7].toUpperCase().trim();
        //the 8th column of the file is the description
        final String description = tokens[7].toUpperCase();
        //when there are two different species it is possible that the gene set could
        //already exist in the set of genesets.  if it does exist then add the genes
        //in this set to the geneset
        ImmutableSet.Builder<Integer> builder = ImmutableSet.builder();
        if (genesets.containsKey(name))
            builder = builder.addAll(genesets.get(name).getGenes());
        String[] gene_tokens = tokens[8].split("\\|");
        //All subsequent fields in the list are the geneset associated with this geneset.
        for (int j = 0; j < gene_tokens.length; j++) {
            String gene = gene_tokens[j].toUpperCase();
            //if it is already in the hash then get its associated key and put it into the set of genes
            if (map.containsGene(gene)) {
                builder.add(map.getHashFromGene(gene));
            } else if (!gene.isEmpty()) {
                Integer hash = map.addGene(gene).get();
                builder.add(hash);
            }
        }
        //finished parsing that geneset
        //add the current geneset to the hashmap of genesets
        GeneSet gs = new GeneSet(name, description, builder.build());
        genesets.put(name, gs);
        //The 2nd column is the nominal p-value
        if (tokens[1].equalsIgnoreCase("")) {
        //do nothing
        } else {
            pvalue = Double.parseDouble(tokens[1]);
        }
        //the Count is the size of the geneset (restricted by the gene list)
        if (tokens[3].equalsIgnoreCase("")) {
        //do nothing
        } else {
            gs_size = Integer.parseInt(tokens[3]);
        }
        //Use the correct p-value - 3rd column
        if (tokens[2].equalsIgnoreCase("")) {
        //do nothing
        } else {
            FDRqvalue = Double.parseDouble(tokens[2]);
        }
        result = new GenericResult(name, description, pvalue, gs_size, FDRqvalue);
        // Calculate Percentage.  This must be a value between 0..100.
        int percentComplete = (int) (((double) currentProgress / maxValue) * 100);
        taskMonitor.setProgress(percentComplete);
        currentProgress++;
        //check to see if the gene set has already been entered in the results
        //it is possible that one geneset will be in both phenotypes.
        //if it is already exists then we want to make sure the one retained is the result with the
        //lower p-value.
        //ticket #149
        GenericResult temp = (GenericResult) results.get(name);
        if (temp == null)
            results.put(name, result);
        else {
            if (result.getPvalue() < temp.getPvalue())
                results.put(name, result);
        }
    }
    if (FDR)
        dataset.getMap().getParams().setFDR(FDR);
}