use of org.baderlab.csplugins.enrichmentmap.model.EMCreationParameters.GreatFilter 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);
}
}
}
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