class BlastMapperTest(unittest.TestCase):
"""
Run a number of tests using taxa id
"""
def setUp(self):
"""
connect to the database
"""
self.parsedFile = os.path.join(os.path.dirname(__file__),"blast-parsed.csv")
self.bm = BlastMapper()
def test01Summarize(self):
"""
test the summarize function
"""
summaryFile = re.sub("\.csv","",self.parsedFile)+"_summary.csv"
if os.path.exists(summaryFile):
os.remove(summaryFile)
self.bm.create_summarized(self.parsedFile,uniprot=True)
self.assertTrue(os.path.exists(summaryFile))
def test02Something(self):
"""
read in the results summary
"""
summaryFile = re.sub("\.csv","",self.parsedFile)+"_summary.csv"
bmap = self.bm.load_summary(summaryFile,taxaList=["10090"])
self.assertEqual(bmap['GG11117|c2_g1_i1'][0],'INT1_MOUSE')
self.assertEqual(bmap['GG11117|c2_g1_i1'][1],'68510')
bmap = self.bm.load_summary(summaryFile,taxaList=["10090"],trinityGene=True)
self.assertEqual(bmap['GG11117|c2_g1'][0],'INT1_MOUSE')
bmap = self.bm.load_summary(summaryFile,taxaList=["10090"],trinityGene=True,best=False)
self.assertEqual(bmap['GG11117|c2_g1'][0][0],'INT1_MOUSE')
self.assertEqual(bmap['GG11117|c2_g1'][0][4],0.0)
for r in geneQueries]))
gene2sym.update(
dict([(str(r['ncbi_id']), str(r['symbol'])) for r in geneQueries]))
## load the blast map
bm = BlastMapper()
summaryFile1 = os.path.join(homeDir, "dn-trinity",
'blast-dn-parsed_summary.csv')
summaryFile2 = os.path.join(homeDir, "dn-trinity",
'blast-dm-parsed_summary.csv')
summaryFile3 = os.path.join(homeDir, "dn-trinity",
"blast-mc-parsed_summary.csv")
summaryFile4 = os.path.join(homeDir, "dn-trinity",
'blast-dp-parsed_summary.csv')
bmapSP = bm.load_summary(summaryFile1, trinityGene=False, best=True)
bmapDM = bm.load_summary(summaryFile2, trinityGene=False, best=True)
bmapMC = bm.load_summary(summaryFile3, trinityGene=False, best=True)
bmapDP = bm.load_summary(summaryFile4, trinityGene=False, best=True)
## prepare supplment output
columns = [20, 22, 25, 50, 17, 17, 17]
row = "+"
head = "+"
for col in columns:
row += "-" * col + "-+"
head += "=" * col + "=+"
print("\nGene sets\n_____________________")
print(row)
items = [
scps = SpectralClusterParamSearch(geneDistancePath,dtype='distance')
scps.run(chunks=15)
## plot the parameter search
psFigureFile = os.path.join(homeDir,"param-scan-%s.png"%(_aspect))
if not os.path.exists(psFigureFile):
scr = SpectralClusterResults(silvalFile,clustersFile)
scr.plot(figName=psFigureFile)
## run spectral clustering
k = 20
sigma = 0.08
labelsPath = os.path.join(homeDir,"sc-labels-%s.csv"%(_aspect))
if not os.path.exists(labelsPath):
sc = SpectralCluster(geneDistancePath,dtype='distance')
sc.run(k,sk=None,sigma=sigma,verbose=True)
sc.save(labelsPath=labelsPath)
## Save gene sets
bm = BlastMapper()
bmap = bm.load_summary('blast-parsed-summary.csv',best=False,taxaList=['8355','8364'])
transcriptMin,transcriptMax = 9,1000
gsFile = os.path.join(homeDir,"%s.gmt"%(_aspect))
if not os.path.exists(gsFile):
gsc = GeneSetCollection(labelsPath,gene2go)
gsc.write(blastMap=bmap,transcriptMin=transcriptMin,transcriptMax=transcriptMax,outFile=gsFile)
print("process complete.")
def write_summary(name, aspect, transcript, assembly, geneset):
## load the go dictionaries
termsPath = os.path.join("..", "results",
"go-terms-%s-%s.pickle" % (name, aspect))
tmp = open(termsPath, 'r')
gene2go, go2gene = cPickle.load(tmp)
tmp.close()
## load the blast map
bm = BlastMapper()
homeDir = os.path.join(os.path.expanduser("~"), "sequencing", "xenopus")
sizeMin, sizeMax = 5, 100
summaryFile = os.path.join(homeDir, "%s-trinity" % (assembly),
'blast-%s-parsed_summary.csv' % assembly)
if transcript == 'genes':
bmap = bm.load_summary(summaryFile,
trinityGene=True,
best=False,
taxaList=['8364', '8355', '9606'],
evalue=0.0001)
else:
bmap = bm.load_summary(summaryFile,
trinityGene=False,
best=False,
taxaList=['8364', '8355', '9606'],
evalue=0.0001)
## get gene level differencial exp results
featuresDir = os.path.join(homeDir, "%s-trinity" % assembly, "features")
deseqResultsPath = os.path.join(featuresDir,
"deseq_%s_de.csv" % (transcript))
deseqIds, deseqColumns, deseqMat = read_de_results(deseqResultsPath,
tool='DESeq')
padjInd = np.where(deseqColumns == 'padj')[0]
pvalInd = np.where(deseqColumns == 'pvalue')[0]
## input/output
genesetSummaryFile = os.path.join(
"..", "results", "genesets", "%s-%s-%s-%s-%s.csv" %
(name, aspect, transcript, assembly, re.sub("gs-", "", geneset)))
genesetFile = os.path.join(
"..", "results",
"%s-%s-%s-%s.gmt" % (name, aspect, assembly, transcript))
if not os.path.exists(genesetFile):
raise Exception("cannot find gene set file")
allGenesets = {}
fid = open(genesetFile, 'r')
for linja in fid:
linja = [re.sub("\s+", "", l) for l in linja.split("\t")]
allGenesets[linja[0]] = linja[2:]
fid.close()
gsTranscripts = allGenesets[geneset]
## map back to gene space and collect go terms
transcript2genes = {}
for t in gsTranscripts:
transcript2genes[t] = {}
species = list(set([hit[2] for hit in bmap[t]]))
## organize the hits by species
for hit in bmap[t]:
if not transcript2genes[t].has_key(hit[2]):
transcript2genes[t][hit[2]] = []
transcript2genes[t][hit[2]].append(hit[1])
## get inferred go terms for each transcript
transcript2go = {}
for t, hit in transcript2genes.iteritems():
transcript2go[t] = []
for genes in hit.itervalues():
#gene = v[1]
for gene in genes:
if gene2go.has_key(gene):
transcript2go[t].extend(gene2go[gene])
transcript2go[t] = list(set(transcript2go[t]))
transcript2go[t].sort()
## write to file
writer = csv.writer(open(genesetSummaryFile, 'w'))
writer.writerow(["transcript", "p-value", "genes", "go-terms"])
allTerms = []
for ts in gsTranscripts:
pvalue = deseqMat[np.where(deseqIds == ts)[0], pvalInd][0]
reportedGenes = []
for taxa, genes in transcript2genes[ts].iteritems():
reportedGenes.extend(genes[:2])
reportedGenes = list(set(reportedGenes))
if len(reportedGenes) > 1:
genes = ";".join(reportedGenes)
else:
genes = reportedGenes[0]
terms = transcript2go[ts]
if terms:
allTerms.extend(terms)
if not terms:
terms = "None"
elif len(terms) > 1:
terms = ";".join(terms)
else:
terms = terms[0]
writer.writerow([ts, pvalue, genes, terms])
writer.writerow(["--------"])
## write a summary of the go terms
allTerms = np.array(list(set(allTerms)))
allTermCounts = np.zeros(allTerms.size, )
for t, term in enumerate(allTerms):
for ts in gsTranscripts:
allTermCounts[t] += np.where(
np.array(transcript2go[ts]) == term)[0].size
sortedTerms = allTerms[np.argsort(allTermCounts)[::-1]]
sortedCounts = allTermCounts[np.argsort(allTermCounts)[::-1]]
writer.writerow(["ID", "Counts", "Description"])
for t, term in enumerate(sortedTerms):
desc = session.query(GoTerm).filter(GoTerm.go_id == term).first().name
writer.writerow([term, sortedCounts[t], desc])
if not os.path.exists(psFigureFile):
scr = SpectralClusterResults(silvalFile, clustersFile)
scr.plot(figName=psFigureFile)
## run spectral clustering
k = 20
sigma = 0.08
labelsPath = os.path.join(homeDir, "sc-labels-%s.csv" % (_aspect))
if not os.path.exists(labelsPath):
sc = SpectralCluster(geneDistancePath, dtype='distance')
sc.run(k, sk=None, sigma=sigma, verbose=True)
sc.save(labelsPath=labelsPath)
## Save gene sets
bm = BlastMapper()
bmap = bm.load_summary('blast-parsed-summary.csv',
best=False,
taxaList=['8355', '8364'])
transcriptMin, transcriptMax = 9, 1000
gsFile = os.path.join(homeDir, "%s.gmt" % (_aspect))
if not os.path.exists(gsFile):
gsc = GeneSetCollection(labelsPath, gene2go)
gsc.write(blastMap=bmap,
transcriptMin=transcriptMin,
transcriptMax=transcriptMax,
outFile=gsFile)
print("process complete.")
homeDir = os.path.join(os.path.expanduser("~"), "sequencing", "xenopus")
if source in ['dn', 'gg']:
sourceDir = "%s-trinity" % (source)
elif source == 'ref':
sourceDir = "reference"
else:
raise Exception("Bad source")
summaryFile1 = os.path.join(homeDir, sourceDir,
"blast-%s-parsed_summary.csv" % (source))
summaryFile2 = os.path.join(homeDir, sourceDir, "blast-xt-parsed_summary.csv")
bm = BlastMapper()
## load the gene and isoform maps
bmapIsoforms = bm.load_summary(summaryFile1, trinityGene=False, best=True)
bmapFrog = bm.load_summary(summaryFile1,
trinityGene=False,
best=True,
taxaList=['8355', '8364'])
bmapXT = bm.load_summary(summaryFile2, trinityGene=False, best=True)
print("-----------")
print("SwissProt - isoforms")
bm.print_summary(bmapIsoforms)
print("SwissProt [8355,8364] - isoforms")
bm.print_summary(bmapFrog)
print("X. tropicalis - isoforms")
bm.print_summary(bmapXT)
bm.make_taxa_pie_chart_and_table(
