def test_dataframe(self):
g = Genome('hg18')
kg = g.dataframe('cpgIslandExt')
self.assert_(kg.shape[0] == g.cpgIslandExt.count())
q = g.cpgIslandExt.filter(g.cpgIslandExt.chromStart < 300000).limit(10)
df = g.dataframe(q)
self.assert_(df.shape[0] == 10)
def test_dataframe(self):
g = Genome('hg18')
kg = g.dataframe('cpgIslandExt')
self.assert_(kg.shape[0] == g.cpgIslandExt.count())
q = g.cpgIslandExt.filter(g.cpgIslandExt.chromStart < 300000).limit(10)
df = g.dataframe(q)
self.assert_(df.shape[0] == 10)
new_names.extend(names[1:])
print "\n", new_names
vcf_ex.columns = new_names
# If QUAL > 0.5, sample passes
vcf_ex_sub = vcf_ex.loc[vcf_ex.QUAL > 0.5, ['CHROM', 'POS']].copy()
print vcf_ex_sub.head()
# Get the Genome object from cruzdb
# connects to MySQL genome browser at UCSC
g = Genome('hg38')
# Convert table 'refGene' to pandas dataframe
# columns of interest 'chrom' (chrX, %s), 'txStart' (number, %s), 'txEnd' (number , %s)
print "Extracting reference genome table (HG38) from UCSC Genome Browser"
df = g.dataframe('refGene')
df[['txStart', 'txEnd']] = df[['txStart', 'txEnd']].astype(int)
genes = pd.Series(np.zeros(vcf_ex_sub.shape[0]))
#gene = hg19.bin_query('refGene', vcf_ex_sub.CHROM[1], vcf_ex_sub.POS[1], vcf_ex_sub.POS[1])
#print vcf_ex_sub.POS.iloc[0]
for i in range(0, vcf_ex_sub.shape[0]):
#genes[i] = df[[df.chrom == str(vcf_ex_sub.CHROM.iloc[i]) and df.txStart >= str(vcf_ex_sub.POS.iloc[i])]].bool() #and df.txStart >= str(vcf_ex_sub.POS.iloc[i]) and df.txEnd <= vcf_ex_sub.POS.iloc[i]].bool(),
chrom = vcf_ex_sub['CHROM'].iloc[i]
location = vcf_ex_sub['POS'].iloc[i]
#print i, chrom, location
tmp = df.copy()
tmp = tmp[tmp.chrom == chrom]
tmp = tmp[tmp['txStart'] <= location]
new_names.extend(names[1:])
print "\n", new_names
vcf_ex.columns = new_names
# If QUAL > 0.5, sample passes
vcf_ex_sub = vcf_ex.loc[vcf_ex.QUAL > 0.5, ['CHROM', 'POS']].copy()
print vcf_ex_sub.head()
# Get the Genome object from cruzdb
# connects to MySQL genome browser at UCSC
g = Genome('hg38')
# Convert table 'refGene' to pandas dataframe
# columns of interest 'chrom' (chrX, %s), 'txStart' (number, %s), 'txEnd' (number , %s)
print "Extracting reference genome table (HG38) from UCSC Genome Browser"
df = g.dataframe('refGene')
df[['txStart', 'txEnd']] = df[['txStart', 'txEnd']].astype(int)
genes = pd.Series(np.zeros(vcf_ex_sub.shape[0]))
#gene = hg19.bin_query('refGene', vcf_ex_sub.CHROM[1], vcf_ex_sub.POS[1], vcf_ex_sub.POS[1])
#print vcf_ex_sub.POS.iloc[0]
for i in range(0, vcf_ex_sub.shape[0]):
#genes[i] = df[[df.chrom == str(vcf_ex_sub.CHROM.iloc[i]) and df.txStart >= str(vcf_ex_sub.POS.iloc[i])]].bool() #and df.txStart >= str(vcf_ex_sub.POS.iloc[i]) and df.txEnd <= vcf_ex_sub.POS.iloc[i]].bool(),
chrom = vcf_ex_sub['CHROM'].iloc[i]
location = vcf_ex_sub['POS'].iloc[i]
#print i, chrom, location
tmp = df.copy()
tmp = tmp[tmp.chrom == chrom]
tmp = tmp[tmp['txStart'] <= location]
tmp = tmp[tmp['txEnd'] >= location]
