def fast_enrich(D, M, alpha):
# any bool any(array)
D = D / ('id', 'class1', 'class2')
D = D.To(_.class1, Do=_.Cast(bool))
D = D.ReplaceMissing()
Dflat = D.FlatAll()
Dgroupclass2 = Dflat.GroupBy(_.class2)
Nids = D.id.Unique().Shape()()
Nclass2 = Dgroupclass2.class2.Shape()()
Ntrue = D[_.class1].id.Shape()()
Nfalse = Nids - Ntrue
a = Dgroupclass2[_.class1].id.Shape().Get(1)()
c = Dgroupclass2[~_.class1].id.Shape().Get(1)()
b = [(Ntrue - x) for x in a]
d = [(Nfalse - x) for x in c]
sys.stdout.flush()
print "Ntrue: ", Ntrue, " | Nfalse: ", Nfalse
print "Nids: ", Nids
if any(numpy.concatenate((a, b, c, d)) < 0):
print "ERROR, SOMETHING WEIRD I CAN't EXPLAIN YET!"
p = [1.0 for i in xrange(Nclass2)]
else:
p = [
ssp.fisher_exact([[a[i] + 1, b[i] + 1], [c[i] + 1, d[i] + 1]])[1]
for i in xrange(Nclass2)
]
#fi
# Benjamini-Hochberg procedure
q = mtc.fdr_bh(p, alpha)
T = zip(Dgroupclass2.class2(), a, b, c, d, p, q)
R = Rep(T) / ('annotation_id', 'a', 'b', 'c', 'd', 'pvalue', 'qvalue')
if M is not None:
R = R | Match(0, 0, merge_same="equi") | M
#fi
return R.Copy()
def fast_enrich(D, M, alpha):
# any bool any(array)
D = D / ('id', 'class1', 'class2');
D = D.To(_.class1, Do=_.Cast(bool));
D = D.ReplaceMissing();
Dflat = D.FlatAll();
Dgroupclass2 = Dflat.GroupBy(_.class2);
Nids = D.id.Unique().Shape()();
Nclass2 = Dgroupclass2.class2.Shape()();
Ntrue = D[_.class1].id.Shape()();
Nfalse = Nids - Ntrue;
a = Dgroupclass2[_.class1].id.Shape().Get(1)();
c = Dgroupclass2[~_.class1].id.Shape().Get(1)();
b = [ (Ntrue - x) for x in a ];
d = [ (Nfalse - x) for x in c ];
sys.stdout.flush()
print "Ntrue: ", Ntrue, " | Nfalse: ", Nfalse;
print "Nids: ", Nids;
if any(numpy.concatenate((a,b,c,d)) < 0):
print "ERROR, SOMETHING WEIRD I CAN't EXPLAIN YET!";
p = [ 1.0 for i in xrange(Nclass2) ];
else:
p = [ ssp.fisher_exact([ [a[i]+1,b[i]+1], [c[i]+1,d[i]+1]])[1] for i in xrange(Nclass2)];
#fi
# Benjamini-Hochberg procedure
q = mtc.fdr_bh(p, alpha);
T = zip(Dgroupclass2.class2(), a, b, c, d, p, q);
R = Rep(T) / ('annotation_id', 'a', 'b', 'c', 'd', 'pvalue', 'qvalue');
if M is not None:
R = R | Match(0, 0, merge_same="equi") | M;
#fi
return R.Copy();
def fast_enrich_sample(D, M, alpha, all_or_up_or_down='all'):
# TERM NTERM
# +-----+-----+
# DIFF | a | b | ND
# +-----+-----+
# NDIFF | c | d | NND
# +-----+-----+
# D.0 = test_id
# D.1 = significant
# D.2 = annotation_id
output_slice_names = ('annotation_id', 'a', 'b', 'c', 'd', 'pvalue',
'qvalue')
D = D / ('test_id', 'significant', 'logfold', 'annotation_id')
D = D.To(_.significant, Do=_.Cast('bytes'))
D = D.To(_.logfold, Do=_.Cast('real64'))
D = D.To(_.significant, _.logfold, Do=_.ReplaceMissing())
if D.annotation_id.Shape().Get(1).Sum()() == 0:
S = Rep(tuple([0 for x in output_slice_names])) / output_slice_names
return S[_.test_id > 0]
#fi
# The data grouped by annotation ids
Df = D.FlatAll()
Dg = Df.GroupBy(_.annotation_id)
# The total number of genes and terms
NG = D.test_id.Unique().Shape()()
NT = Dg.annotation_id.Shape()()
# ND: The number of significant test_ids
# a: The set of significant genes
# c: The set of non-significant genes
if all_or_up_or_down == 'all':
# ND: The number of significant test_ids (differentially expressed) ND = Number Diff
# a: The set of significant genes
# c: The set of non-significant genes
ND = D[_.significant == 'yes'].test_id.Shape()()
a = Dg[_.significant == 'yes']
c = Dg[~(_.significant == 'yes')]
elif all_or_up_or_down == 'up':
# ND: The number of significant test_ids which are upregulated
# a: The set of significant genes which are upregulated
# c: The set of genes which are non-significant or are not up-regulated
ND = D[(_.significant == 'yes') & (_.logfold > 0)].test_id.Shape()()
a = Dg[(_.significant == 'yes') & (_.logfold > 0)]
c = Dg[~((_.significant == 'yes') & (_.logfold > 0))]
elif all_or_up_or_down == 'down':
# ND: The number of significant test_ids which are downregulated
# a: The set of significant genes which are downregulated
# c: The set of genes which are non-significant or are not downregulated
ND = D[(_.significant == 'yes') & (_.logfold < 0)].test_id.Shape()()
a = Dg[(_.significant == 'yes') & (_.logfold < 0)]
c = Dg[~((_.significant == 'yes') & (_.logfold < 0))]
#fi
# The number of genes which are not significant and not up or downregulated
NND = NG - ND
a = a.test_id.Shape().Get(1)()
c = c.test_id.Shape().Get(1)()
b = [(ND - x) for x in a]
d = [(NND - x) for x in c]
T = Dg.annotation_id()
p = [
ssp.fisher_exact([[a[i] + 1, b[i] + 1], [c[i] + 1, d[i] + 1]])[1]
for i in xrange(NT)
]
# Benjamini-Hochberg procedure
q = mtc.fdr_bh(p, alpha)
T = zip(T, a, b, c, d, p, q)
R = Rep(T) / ('annotation_id', 'a', 'b', 'c', 'd', 'pvalue', 'qvalue')
if M is not None:
R = R | Match(0, 0, merge_same="equi") | M
#fi
return R.Copy()
def fast_enrich_sample(D, M, alpha, all_or_up_or_down='all'):
# TERM NTERM
# +-----+-----+
# DIFF | a | b | ND
# +-----+-----+
# NDIFF | c | d | NND
# +-----+-----+
# D.0 = test_id
# D.1 = significant
# D.2 = annotation_id
output_slice_names = ('annotation_id', 'a', 'b', 'c', 'd', 'pvalue', 'qvalue');
D = D / ('test_id', 'significant', 'logfold', 'annotation_id');
D = D.To(_.significant, Do=_.Cast('bytes'));
D = D.To(_.logfold, Do=_.Cast('real64'));
D = D.To(_.significant, _.logfold, Do=_.ReplaceMissing());
if D.annotation_id.Shape().Get(1).Sum()() == 0:
S = Rep(tuple([0 for x in output_slice_names])) / output_slice_names;
return S[_.test_id > 0];
#fi
# The data grouped by annotation ids
Df = D.FlatAll();
Dg = Df.GroupBy(_.annotation_id);
# The total number of genes and terms
NG = D.test_id.Unique().Shape()();
NT = Dg.annotation_id.Shape()();
# ND: The number of significant test_ids
# a: The set of significant genes
# c: The set of non-significant genes
if all_or_up_or_down == 'all':
# ND: The number of significant test_ids (differentially expressed) ND = Number Diff
# a: The set of significant genes
# c: The set of non-significant genes
ND = D[_.significant == 'yes'].test_id.Shape()();
a = Dg[ _.significant == 'yes' ];
c = Dg[~(_.significant == 'yes')];
elif all_or_up_or_down == 'up':
# ND: The number of significant test_ids which are upregulated
# a: The set of significant genes which are upregulated
# c: The set of genes which are non-significant or are not up-regulated
ND = D[ ( _.significant == 'yes' ) & ( _.logfold > 0 ) ].test_id.Shape()();
a = Dg[ ( _.significant == 'yes' ) & ( _.logfold > 0 ) ];
c = Dg[~(( _.significant == 'yes' ) & ( _.logfold > 0 )) ];
elif all_or_up_or_down == 'down':
# ND: The number of significant test_ids which are downregulated
# a: The set of significant genes which are downregulated
# c: The set of genes which are non-significant or are not downregulated
ND = D[ ( _.significant == 'yes' ) & ( _.logfold < 0 ) ].test_id.Shape()();
a = Dg[ ( _.significant == 'yes' ) & ( _.logfold < 0 ) ];
c = Dg[~(( _.significant == 'yes' ) & ( _.logfold < 0 )) ];
#fi
# The number of genes which are not significant and not up or downregulated
NND = NG - ND;
a = a.test_id.Shape().Get(1)();
c = c.test_id.Shape().Get(1)();
b = [ (ND - x) for x in a ];
d = [ (NND - x) for x in c ];
T = Dg.annotation_id();
p = [ ssp.fisher_exact([ [a[i]+1,b[i]+1], [c[i]+1,d[i]+1]])[1] for i in xrange(NT)];
# Benjamini-Hochberg procedure
q = mtc.fdr_bh(p, alpha);
T = zip(T, a, b, c, d, p, q);
R = Rep(T) / ('annotation_id', 'a', 'b', 'c', 'd', 'pvalue', 'qvalue');
if M is not None:
R = R | Match(0, 0, merge_same="equi") | M;
#fi
return R.Copy();
