def safe_sort_index(index: Index) -> Index:
"""
Returns the sorted index
We keep the dtypes and the name attributes.
Parameters
----------
index : an Index
Returns
-------
Index
"""
if index.is_monotonic_increasing:
return index
try:
array_sorted = safe_sort(index)
except TypeError:
pass
else:
array_sorted = cast(np.ndarray, array_sorted)
if isinstance(index, MultiIndex):
index = MultiIndex.from_tuples(array_sorted, names=index.names)
else:
index = Index(array_sorted, name=index.name, dtype=index.dtype)
return index
def analyze_one(nm, genedf, min_pos_reads, min_perc_poss,
min_total_var_support, min_maf, min_samples, min_variants,
gene_seqs, pnpn_groups):
# This method prepares a separate dataframe for each gene (subsampling etc.) and then calculates
# all of the required metrics.
genedf = _prepgene(nm, genedf, min_pos_reads, min_perc_poss,
min_total_var_support, min_maf, min_samples,
min_variants)
if genedf is None:
return [None] * 5
sites, pnps = calc_pnps(genedf, gene_seqs[nm])
percmut = calc_percodon_mut(genedf, gene_seqs[nm])
pnpn = calc_pnpn_all(genedf, gene_seqs[nm], pnpn_groups)
pnpn.index = MultiIndex.from_tuples([(nm, ix[0], ix[1])
for ix in pnpn.index])
for df in ([sites, percmut[0]] if sites is not None else [percmut[0]]):
df.index = MultiIndex.from_product([[nm], df.index])
percmut[1] = Series(percmut[1]).to_frame(nm).T
ffdeg_pw, ffdeg_poss = calc_ffdeg_pi_within(genedf, gene_seqs[nm])
return sites, (pnps.to_frame(nm).T if pnps is not None else pnps), percmut, \
(ffdeg_pw.to_frame(nm).T, ffdeg_poss), pnpn
def apply_counts(ldf):
nm = ldf.index.get_level_values(0)[0]
ldf.index = MultiIndex.from_tuples(ldf.index.get_level_values(1))
ldf = ldf.dropna(how='all', axis=1)
codldf = Series({
i: (cod.loc[nm, i[0]] if i[0] in cod.loc[nm] else 0)
for i in allcodons
})
ldf = codldf.to_frame('cod').join(ldf).drop('cod', axis=1)
for col in ldf.columns:
ldf[col + '_s'] = codldf
return ldf.fillna(0)
def create_codon_trans_matrix(dirsname, tmpdir, grpnm):
chdirmkifnotexist(join(tmpdir, 'tmpmq'))
# TODO: IMPORTANT! Wrap this for loop with your hpc job submission pipeline
for fname in glob(join(dirsname, '*codons*')):
if exists(
join(tmpdir,
grpnm + basename(fname).replace('codons', 'sums'))):
continue
do_one_unite(fname, grpnm, tmpdir)
df = None
for f in glob(join(tmpdir, grpnm + '*sums.df')):
print(basename(f))
if df is None:
df = read_pickle(f)
else:
df = concat([df, read_pickle(f)],
sort=False).groupby(level=[0, 1]).sum()
df.columns = MultiIndex.from_tuples([('per',i) if '_s' not in i \
else ('sums',i.replace('_s','')) for i in df.columns])
sms = df.sums.sum().truediv(3)
def applyfunc(ldf):
ret = ldf.per.truediv(ldf.sums).loc[ldf.name].iloc[:, 0]
ret[ldf.name] = 1 - ret.sum()
return ret
def applyfunc_counts(ldf):
adds = ldf.sums.iloc[0][0]
ret = ldf.per.loc[ldf.name].iloc[:, 0]
ret[ldf.name] = adds - ret.sum()
return ret
df_counts = df.groupby(
level=1,
axis=1).apply(lambda x: x.groupby(level=0).apply(applyfunc_counts))
df_norm = df.groupby(
level=1, axis=1).apply(lambda x: x.groupby(level=0).apply(applyfunc))
aas, _, _ = get_codon_table()
df_aas = df_counts
df_aas['aa_start'] = [aas[a] for a in df_counts.index.get_level_values(0)]
df_aas['aa_end'] = [aas[a2] + ('.' if a1!=a2 and aas[a1]==aas[a2] else '') \
for a1, a2 in \
zip(df_counts.index.get_level_values(0),df_counts.index.get_level_values(1))]
df_aas = df_aas.groupby(['aa_start', 'aa_end']).sum()
df_norm.to_pickle(
join(General.Basepath, grpnm + '_4_60_mutation_codons.df'))
df_counts.to_pickle(
join(General.Basepath, grpnm + '_4_60_mutation_codon_counts.df'))
df_aas.to_pickle(
join(General.Basepath, grpnm + '_4_60_mutation_aas_counts.df'))
sms.to_pickle(join(General.Basepath, grpnm + '_4_60_nucleotide_count.df'))
def _Intercept_2const(df):
from pandas.core.indexes.multi import MultiIndex
if isinstance(df.index, MultiIndex):
new_index = []
for v in df.index.values:
v = list(v)
if 'Intercept' in v:
v[v.index('Intercept')] = 'const'
new_index.append(v)
multi_index = lzip(*new_index)
df.index = MultiIndex.from_arrays(multi_index)
else:
index_value = df.index.tolist()
if 'Intercept' in index_value:
index_value[index_value.index('Intercept')] = 'const'
df.index = index_value
return df
def _Intercept_2const(df):
from pandas.core.indexes.multi import MultiIndex
if df.index.contains('Intercept'):
if isinstance(df.index,MultiIndex):
new_index = []
for i in df.index.values:
i = list(i)
if 'Intercept' in i:
i[i.index('Intercept')] = 'const'
new_index.append(i)
multi_index = lzip(*new_index)
df.index = MultiIndex.from_arrays(multi_index)
else:
index_list = df.index.tolist()
idx = index_list.index('Intercept')
index_list[idx] = 'const'
df.index = index_list
return df
def _Intercept_2const(df):
from pandas.core.indexes.multi import MultiIndex
if "Intercept" in df.index:
if isinstance(df.index, MultiIndex):
new_index = []
for i in df.index.values:
i = list(i)
if "Intercept" in i:
i[i.index("Intercept")] = "const"
new_index.append(i)
multi_index = lzip(*new_index)
df.index = MultiIndex.from_arrays(multi_index)
else:
index_list = df.index.tolist()
idx = index_list.index("Intercept")
index_list[idx] = "const"
df.index = index_list
return df
def calc_pnpn_all(genedf, geneseq, pnpn_groups):
# LIAT:
# Here we calculate pN(group1)/pN(group2) for all different groups that we define below.
# We start by taking the consensus sequence:
consensus = _get_consensus(genedf, geneseq)
if consensus is None:
return None
# Splitting to codons
cons_codons = [consensus[i:i + 3] for i in range(0, len(consensus), 3)]
# Separately we normalize by the numebr of reads in each poisition to get a relative abundance
# of each SNP
normdf = genedf.groupby(level=1).apply(lambda x: x.truediv(x.sum(0)))
ret = []
for nm, (g1, g2, nononymity) in pnpn_groups.items():
# Call a calculation for each condition
res = calc_pnpn_one(cons_codons, normdf, g1, g2, nononymity)
# Organize with the constructed name as index
res.index = MultiIndex.from_tuples([(nm, i) for i in res.index])
ret.append(res)
return concat(ret, sort=False)
def _multiindex(self) -> MultiIndex:
return MultiIndex.from_arrays([self.left, self.right], names=["left", "right"])
def _multiindex(self):
return MultiIndex.from_arrays([self.left, self.right],
names=['left', 'right'])
def _multiindex(self):
return MultiIndex.from_arrays([self.left, self.right],
names=['left', 'right'])
