code string | signature string | docstring string | loss_without_docstring float64 | loss_with_docstring float64 | factor float64 |
|---|---|---|---|---|---|
# make sure provide context is valid
if not self.is_valid_context(context):
error_msg = 'Context ({0}) was never seen in sequence.'.format(context)
raise ValueError(error_msg)
# make sure sampling is a positive integer
if num < 1:
error_msg = ('There must be at least one sample (specified {0}) '
'for a context'.format(num))
raise ValueError(error_msg)
# randomly select from available positions that fit the specified context
available_pos = self.context2pos[context]
random_pos = self.prng_dict[context].choice(available_pos, (num_permutations, num))
return random_pos | def random_context_pos(self, num, num_permutations, context) | Samples with replacement available positions matching the
sequence context.
Note: this method does random sampling only for an individual
sequence context.
Parameters
----------
num : int
Number of positions to sample for each permutation. This
is the number of actually observed mutations having the
matching sequence context for this gene.
num_permutations : int
Number of permutations for permutation test.
context : str
Sequence context.
Returns
-------
random_pos : np.array
num_permutations X num sized array that represents the
randomly sampled positions for a specific context. | 4.753926 | 4.721913 | 1.00678 |
position_list = []
for contxt, n in context_iterable:
pos_array = self.random_context_pos(n, num_permutations, contxt)
position_list.append([contxt, pos_array])
return position_list | def random_pos(self, context_iterable, num_permutations) | Obtains random positions w/ replacement which match sequence context.
Parameters
----------
context_iterable: iterable containing two element tuple
Records number of mutations in each context. context_iterable
should be something like [('AA', 5), ...].
num_permutations : int
Number of permutations used in the permutation test.
Returns
-------
position_list : list
Contains context string and the randomly chosen positions
for that context. | 3.773666 | 3.574734 | 1.055649 |
chroms = sorted(bed_dict.keys())
multiprocess_flag = opts['processes']>0
if multiprocess_flag:
num_processes = opts['processes']
else:
num_processes = 1
num_permutations = opts['num_permutations']
if not opts['by_sample']:
obs_result = []
else:
uniq_samp = mut_df['Tumor_Sample'].unique()
obs_result = pd.DataFrame(np.zeros((len(uniq_samp), len(cols))),
index=uniq_samp, columns=cols)
# initialize list containing output
if not opts['score_dir']:
result_list = [[0, 0, 0, 0, 0, 0, 0] for k in range(num_permutations)]
else:
result_list = [[0, 0, 0, 0, 0, 0, 0, 0, 0] for k in range(num_permutations)]
# iterate over each chromosome
for i in range(0, len(chroms), num_processes):
if multiprocess_flag:
pool = Pool(processes=num_processes)
tmp_num_proc = len(chroms) - i if i + num_processes > len(chroms) else num_processes
info_repeat = ((bed_dict[chroms[tmp_ix]], mut_df, opts)
for tmp_ix in range(i, i+tmp_num_proc))
process_results = pool.imap(singleprocess_permutation, info_repeat)
process_results.next = utils.keyboard_exit_wrapper(process_results.next)
try:
for chrom_result, obs_mutations in process_results:
for j in range(num_permutations):
result_list[j][0] += chrom_result[j][0]
result_list[j][1] += chrom_result[j][1]
result_list[j][2] += chrom_result[j][2]
result_list[j][3] += chrom_result[j][3]
result_list[j][4] += chrom_result[j][4]
result_list[j][5] += chrom_result[j][5]
result_list[j][6] += chrom_result[j][6]
if opts['score_dir']:
result_list[j][7] += chrom_result[j][7]
result_list[j][8] += chrom_result[j][8]
if not opts['by_sample']:
obs_result.append(obs_mutations)
else:
obs_result = obs_result + obs_mutations
except KeyboardInterrupt:
pool.close()
pool.join()
logger.info('Exited by user. ctrl-c')
sys.exit(0)
pool.close()
pool.join()
else:
info = (bed_dict[chroms[i]], mut_df, opts)
chrom_result, obs_mutations = singleprocess_permutation(info)
for j in range(num_permutations):
result_list[j][0] += chrom_result[j][0]
result_list[j][1] += chrom_result[j][1]
result_list[j][2] += chrom_result[j][2]
result_list[j][3] += chrom_result[j][3]
result_list[j][4] += chrom_result[j][4]
result_list[j][5] += chrom_result[j][5]
result_list[j][6] += chrom_result[j][6]
if opts['score_dir']:
result_list[j][7] += chrom_result[j][7]
result_list[j][8] += chrom_result[j][8]
if not opts['by_sample']:
obs_result.append(obs_mutations)
else:
obs_result = obs_result + obs_mutations
return result_list, obs_result | def multiprocess_permutation(bed_dict, mut_df, opts) | Handles parallelization of permutations by splitting work
by chromosome. | 1.810291 | 1.809255 | 1.000572 |
# get variant types
#var_class = cutils.get_variant_classification(germ_aa, somatic_aa, codon_pos)
# get information about MGA entropy
mga_path = os.path.join(sdir, gname+".mgaentropy.pickle")
if os.path.exists(mga_path):
if sys.version_info < (3,):
# python 2.7 way
with open(mga_path) as handle:
mga_ent = pickle.load(handle)
else:
# python 3.X way
with open(mga_path, 'rb') as handle:
mga_ent = pickle.load(handle, encoding='latin-1')
else:
mga_ent = None
missense_pos = [p for i, p in enumerate(codon_pos)
if (germ_aa[i]!=somatic_aa[i]) and
(germ_aa[i] not in ['-', '*', 'Splice_Site']) and
(somatic_aa[i] not in ['-', '*', 'Splice_Site'])]
total_mga_ent = compute_mga_entropy_stat(mga_ent, missense_pos, sum, default_mga)
#mga_ent_ixs = [codon_pos[i] for i in range(len(var_class))
#if var_class[i] == 'Missense_Mutation']
#len_mga_ent = len(mga_ent)
#mga_ent_scores = [mga_ent[ix] for ix in mga_ent_ixs if ix < len_mga_ent]
#if mga_ent_scores:
#total_mga_ent = sum(mga_ent_scores)
#else:
#total_mga_ent = default_mga
#else:
#total_mga_ent = no_file_flag
# get information about VEST scores
vest_path = os.path.join(sdir, gname+".vest.pickle")
if os.path.exists(vest_path):
if sys.version_info < (3,):
# python 2.7 way
with open(vest_path) as handle:
vest_score = pickle.load(handle)
else:
# python 3.X way
with open(vest_path, 'rb') as handle:
vest_score = pickle.load(handle, encoding='latin-1')
else:
vest_score = None
total_vest = compute_vest_stat(vest_score,
germ_aa, somatic_aa, codon_pos,
stat_func=sum, default_val=default_vest)
#vest_scores = [vest_score.get(codon_pos[i]+1, {}).get(germ_aa[i], {}).get(somatic_aa[i], default_vest)
#for i in range(len(var_class))
#if var_class[i] == 'Missense_Mutation']
#total_vest = sum(vest_scores)
#else:
#total_vest = no_file_flag
return total_mga_ent, total_vest | def retrieve_scores(gname, sdir,
codon_pos, germ_aa, somatic_aa,
default_mga=5., default_vest=0,
no_file_flag=-1) | Retrieves scores from pickle files.
Used by summary script. | 2.046449 | 2.063861 | 0.991563 |
vest_path = os.path.join(score_dir, gname+".vest.pickle")
if os.path.exists(vest_path):
if sys.version_info < (3,):
with open(vest_path) as handle:
gene_vest = pickle.load(handle)
else:
with open(vest_path, 'rb') as handle:
gene_vest = pickle.load(handle, encoding='latin-1')
return gene_vest
else:
return None | def read_vest_pickle(gname, score_dir) | Read in VEST scores for given gene.
Parameters
----------
gname : str
name of gene
score_dir : str
directory containing vest scores
Returns
-------
gene_vest : dict or None
dict containing vest scores for gene. Returns None if not found. | 1.895329 | 1.88679 | 1.004526 |
# return default value if VEST scores are missing
if vest_dict is None:
return default_val
# fetch scores
myscores = fetch_vest_scores(vest_dict, ref_aa, somatic_aa, codon_pos)
# calculate mean score
if myscores:
score_stat = stat_func(myscores)
else:
score_stat = default_val
return score_stat | def compute_vest_stat(vest_dict, ref_aa, somatic_aa, codon_pos,
stat_func=np.mean,
default_val=0.0) | Compute missense VEST score statistic.
Note: non-missense mutations are intentially not filtered out and will take
a default value of zero.
Parameters
----------
vest_dict : dict
dictionary containing vest scores across the gene of interest
ref_aa: list of str
list of reference amino acids
somatic_aa: list of str
somatic mutation aa
codon_pos : list of int
position of codon in protein sequence
stat_func : function, default=np.mean
function that calculates a statistic
default_val : float
default value to return if there are no mutations
Returns
-------
score_stat : float
vest score statistic for provided mutation list | 2.886148 | 2.945785 | 0.979755 |
# return default value if VEST scores are missing
if mga_vec is None:
return default_val
# fetch scores
myscores = fetch_mga_scores(mga_vec, codon_pos)
# calculate mean score
if myscores is not None and len(myscores):
score_stat = stat_func(myscores)
else:
score_stat = default_val
return score_stat | def compute_mga_entropy_stat(mga_vec, codon_pos,
stat_func=np.mean,
default_val=0.0) | Compute MGA entropy conservation statistic
Parameters
----------
mga_vec : np.array
numpy vector containing MGA Entropy conservation scores for residues
codon_pos : list of int
position of codon in protein sequence
stat_func : function, default=np.mean
function that calculates a statistic
default_val : float
default value to return if there are no mutations
Returns
-------
score_stat : float
MGA entropy score statistic for provided mutation list | 3.712401 | 3.721551 | 0.997541 |
vest_score_list = []
for i in range(len(somatic_aa)):
# make sure position is valid
if codon_pos[i] is not None:
tmp_score = vest_dict.get(codon_pos[i]+1, {}).get(ref_aa[i], {}).get(somatic_aa[i], default_vest)
else:
tmp_score = 0.0
vest_score_list.append(tmp_score)
return vest_score_list | def fetch_vest_scores(vest_dict,
ref_aa, somatic_aa, codon_pos,
default_vest=0.0) | Get VEST scores from pre-computed scores in dictionary.
Note: either all mutations should be missense or non-missense intended
to have value equal to default.
Parameters
----------
vest_dict : dict
dictionary containing vest scores across the gene of interest
ref_aa: list of str
list of reference amino acids
somatic_aa: list of str
somatic mutation aa
codon_pos: list of int
position of codon in protein sequence
default_vest: float, default=0.0
value to use if VEST score not available for a given mutation
Returns
-------
vest_score_list: list
score results for mutations | 2.159358 | 2.330405 | 0.926602 |
# keep only positions in range of MGAEntropy scores
len_mga = len(mga_vec)
good_codon_pos = [p for p in codon_pos if p < len_mga]
# get MGAEntropy scores
if good_codon_pos:
mga_ent_scores = mga_vec[good_codon_pos]
else:
mga_ent_scores = None
return mga_ent_scores | def fetch_mga_scores(mga_vec,
codon_pos,
default_mga=None) | Get MGAEntropy scores from pre-computed scores in array.
Parameters
----------
mga_vec : np.array
numpy vector containing MGA Entropy conservation scores for residues
codon_pos: list of int
position of codon in protein sequence
default_mga: float or None, default=None
value to use if MGA entropy score not available for a given mutation.
Drop mutations if no default specified.
Returns
-------
mga_ent_scores : np.array
score results for MGA entropy conservation | 3.316526 | 2.980897 | 1.112593 |
graph_path = os.path.join(graph_dir, gname+".pickle")
if os.path.exists(graph_path):
with open(graph_path) as handle:
gene_graph = pickle.load(handle)
return gene_graph
else:
return None | def read_neighbor_graph_pickle(gname, graph_dir) | Read in neighbor graph for given gene.
Parameters
----------
gname : str
name of gene
graph_dir : str
directory containing gene graphs
Returns
-------
gene_graph : dict or None
neighbor graph as dict for gene. Returns None if not found. | 2.180921 | 2.116139 | 1.030613 |
# skip if there are no missense mutations
if not len(pos_ct):
return 1.0, 0
max_pos = max(gene_graph)
codon_vals = np.zeros(max_pos+1)
# smooth out mutation counts
for pos in pos_ct:
mut_count = pos_ct[pos]
# update neighbor values
neighbors = list(gene_graph[pos])
num_neighbors = len(neighbors)
codon_vals[neighbors] += alpha*mut_count
# update self-value
codon_vals[pos] += (1-alpha)*mut_count
# compute the normalized entropy
#total_cts = float(np.count_nonzero(codon_vals))
#graph_score = mymath.normalized_mutation_entropy(codon_vals, total_cts=total_cts)
# compute regular entropy
p = codon_vals / np.sum(codon_vals)
graph_score = mymath.shannon_entropy(p)
# get coverage
coverage = np.count_nonzero(p)
return graph_score, coverage | def compute_ng_stat(gene_graph, pos_ct, alpha=.5) | Compute the clustering score for the gene on its neighbor graph.
Parameters
----------
gene_graph : dict
Graph of spatially near codons. keys = nodes, edges = key -> value.
pos_ct : dict
missense mutation count for each codon
alpha : float
smoothing factor
Returns
-------
graph_score : float
score measuring the clustering of missense mutations in the graph
coverage : int
number of nodes that received non-zero weight | 3.844071 | 3.449 | 1.114547 |
if to_zero_based:
mut_df['Start_Position'] = mut_df['Start_Position'] - 1
fs_cts = {} # frameshift count information for each gene
fs_df = indel.keep_frameshifts(mut_df)
for bed in utils.bed_generator(bed_path):
gene_df = fs_df[fs_df['Gene']==bed.gene_name]
# find it frameshift actually is on gene annotation
fs_pos = []
for ix, row in gene_df.iterrows():
indel_pos = [row['Start_Position'], row['End_Position']]
coding_pos = bed.query_position(bed.strand, row['Chromosome'], indel_pos)
fs_pos.append(coding_pos)
# mark frameshifts that could not be mapped to reference tx
gene_df['unmapped'] = [(1 if x is None else 0) for x in fs_pos]
total_fs = len(gene_df)
unmapped_fs = len(gene_df[gene_df['unmapped']==1])
# filter out frameshifts that did not match reference tx
if not use_unmapped:
gene_df = gene_df[gene_df['unmapped']==0]
total_fs -= unmapped_fs
info = [total_fs, unmapped_fs,]
fs_cts[bed.gene_name] = info
# prepare counts into a dataframe
fs_cts_df = pd.DataFrame.from_dict(fs_cts, orient='index')
cols = ['total', 'unmapped',]
fs_cts_df.columns = cols
return fs_cts_df | def count_frameshift_total(mut_df,
bed_path,
use_unmapped=False,
to_zero_based=False) | Count frameshifts for each gene.
Parameters
----------
mut_df : pd.DataFrame
mutation input
bed_path : str
path to BED file containing reference tx for genes
use_unmapped : Bool
flag indicating whether to include frameshifts not mapping
to reference tx
to_zero_based : Bool
whether to convert end-coordinate to zero based for analysis
Returns
-------
fs_cts_df : pd.DataFrame
contains both total frameshift counts and frameshift counts
not mappable to the reference transcript. | 3.194492 | 3.073259 | 1.039448 |
if strand == '-':
ss_seq = fasta.fetch(reference=chrom,
start=end-1,
end=end+3)
ss_seq = utils.rev_comp(ss_seq)
elif strand == '+':
ss_seq = fasta.fetch(reference=chrom,
start=start-3,
end=start+1)
ss_fasta = '>{0};exon{1};3SS\n{2}\n'.format(gene_name,
exon_num,
ss_seq.upper())
return ss_fasta | def _fetch_3ss_fasta(fasta, gene_name, exon_num,
chrom, strand, start, end) | Retreives the 3' SS sequence flanking the specified exon.
Returns a string in fasta format with the first line containing
a ">" and the second line contains the two base pairs of 3' SS.
Parameters
----------
fasta : pysam.Fastafile
fasta object from pysam
gene_name : str
gene name used for fasta seq id
exon_num : int
the `exon_num` exon, used for seq id
chrom : str
chromsome
strand : str
strand, {'+', '-'}
start : int
0-based start position
end : int
0-based end position
Returns
-------
ss_fasta : str
string in fasta format with first line being seq id | 2.515972 | 2.424369 | 1.037784 |
gene_fasta = ''
strand = gene_bed.strand
exons = gene_bed.get_exons()
if strand == '-':
exons.reverse() # order exons 5' to 3', so reverse if '-' strand
# iterate over exons
for i, exon in enumerate(exons):
exon_seq = fasta_obj.fetch(reference=gene_bed.chrom,
start=exon[0],
end=exon[1]).upper()
if strand == '-':
exon_seq = utils.rev_comp(exon_seq)
exon_fasta = '>{0};exon{1}\n{2}\n'.format(gene_bed.gene_name,
i, exon_seq)
# get splice site sequence
if len(exons) == 1:
# splice sites don't matter if there is no splicing
ss_fasta = ''
elif i == 0:
# first exon only, get 3' SS
ss_fasta = _fetch_5ss_fasta(fasta_obj, gene_bed.gene_name, i,
gene_bed.chrom, strand, exon[0], exon[1])
elif i == (len(exons) - 1):
# last exon only, get 5' SS
ss_fasta = _fetch_3ss_fasta(fasta_obj, gene_bed.gene_name, i,
gene_bed.chrom, strand, exon[0], exon[1])
else:
# middle exon, get bot 5' and 3' SS
fasta_3ss = _fetch_3ss_fasta(fasta_obj, gene_bed.gene_name, i,
gene_bed.chrom, strand, exon[0], exon[1])
fasta_5ss = _fetch_5ss_fasta(fasta_obj, gene_bed.gene_name, i,
gene_bed.chrom, strand, exon[0], exon[1])
ss_fasta = fasta_5ss + fasta_3ss
gene_fasta += exon_fasta + ss_fasta
return gene_fasta | def fetch_gene_fasta(gene_bed, fasta_obj) | Retreive gene sequences in FASTA format.
Parameters
----------
gene_bed : BedLine
BedLine object representing a single gene
fasta_obj : pysam.Fastafile
fasta object for index retreival of sequence
Returns
-------
gene_fasta : str
sequence of gene in FASTA format | 2.087579 | 2.104424 | 0.991996 |
exon_seq_list, five_ss_seq_list, three_ss_seq_list = self._fetch_seq()
self.exon_seq = ''.join(exon_seq_list)
self.three_prime_seq = three_ss_seq_list
self.five_prime_seq = five_ss_seq_list
self._to_upper() | def _reset_seq(self) | Updates attributes for gene represented in the self.bed attribute.
Sequences are always upper case. | 3.717751 | 3.165327 | 1.174523 |
if len(germline_nucs) != len(coding_pos):
raise ValueError('Each germline nucleotide should have a coding position')
es = list(self.exon_seq)
for i in range(len(germline_nucs)):
gl_nuc, cpos = germline_nucs[i].upper(), coding_pos[i]
if not utils.is_valid_nuc(gl_nuc):
raise ValueError('{0} is not a valid nucleotide'.format(gl_nuc))
if cpos >= 0:
es[cpos] = gl_nuc
self.exon_seq = ''.join(es) | def add_germline_variants(self, germline_nucs, coding_pos) | Add potential germline variants into the nucleotide sequence.
Sequenced individuals may potentially have a SNP at a somatic mutation position.
Therefore they may differ from the reference genome. This method updates the gene
germline gene sequence to match the actual individual.
Parameters
----------
germline_nucs : list of str
list of DNA nucleotides containing the germline letter
coding_pos : int
0-based nucleotide position in coding sequence
NOTE: the self.exon_seq attribute is updated, no return value | 2.394924 | 2.360155 | 1.014732 |
self.exon_seq = self.exon_seq.upper()
self.three_prime_seq = [s.upper() for s in self.three_prime_seq]
self.five_prime_seq = [s.upper() for s in self.five_prime_seq] | def _to_upper(self) | Convert sequences to upper case. | 2.426005 | 2.142308 | 1.132426 |
exons = []
three_prime_ss = []
five_prime_ss = []
num_exons = self.bed.get_num_exons()
for i in range(num_exons):
# add exon sequence
tmp_id = '{0};exon{1}'.format(self.bed.gene_name, i)
tmp_exon = self.fasta.fetch(reference=tmp_id)
exons.append(tmp_exon)
# add splice site sequence
tmp_id_3ss = '{0};3SS'.format(tmp_id)
tmp_id_5ss = '{0};5SS'.format(tmp_id)
if num_exons == 1:
pass
elif i == 0:
tmp_5ss = self.fasta.fetch(tmp_id_5ss)
five_prime_ss.append(tmp_5ss)
elif i == (num_exons - 1):
tmp_3ss = self.fasta.fetch(tmp_id_3ss)
three_prime_ss.append(tmp_3ss)
else:
tmp_3ss = self.fasta.fetch(tmp_id_3ss)
tmp_5ss = self.fasta.fetch(tmp_id_5ss)
three_prime_ss.append(tmp_3ss)
five_prime_ss.append(tmp_5ss)
return exons, five_prime_ss, three_prime_ss | def _fetch_seq(self) | Fetches gene sequence from PySAM fasta object.
Returns
-------
exons : list of str
list of exon nucleotide sequences
five_prime_ss : list of str
list of 5' splice site sequences
three_prime_ss : list of str
list of 3' splice site sequences | 1.89952 | 1.7084 | 1.111871 |
chrom_list = []
for chrom in chroms:
# fix chrom numbering
chrom = str(chrom)
chrom = chrom.replace('23', 'X')
chrom = chrom.replace('24', 'Y')
chrom = chrom.replace('25', 'Mt')
if not chrom.startswith('chr'):
chrom = 'chr' + chrom
chrom_list.append(chrom)
return chrom_list | def correct_chrom_names(chroms) | Make sure chromosome names follow UCSC chr convention. | 2.135382 | 2.122339 | 1.006146 |
pvals = np.asarray(pvals)
degrees_of_freedom = 2 * pvals.size
chisq_stat = np.sum(-2*np.log(pvals))
fishers_pval = stats.chi2.sf(chisq_stat, degrees_of_freedom)
return fishers_pval | def fishers_method(pvals) | Fisher's method for combining independent p-values. | 2.444413 | 2.45075 | 0.997414 |
for i in range(1, len(x)):
if x[i-1] < x[i]:
x[i] = x[i-1]
return x | def cummin(x) | A python implementation of the cummin function in R | 1.857548 | 2.03298 | 0.913707 |
pval_array = np.array(pval)
sorted_order = np.argsort(pval_array)
original_order = np.argsort(sorted_order)
pval_array = pval_array[sorted_order]
# calculate the needed alpha
n = float(len(pval))
pval_adj = np.zeros(int(n))
i = np.arange(1, int(n)+1, dtype=float)[::-1] # largest to smallest
pval_adj = np.minimum(1, cummin(n/i * pval_array[::-1]))[::-1]
return pval_adj[original_order] | def bh_fdr(pval) | A python implementation of the Benjamani-Hochberg FDR method.
This code should always give precisely the same answer as using
p.adjust(pval, method="BH") in R.
Parameters
----------
pval : list or array
list/array of p-values
Returns
-------
pval_adj : np.array
adjusted p-values according the benjamani-hochberg method | 3.527563 | 3.684375 | 0.957439 |
#prng = np.random.RandomState(seed)
if len(mut_info) > 0:
mut_info['Coding Position'] = mut_info['Coding Position'].astype(int)
mut_info['Context'] = mut_info['Coding Position'].apply(lambda x: sc.pos2context[x])
# group mutations by context
cols = ['Context', 'Tumor_Allele']
unmapped_mut_df = pd.DataFrame(unmapped_mut_info)
tmp_df = pd.concat([mut_info[cols], unmapped_mut_df[cols]])
context_cts = tmp_df['Context'].value_counts()
context_to_mutations = dict((name, group['Tumor_Allele'])
for name, group in tmp_df.groupby('Context'))
# get deleterious info for actual mutations
aa_mut_info = mc.get_aa_mut_info(mut_info['Coding Position'],
mut_info['Tumor_Allele'].tolist(),
gs)
ref_aa = aa_mut_info['Reference AA'] + unmapped_mut_info['Reference AA']
somatic_aa = aa_mut_info['Somatic AA'] + unmapped_mut_info['Somatic AA']
codon_pos = aa_mut_info['Codon Pos'] + unmapped_mut_info['Codon Pos']
num_del = cutils.calc_deleterious_info(ref_aa, somatic_aa, codon_pos)
#num_del = fs_ct + num_snv_del
# skip permutation test if number of deleterious mutations is not at
# least meet some user-specified threshold
if num_del >= del_threshold:
# perform permutations
del_p_value = pm.deleterious_permutation(num_del,
context_cts,
context_to_mutations,
sc, # sequence context obj
gs, # gene sequence obj
num_permutations,
stop_thresh,
pseudo_count)
else:
del_p_value = None
else:
num_del = 0
del_p_value = None
result = [bed.gene_name, num_del, del_p_value]
return result | def calc_deleterious_p_value(mut_info,
unmapped_mut_info,
sc,
gs,
bed,
num_permutations,
stop_thresh,
del_threshold,
pseudo_count,
seed=None) | Calculates the p-value for the number of inactivating SNV mutations.
Calculates p-value based on how many simulations exceed the observed value.
Parameters
----------
mut_info : dict
contains codon and amino acid residue information for mutations mappable
to provided reference tx.
unmapped_mut_info : dict
contains codon/amino acid residue info for mutations that are NOT mappable
to provided reference tx.
fs_ct : int
number of frameshifts for gene
prob_inactive : float
proportion of inactivating mutations out of total over all genes
sc : SequenceContext
object contains the nucleotide contexts for a gene such that new random
positions can be obtained while respecting nucleotide context.
gs : GeneSequence
contains gene sequence
bed : BedLine
just used to return gene name
num_permutations : int
number of permutations to perform to estimate p-value. more permutations
means more precision on the p-value.
seed : int (Default: None)
seed number to random number generator (None to be randomly set) | 3.234441 | 3.11131 | 1.039575 |
if len(mut_info) > 0:
mut_info['Coding Position'] = mut_info['Coding Position'].astype(int)
mut_info['Context'] = mut_info['Coding Position'].apply(lambda x: sc.pos2context[x])
# group mutations by context
cols = ['Context', 'Tumor_Allele']
unmapped_mut_df = pd.DataFrame(unmapped_mut_info)
tmp_df = pd.concat([mut_info[cols], unmapped_mut_df[cols]])
context_cts = tmp_df['Context'].value_counts()
context_to_mutations = dict((name, group['Tumor_Allele'])
for name, group in tmp_df.groupby('Context'))
# get vest scores for gene if directory provided
if graph_dir:
gene_graph = scores.read_neighbor_graph_pickle(bed.gene_name, graph_dir)
if gene_graph is None:
logger.warning('Could not find neighbor graph for {0}, skipping . . .'.format(bed.gene_name))
else:
gene_graph = None
# get recurrent info for actual mutations
aa_mut_info = mc.get_aa_mut_info(mut_info['Coding Position'],
mut_info['Tumor_Allele'].tolist(),
gs)
codon_pos = aa_mut_info['Codon Pos'] + unmapped_mut_info['Codon Pos']
ref_aa = aa_mut_info['Reference AA'] + unmapped_mut_info['Reference AA']
somatic_aa = aa_mut_info['Somatic AA'] + unmapped_mut_info['Somatic AA']
num_recurrent, pos_ent, delta_pos_ent, pos_ct = cutils.calc_pos_info(codon_pos,
ref_aa,
somatic_aa,
min_frac=min_fraction,
min_recur=min_recurrent)
try:
# get vest score for actual mutations
graph_score, coverage = scores.compute_ng_stat(gene_graph, pos_ct)
# perform simulations to get p-value
protein_p_value, norm_graph_score = pm.protein_permutation(
graph_score, len(pos_ct), context_cts,
context_to_mutations,
sc, # sequence context obj
gs, # gene sequence obj
gene_graph, num_permutations, stop_thresh
)
except Exception as err:
exc_info = sys.exc_info()
norm_graph_score = 0.0
protein_p_value = 1.0
logger.warning('Codon numbering problem with '+bed.gene_name)
else:
norm_graph_score = 0.0
protein_p_value = 1.0
num_recurrent = 0
result = [bed.gene_name, num_recurrent, norm_graph_score, protein_p_value]
return result | def calc_protein_p_value(mut_info,
unmapped_mut_info,
sc,
gs,
bed,
graph_dir,
num_permutations,
stop_thresh,
min_recurrent,
min_fraction) | Computes the p-value for clustering on a neighbor graph composed
of codons connected with edges if they are spatially near in 3D protein
structure.
Parameters
----------
Returns
------- | 3.665694 | 3.757232 | 0.975637 |
return -np.sum(np.where(p!=0, p * np.log2(p), 0)) | def shannon_entropy(p) | Calculates shannon entropy in bits.
Parameters
----------
p : np.array
array of probabilities
Returns
-------
shannon entropy in bits | 2.807511 | 4.778575 | 0.58752 |
cts = np.asarray(counts, dtype=float)
if total_cts is None:
total_cts = np.sum(cts)
if total_cts > 1:
p = cts / total_cts
ent = shannon_entropy(p)
max_ent = max_shannon_entropy(total_cts)
norm_ent = ent / max_ent
else:
norm_ent = 1.0
return norm_ent | def normalized_mutation_entropy(counts, total_cts=None) | Calculate the normalized mutation entropy based on a list/array
of mutation counts.
Note: Any grouping of mutation counts together should be done before hand
Parameters
----------
counts : np.array_like
array/list of mutation counts
Returns
-------
norm_ent : float
normalized entropy of mutation count distribution. | 2.486424 | 2.885573 | 0.861674 |
# make sure numpy arrays are floats
p = p.astype(float)
q = q.astype(float)
# compute kl divergence
kl = np.sum(np.where(p!=0, p*np.log2(p/q), 0))
return kl | def kl_divergence(p, q) | Compute the Kullback-Leibler (KL) divergence for discrete distributions.
Parameters
----------
p : np.array
"Ideal"/"true" Probability distribution
q : np.array
Approximation of probability distribution p
Returns
-------
kl : float
KL divergence of approximating p with the distribution q | 2.591711 | 3.135802 | 0.826491 |
m = .5 * (p+q)
js_div = .5*kl_divergence(p, m) + .5*kl_divergence(q, m)
return js_div | def js_divergence(p, q) | Compute the Jensen-Shannon Divergence between two discrete distributions.
Parameters
----------
p : np.array
probability mass array (sums to 1)
q : np.array
probability mass array (sums to 1)
Returns
-------
js_div : float
js divergence between the two distrubtions | 3.172861 | 3.582508 | 0.885654 |
js_dist = np.sqrt(js_divergence(p, q))
return js_dist | def js_distance(p, q) | Compute the Jensen-Shannon distance between two discrete distributions.
NOTE: JS divergence is not a metric but the sqrt of JS divergence is a
metric and is called the JS distance.
Parameters
----------
p : np.array
probability mass array (sums to 1)
q : np.array
probability mass array (sums to 1)
Returns
-------
js_dist : float
Jensen-Shannon distance between two discrete distributions | 5.202021 | 5.096797 | 1.020645 |
# define coding region
coding_start = int(self.bed_tuple.thickStart)
coding_end = int(self.bed_tuple.thickEnd)
if (coding_end - coding_start) < 3:
# coding regions should have at least one codon, otherwise the
# region is invalid and does not indicate an actually coding region
logger.debug('{0} has an invalid coding region specified by thickStart '
'and thickEnd (only {1} bps long). This gene is possibly either '
'a non-coding transcript or a pseudo gene.'.format(self.gene_name,
coding_end-coding_start))
return []
filtered_exons = []
for exon in ex:
if exon[0] > coding_end and exon[1] > coding_end:
# exon has no coding region
pass
elif exon[0] < coding_start and exon[1] < coding_start:
# exon has no coding region
pass
elif exon[0] <= coding_start and exon[1] >= coding_end:
# coding region entirely contained within one exon
filtered_exons.append((coding_start, coding_end))
elif exon[0] <= coding_start and exon[1] < coding_end:
# only beginning of exon contains UTR
filtered_exons.append((coding_start, exon[1]))
elif exon[0] > coding_start and exon[1] >= coding_end:
# only end part of exon contains UTR
filtered_exons.append((exon[0], coding_end))
elif exon[0] > coding_start and exon[1] < coding_end:
# entire exon is coding
filtered_exons.append(exon)
else:
# exon is only a UTR
pass
return filtered_exons | def _filter_utr(self, ex) | Filter out UTR regions from the exon list (ie retain only coding regions).
Coding regions are defined by the thickStart and thickEnd attributes.
Parameters
----------
ex : list of tuples
list of exon positions, [(ex1_start, ex1_end), ...]
Returns
-------
filtered_exons : list of tuples
exons with UTR regions "chopped" out | 2.836432 | 2.678349 | 1.059023 |
exon_starts = [self.chrom_start + int(s)
for s in self.bed_tuple.blockStarts.strip(',').split(',')]
exon_sizes = list(map(int, self.bed_tuple.blockSizes.strip(',').split(',')))
# get chromosome intervals
exons = [(exon_starts[i], exon_starts[i] + exon_sizes[i])
for i in range(len(exon_starts))]
no_utr_exons = self._filter_utr(exons)
self.exons = no_utr_exons
self.exon_lens = [e[1] - e[0] for e in self.exons]
self.num_exons = len(self.exons)
self.cds_len = sum(self.exon_lens)
self.five_ss_len = 2*(self.num_exons-1)
self.three_ss_len = 2*(self.num_exons-1)
self._init_splice_site_pos() | def _init_exons(self) | Sets a list of position intervals for each exon.
Only coding regions as defined by thickStart and thickEnd are kept.
Exons are stored in the self.exons attribute. | 2.678381 | 2.682771 | 0.998364 |
self.seqpos2genome = {}
# record genome positions for each sequence position
seq_pos = 0
for estart, eend in self.exons:
for genome_pos in range(estart, eend):
if self.strand == '+':
self.seqpos2genome[seq_pos] = genome_pos
elif self.strand == '-':
tmp = self.cds_len - seq_pos - 1
self.seqpos2genome[tmp] = genome_pos
seq_pos += 1
# recode 5' splice site locations
for i in range(0, self.five_ss_len):
seq_pos = self.cds_len + i
ss_ix = i // 2 # the ss_ix'th 5'ss starting from upstream tx
pos_in_ss = i % 2 # whether first/second nuc in splice site
# determine genome coordinates for 5' splice site
if self.strand == '+':
self.seqpos2genome[seq_pos] = self.exons[ss_ix][1] + pos_in_ss
else:
exon_pos = -1 - ss_ix
self.seqpos2genome[seq_pos] = self.exons[exon_pos][0] - pos_in_ss - 1
# recode 3' splice site locations
for i in range(0, self.three_ss_len):
seq_pos = self.cds_len + self.five_ss_len + i
ss_ix = i // 2 # the ss_ix'th 3'ss starting from upstream tx
pos_in_ss = i % 2 # whether first/second nuc in splice site
# determine genome coordinates for 3' splice site
if self.strand == '+':
self.seqpos2genome[seq_pos] = self.exons[ss_ix+1][0] - 2 + pos_in_ss
else:
exon_pos = -1 - ss_ix
self.seqpos2genome[seq_pos] = self.exons[exon_pos-1][1] + 1 - pos_in_ss | def init_genome_coordinates(self) | Creates the self.seqpos2genome dictionary that converts positions
relative to the sequence to genome coordinates. | 2.316622 | 2.223539 | 1.041863 |
# first check if valid
pos = None # initialize to invalid pos
if chr != self.chrom:
#logger.debug('Wrong chromosome queried. You provided {0} but gene is '
#'on {1}.'.format(chr, self.chrom))
# return pos
pass
if type(genome_coord) is list:
# handle case for indels
pos_left = self.query_position(strand, chr, genome_coord[0])
pos_right = self.query_position(strand, chr, genome_coord[1])
if pos_left is not None or pos_right is not None:
return [pos_left, pos_right]
else:
return None
# return position if contained within coding region or splice site
for i, (estart, eend) in enumerate(self.exons):
# in coding region
if estart <= genome_coord < eend:
if strand == '+':
prev_lens = sum(self.exon_lens[:i]) # previous exon lengths
pos = prev_lens + (genome_coord - estart)
elif strand == '-':
prev_lens = sum(self.exon_lens[:i]) # previous exon lengths
pos = prev_lens + (genome_coord - estart)
pos = self.cds_len - pos - 1 # flip coords because neg strand
return pos
# in splice site
elif (eend <= genome_coord < eend + 2) and i != self.num_exons-1:
if strand == '+':
pos = self.cds_len + 2*i + (genome_coord - eend)
elif strand == '-':
pos = self.cds_len + self.five_ss_len + 2*(self.num_exons-(i+2)) + (genome_coord - eend)
return pos
# in splice site
elif (estart - 2 <= genome_coord < estart) and i != 0:
if strand == '-':
pos = self.cds_len + 2*(self.num_exons-(i+2)) + (genome_coord - (estart - 2))
elif strand == '+':
pos = self.cds_len + self.five_ss_len + 2*(i-1) + (genome_coord - (estart - 2))
return pos
return pos | def query_position(self, strand, chr, genome_coord) | Provides the relative position on the coding sequence for a given
genomic position.
Parameters
----------
chr : str
chromosome, provided to check validity of query
genome_coord : int
0-based position for mutation, actually used to get relative coding pos
Returns
-------
pos : int or None
position of mutation in coding sequence, returns None if mutation
does not match region found in self.exons | 2.686636 | 2.675242 | 1.004259 |
if not log_file:
# create log directory if it doesn't exist
log_dir = os.path.abspath('log') + '/'
if not os.path.isdir(log_dir):
os.mkdir(log_dir)
# path to new log file
log_file = log_dir + 'log.run.' + str(datetime.datetime.now()).replace(':', '.') + '.txt'
# logger options
lvl = logging.DEBUG if log_level.upper() == 'DEBUG' else logging.INFO
# ignore warnings if not in debug
if log_level.upper() != 'DEBUG':
warnings.filterwarnings('ignore')
# define logging format
if verbose:
myformat = '%(asctime)s - %(name)s - %(levelname)s \n>>> %(message)s'
else:
myformat = '%(message)s'
# create logger
if not log_file == 'stdout':
# normal logging to a regular file
logging.basicConfig(level=lvl,
format=myformat,
filename=log_file,
filemode='w')
else:
# logging to stdout
root = logging.getLogger()
root.setLevel(lvl)
stdout_stream = logging.StreamHandler(sys.stdout)
stdout_stream.setLevel(lvl)
formatter = logging.Formatter(myformat)
stdout_stream.setFormatter(formatter)
root.addHandler(stdout_stream)
root.propagate = True | def start_logging(log_file='', log_level='INFO', verbose=False) | Start logging information into the log directory.
If os.devnull is specified as the log_file then the log file will
not actually be written to a file. | 2.399486 | 2.41876 | 0.992032 |
@wraps(f)
def wrapper(*args, **kwds):
try:
result = f(*args, **kwds)
return result
except KeyboardInterrupt:
logger.info('Ctrl-C stopped a process.')
except Exception as e:
logger.exception(e)
raise
return wrapper | def log_error_decorator(f) | Writes exception to log file if occured in decorated function.
This decorator wrapper is needed for multiprocess logging since otherwise
the python multiprocessing module will obscure the actual line of the error. | 2.765519 | 3.005681 | 0.920097 |
# indices need to be in reverse order for filtering
# to prevent .pop() from yielding eroneous results
bad_ixs = sorted(bad_ixs, reverse=True)
for i in bad_ixs:
mylist.pop(i)
return mylist | def filter_list(mylist, bad_ixs) | Removes indices from a list.
All elements in bad_ixs will be removed from the list.
Parameters
----------
mylist : list
list to filter out specific indices
bad_ixs : list of ints
indices to remove from list
Returns
-------
mylist : list
list with elements filtered out | 5.314985 | 6.603494 | 0.804875 |
rev_seq = seq[::-1]
rev_comp_seq = ''.join([base_pairing[s] for s in rev_seq])
return rev_comp_seq | def rev_comp(seq) | Get reverse complement of sequence.
rev_comp will maintain the case of the sequence.
Parameters
----------
seq : str
nucleotide sequence. valid {a, c, t, g, n}
Returns
-------
rev_comp_seq : str
reverse complement of sequence | 2.872111 | 4.67189 | 0.614764 |
with open(bed_path) as handle:
bed_reader = csv.reader(handle, delimiter='\t')
for line in bed_reader:
yield BedLine(line) | def bed_generator(bed_path) | Iterates through a BED file yielding parsed BED lines.
Parameters
----------
bed_path : str
path to BED file
Yields
------
BedLine(line) : BedLine
A BedLine object which has parsed the individual line in
a BED file. | 2.44282 | 2.55916 | 0.95454 |
# read in entire bed file into a dict with keys as chromsomes
bed_dict = OrderedDict()
for bed_row in bed_generator(file_path):
is_restrict_flag = restricted_genes is None or bed_row.gene_name in restricted_genes
if is_restrict_flag:
bed_dict.setdefault(bed_row.chrom, [])
bed_dict[bed_row.chrom].append(bed_row)
sort_chroms = sorted(bed_dict.keys(), key=lambda x: len(bed_dict[x]), reverse=True)
bed_dict = OrderedDict((chrom, bed_dict[chrom]) for chrom in sort_chroms)
return bed_dict | def read_bed(file_path, restricted_genes=None) | Reads BED file and populates a dictionary separating genes
by chromosome.
Parameters
----------
file_path : str
path to BED file
filtered_genes: list
list of gene names to not use
Returns
-------
bed_dict: dict
dictionary mapping chromosome keys to a list of BED lines | 2.478613 | 2.711614 | 0.914073 |
# only keep allowed mutation types
orig_len = len(mutation_df) # number of mutations before filtering
mutation_df = mutation_df[mutation_df.Variant_Classification.isin(variant_snv)] # only keep SNV
type_len = len(mutation_df) # number of mutations after filtering based on mut type
# log the number of dropped mutations
log_msg = ('Dropped {num_dropped} mutations after only keeping '
'{mut_types}. Indels are processed separately.'.format(num_dropped=orig_len-type_len,
mut_types=', '.join(variant_snv)))
logger.info(log_msg)
# check if mutations are valid SNVs
valid_nuc_flag = (mutation_df['Reference_Allele'].apply(is_valid_nuc) & \
mutation_df['Tumor_Allele'].apply(is_valid_nuc))
mutation_df = mutation_df[valid_nuc_flag] # filter bad lines
mutation_df = mutation_df[mutation_df['Tumor_Allele'].apply(lambda x: len(x)==1)]
mutation_df = mutation_df[mutation_df['Reference_Allele'].apply(lambda x: len(x)==1)]
valid_len = len(mutation_df)
# log the number of dropped mutations
log_msg = ('Dropped {num_dropped} mutations after only keeping '
'valid SNVs'.format(num_dropped=type_len-valid_len))
logger.info(log_msg)
# drop duplicate mutations
if only_unique:
dup_cols = ['Tumor_Sample', 'Chromosome', 'Start_Position',
'End_Position', 'Reference_Allele', 'Tumor_Allele']
mutation_df = mutation_df.drop_duplicates(subset=dup_cols)
# log results of de-duplication
dedup_len = len(mutation_df)
log_msg = ('Dropped {num_dropped} mutations when removing '
'duplicates'.format(num_dropped=valid_len-dedup_len))
logger.info(log_msg)
# add dummy Protein_Change or Tumor_Type columns if not provided
# in file
if 'Tumor_Type' not in mutation_df.columns:
mutation_df['Tumor_Type'] = ''
if 'Protein_Change' not in mutation_df.columns:
mutation_df['Protein_Change'] = ''
# correct for 1-based coordinates
mutation_df['Start_Position'] = mutation_df['Start_Position'].astype(int) - 1
return mutation_df | def _fix_mutation_df(mutation_df, only_unique=False) | Drops invalid mutations and corrects for 1-based coordinates.
TODO: Be smarter about what coordinate system is put in the provided
mutations.
Parameters
----------
mutation_df : pd.DataFrame
user provided mutations
only_unique : bool
flag indicating whether only unique mutations for each tumor sample
should be kept. This avoids issues when the same mutation has
duplicate reportings.
Returns
-------
mutation_df : pd.DataFrame
mutations filtered for being valid and correct mutation type. Also
converted 1-base coordinates to 0-based. | 2.556937 | 2.51576 | 1.016368 |
pos_ctr, pos_sum = {}, {w: {} for w in window}
num_pos = len(aa_mut_pos)
# figure out the missense mutations
for i in range(num_pos):
pos = aa_mut_pos[i]
# make sure mutation is missense
if germ_aa[i] and somatic_aa[i] and germ_aa[i] != '*' and \
somatic_aa[i] != '*' and germ_aa[i] != somatic_aa[i]:
# should have a position, but if not skip it
if pos is not None:
pos_ctr.setdefault(pos, 0)
pos_ctr[pos] += 1
# calculate windowed sum
pos_list = sorted(pos_ctr.keys())
max_window = max(window)
for ix, pos in enumerate(pos_list):
tmp_sum = {w: 0 for w in window}
# go through the same and lower positions
for k in reversed(range(ix+1)):
pos2 = pos_list[k]
if pos2 < pos-max_window: break
for w in window:
if pos-w <= pos2:
tmp_sum[w] += pos_ctr[pos2]
# go though the higher positions
for l in range(ix+1, len(pos_list)):
pos2 = pos_list[l]
if pos2 > pos+max_window: break
for w in window:
if pos2 <= pos+w:
tmp_sum[w] += pos_ctr[pos2]
# iterate through all other positions
#for pos2 in pos_list:
#for w in window:
#if pos-w <= pos2 <= pos+w:
#tmp_sum[w] += pos_ctr[pos2]
# update windowed counts
for w in window:
pos_sum[w][pos] = tmp_sum[w]
return pos_ctr, pos_sum | def calc_windowed_sum(aa_mut_pos,
germ_aa,
somatic_aa,
window=[3]) | Calculate the sum of mutations within a window around a particular mutated
codon.
Parameters
----------
aa_mut_pos : list
list of mutated amino acid positions
germ_aa : list
Reference amino acid
somatic_aa : list
Somatic amino acid (if missense)
window : list
List of windows to calculate for
Returns
-------
pos_ctr : dict
dictionary of mutated positions (key) with associated counts (value)
pos_sum : dict of dict
Window size as first key points to dictionary of mutated positions (key)
with associated mutation count within the window size (value) | 2.48371 | 2.494027 | 0.995863 |
if context_num == 0:
return ['None']
elif context_num == 1:
return ['A', 'C', 'T', 'G']
elif context_num == 1.5:
return ['C*pG', 'CpG*', 'TpC*', 'G*pA',
'A', 'C', 'T', 'G']
elif context_num == 2:
dinucs = list(set(
[d1+d2
for d1 in 'ACTG'
for d2 in 'ACTG']
))
return dinucs
elif context_num == 3:
trinucs = list(set(
[t1+t2+t3
for t1 in 'ACTG'
for t2 in 'ACTG'
for t3 in 'ACTG']
))
return trinucs | def get_all_context_names(context_num) | Based on the nucleotide base context number, return
a list of strings representing each context.
Parameters
----------
context_num : int
number representing the amount of nucleotide base context to use.
Returns
-------
a list of strings containing the names of the base contexts | 2.754045 | 2.810209 | 0.980015 |
# check if string is correct length
if len(tri_nuc) != 3:
raise ValueError('Chasm context requires a three nucleotide string '
'(Provided: "{0}")'.format(tri_nuc))
# try dinuc context if found
if tri_nuc[1:] == 'CG':
return 'C*pG'
elif tri_nuc[:2] == 'CG':
return 'CpG*'
elif tri_nuc[:2] == 'TC':
return 'TpC*'
elif tri_nuc[1:] == 'GA':
return 'G*pA'
else:
# just return single nuc context
return tri_nuc[1] | def get_chasm_context(tri_nuc) | Returns the mutation context acording to CHASM.
For more information about CHASM's mutation context, look
at http://wiki.chasmsoftware.org/index.php/CHASM_Overview.
Essentially CHASM uses a few specified di-nucleotide contexts
followed by single nucleotide context.
Parameters
----------
tri_nuc : str
three nucleotide string with mutated base in the middle.
Returns
-------
chasm context : str
a string representing the context used in CHASM | 4.083389 | 3.890767 | 1.049508 |
# if no mutations return empty result
if not somatic_base:
aa_info = {'Reference Codon': [],
'Somatic Codon': [],
'Codon Pos': [],
'Reference Nuc': [],
'Reference AA': [],
'Somatic AA': []}
return aa_info
# get codon information into three lists
ref_codon, codon_pos, pos_in_codon, ref_nuc = zip(*[cutils.pos_to_codon(gene_seq, p)
for p in coding_pos])
ref_codon, codon_pos, pos_in_codon, ref_nuc = list(ref_codon), list(codon_pos), list(pos_in_codon), list(ref_nuc)
# construct codons for mutations
mut_codon = [(list(x) if x != 'Splice_Site' else []) for x in ref_codon]
for i in range(len(mut_codon)):
# splice site mutations are not in a codon, so skip such mutations to
# prevent an error
if pos_in_codon[i] is not None:
pc = pos_in_codon[i]
mut_codon[i][pc] = somatic_base[i]
mut_codon = [(''.join(x) if x else 'Splice_Site') for x in mut_codon]
# output resulting info
aa_info = {'Reference Codon': ref_codon,
'Somatic Codon': mut_codon,
'Codon Pos': codon_pos,
'Reference Nuc': ref_nuc,
'Reference AA': [(utils.codon_table[r] if (r in utils.codon_table) else None)
for r in ref_codon],
'Somatic AA': [(utils.codon_table[s] if (s in utils.codon_table) else None)
for s in mut_codon]}
return aa_info | def get_aa_mut_info(coding_pos, somatic_base, gene_seq) | Retrieves relevant information about the effect of a somatic
SNV on the amino acid of a gene.
Information includes the germline codon, somatic codon, codon
position, germline AA, and somatic AA.
Parameters
----------
coding_pos : iterable of ints
Contains the base position (0-based) of the mutations
somatic_base : list of str
Contains the somatic nucleotide for the mutations
gene_seq : GeneSequence
gene sequence
Returns
-------
aa_info : dict
information about the somatic mutation effect on AA's | 2.627751 | 2.588983 | 1.014974 |
permutation_df = pd.DataFrame(sorted(permutation_result, key=lambda x: x[2] if x[2] is not None else 1.1),
columns=['gene', 'inactivating count', 'inactivating p-value',
'Total SNV Mutations', 'SNVs Unmapped to Ref Tx'])
permutation_df['inactivating p-value'] = permutation_df['inactivating p-value'].astype('float')
tmp_df = permutation_df[permutation_df['inactivating p-value'].notnull()]
# get benjamani hochberg adjusted p-values
permutation_df['inactivating BH q-value'] = np.nan
permutation_df.loc[tmp_df.index, 'inactivating BH q-value'] = mypval.bh_fdr(tmp_df['inactivating p-value'])
# sort output by p-value. due to no option to specify NaN order in
# sort, the df needs to sorted descendingly and then flipped
permutation_df = permutation_df.sort_values(by='inactivating p-value', ascending=False)
permutation_df = permutation_df.reindex(index=permutation_df.index[::-1])
# order result
permutation_df = permutation_df.set_index('gene', drop=False)
col_order = ['gene', 'Total SNV Mutations', 'SNVs Unmapped to Ref Tx',
#'Total Frameshift Mutations', 'Frameshifts Unmapped to Ref Tx',
'inactivating count', 'inactivating p-value',
'inactivating BH q-value']
return permutation_df[col_order] | def handle_tsg_results(permutation_result) | Handles result from TSG results.
Takes in output from multiprocess_permutation function and converts to
a better formatted dataframe.
Parameters
----------
permutation_result : list
output from multiprocess_permutation
Returns
-------
permutation_df : pd.DataFrame
formatted output suitable to save | 3.791451 | 3.91062 | 0.969527 |
mycols = ['gene', 'num recurrent', 'position entropy',
'mean vest score', 'entropy p-value',
'vest p-value', 'Total Mutations', 'Unmapped to Ref Tx']
permutation_df = pd.DataFrame(permutation_result, columns=mycols)
# get benjamani hochberg adjusted p-values
permutation_df['entropy BH q-value'] = mypval.bh_fdr(permutation_df['entropy p-value'])
permutation_df['vest BH q-value'] = mypval.bh_fdr(permutation_df['vest p-value'])
# combine p-values
permutation_df['tmp entropy p-value'] = permutation_df['entropy p-value']
permutation_df['tmp vest p-value'] = permutation_df['vest p-value']
permutation_df.loc[permutation_df['entropy p-value']==0, 'tmp entropy p-value'] = 1. / num_permutations
permutation_df.loc[permutation_df['vest p-value']==0, 'tmp vest p-value'] = 1. / num_permutations
permutation_df['combined p-value'] = permutation_df[['entropy p-value', 'vest p-value']].apply(mypval.fishers_method, axis=1)
permutation_df['combined BH q-value'] = mypval.bh_fdr(permutation_df['combined p-value'])
del permutation_df['tmp vest p-value']
del permutation_df['tmp entropy p-value']
# order output
permutation_df = permutation_df.set_index('gene', drop=False) # make sure genes are indices
permutation_df['num recurrent'] = permutation_df['num recurrent'].fillna(-1).astype(int) # fix dtype isssue
col_order = ['gene', 'Total Mutations', 'Unmapped to Ref Tx',
'num recurrent', 'position entropy',
'mean vest score', 'entropy p-value',
'vest p-value', 'combined p-value', 'entropy BH q-value',
'vest BH q-value', 'combined BH q-value']
permutation_df = permutation_df.sort_values(by=['combined p-value'])
return permutation_df[col_order] | def handle_oncogene_results(permutation_result, num_permutations) | Takes in output from multiprocess_permutation function and converts to
a better formatted dataframe.
Parameters
----------
permutation_result : list
output from multiprocess_permutation
Returns
-------
permutation_df : pd.DataFrame
formatted output suitable to save | 2.696193 | 2.760003 | 0.976881 |
if len(permutation_result[0]) == 6:
mycols = ['gene', 'window length', 'codon position', 'mutation count',
'windowed sum', 'p-value']
else:
mycols = ['gene', 'window length', 'codon position', 'index', 'mutation count',
'windowed sum', 'p-value']
permutation_df = pd.DataFrame(permutation_result, columns=mycols)
# get benjamani hochberg adjusted p-values
permutation_df['q-value'] = 1
for w in permutation_df['window length'].unique():
is_window = permutation_df['window length'] == w
permutation_df.loc[is_window, 'q-value'] = mypval.bh_fdr(permutation_df.loc[is_window, 'p-value'])
#permutation_df['q-value'] = mypval.bh_fdr(permutation_df['p-value'])
# order output
#permutation_df = permutation_df.set_index('gene', drop=False) # make sure genes are indices
col_order = mycols + ['q-value']
permutation_df = permutation_df.sort_values(by=['window length', 'p-value'])
return permutation_df[col_order] | def handle_hotmaps_results(permutation_result) | Takes in output from multiprocess_permutation function and converts to
a better formatted dataframe.
Parameters
----------
permutation_result : list
output from multiprocess_permutation
Returns
-------
permutation_df : pd.DataFrame
formatted output suitable to save | 3.32995 | 3.406957 | 0.977397 |
mycols = ['gene', 'num recurrent', 'normalized graph-smoothed position entropy',
'normalized graph-smoothed position entropy p-value',
'Total Mutations', 'Unmapped to Ref Tx']
permutation_df = pd.DataFrame(permutation_result, columns=mycols)
# get benjamani hochberg adjusted p-values
permutation_df['normalized graph-smoothed position entropy BH q-value'] = mypval.bh_fdr(permutation_df['normalized graph-smoothed position entropy p-value'])
# order output
permutation_df = permutation_df.set_index('gene', drop=False) # make sure genes are indices
col_order = ['gene', 'Total Mutations', 'Unmapped to Ref Tx',
'num recurrent',
'normalized graph-smoothed position entropy',
'normalized graph-smoothed position entropy p-value',
'normalized graph-smoothed position entropy BH q-value']
permutation_df = permutation_df.sort_values(by=['normalized graph-smoothed position entropy p-value'])
return permutation_df[col_order] | def handle_protein_results(permutation_result) | Takes in output from multiprocess_permutation function and converts to
a better formatted dataframe.
Parameters
----------
permutation_result : list
output from multiprocess_permutation
Returns
-------
permutation_df : pd.DataFrame
formatted output suitable to save | 4.213128 | 4.38256 | 0.961339 |
mycols = ['gene', 'num recurrent', 'num inactivating', 'entropy-on-effect',
'entropy-on-effect p-value',
'Total Mutations', 'Unmapped to Ref Tx']
permutation_df = pd.DataFrame(sorted(permutation_result, key=lambda x: x[4] if x[4] is not None else 1.1),
columns=mycols)
# get benjamani hochberg adjusted p-values
permutation_df['entropy-on-effect BH q-value'] = mypval.bh_fdr(permutation_df['entropy-on-effect p-value'])
# order output
permutation_df = permutation_df.set_index('gene', drop=False) # make sure genes are indices
permutation_df['num recurrent'] = permutation_df['num recurrent'].fillna(-1).astype(int) # fix dtype isssue
col_order = ['gene', 'Total Mutations', 'Unmapped to Ref Tx',
'num recurrent', 'num inactivating', 'entropy-on-effect',
'entropy-on-effect p-value', 'entropy-on-effect BH q-value']
return permutation_df[col_order] | def handle_effect_results(permutation_result) | Takes in output from multiprocess_permutation function and converts to
a better formatted dataframe.
Parameters
----------
permutation_result : list
output from multiprocess_permutation
Returns
-------
permutation_df : pd.DataFrame
formatted output suitable to save | 4.723446 | 4.809519 | 0.982104 |
fs_df = compute_indel_length(fs_df)
# count the number INDELs with length non-dividable by 3
num_indels = []
indel_len = []
num_categories = 0
i = 1
while(num_categories<bins):
# not inframe length indel
if i%3:
if num_categories != bins-1:
tmp_num = len(fs_df[fs_df['indel len']==i])
else:
tmp_num = len(fs_df[(fs_df['indel len']>=i) & ((fs_df['indel len']%3)>0)])
num_indels.append(tmp_num)
indel_len.append(i)
num_categories += 1
i += 1
return indel_len, num_indels | def get_frameshift_info(fs_df, bins) | Counts frameshifts stratified by a given length.
Parameters
----------
fs_df : pd.DataFrame
indel mutations from non-coding portion
bins : int
number of different length categories for frameshifts
Returns
-------
indel_len : list
length of specific frameshift length category
num_indels : list
number of frameshifts matchin indel_len | 4.063182 | 3.628702 | 1.119734 |
if mut_type:
# user specifies a mutation type
self.mutation_type = mut_type
else:
# mutation type is taken from object attributes
if not self.is_valid:
# does not correctly fall into a category
self.mutation_type = 'not valid'
elif self.unknown_effect:
self.mutation_type = 'unknown effect'
elif self.is_no_protein:
self.mutation_type = 'no protein'
elif self.is_missing_info:
# mutation has a ?
self.mutation_type = 'missing'
else:
# valid mutation type to be counted
if self.is_lost_stop:
self.mutation_type = 'Nonstop_Mutation'
elif self.is_lost_start:
self.mutation_type = 'Translation_Start_Site'
elif self.is_synonymous:
# synonymous must go before missense since mutations
# can be categorized as synonymous and missense. Although
# in reality such cases are actually synonymous and not
# missense mutations.
self.mutation_type = 'Silent'
elif self.is_missense:
self.mutation_type = 'Missense_Mutation'
elif self.is_indel:
self.mutation_type = 'In_Frame_Indel'
elif self.is_nonsense_mutation:
self.mutation_type = 'Nonsense_Mutation'
elif self.is_frame_shift:
self.mutation_type = 'Frame_Shift_Indel' | def set_mutation_type(self, mut_type='') | Sets the mutation type attribute to a single label based on
attribute flags.
Kwargs:
mut_type (str): value to set self.mut_type | 3.447447 | 3.518338 | 0.979851 |
aa = aa.upper() # make sure it is upper case
aa = aa[2:] if aa.startswith('P.') else aa # strip "p."
self.__set_mutation_status() # set flags detailing the type of mutation
self.__parse_hgvs_syntax(aa) | def set_amino_acid(self, aa) | Set amino acid change and position. | 9.267467 | 8.335623 | 1.111791 |
self.__set_lost_stop_status(hgvs_string)
self.__set_lost_start_status(hgvs_string)
self.__set_missense_status(hgvs_string) # missense mutations
self.__set_indel_status() # indel mutations
self.__set_frame_shift_status() # check for fs
self.__set_premature_stop_codon_status(hgvs_string) | def __set_mutation_type(self, hgvs_string) | Interpret the mutation type (missense, etc.) and set appropriate flags.
Args:
hgvs_string (str): hgvs syntax with "p." removed | 3.462771 | 3.70113 | 0.935598 |
# set missense status
if re.search('^[A-Z?]\d+[A-Z?]$', hgvs_string):
self.is_missense = True
self.is_non_silent = True
else:
self.is_missense = False | def __set_missense_status(self, hgvs_string) | Sets the self.is_missense flag. | 3.742715 | 3.450045 | 1.084831 |
# set is lost start status
mymatch = re.search('^([A-Z?])(\d+)([A-Z?])$', hgvs_string)
if mymatch:
grps = mymatch.groups()
if int(grps[1]) == 1 and grps[0] != grps[2]:
self.is_lost_start = True
self.is_non_silent = True
else:
self.is_lost_start = False
else:
self.is_lost_start = False | def __set_lost_start_status(self, hgvs_string) | Sets the self.is_lost_start flag. | 2.901336 | 2.722131 | 1.065833 |
if 'fs' in self.hgvs_original:
self.is_frame_shift = True
self.is_non_silent = True
elif re.search('[A-Z]\d+[A-Z]+\*', self.hgvs_original):
# it looks like some mutations dont follow the convention
# of using 'fs' to indicate frame shift
self.is_frame_shift = True
self.is_non_silent = True
else:
self.is_frame_shift = False | def __set_frame_shift_status(self) | Check for frame shift and set the self.is_frame_shift flag. | 4.432447 | 4.021534 | 1.102178 |
lost_stop_pattern = '^\*\d+[A-Z?]+\*?$'
if re.search(lost_stop_pattern, hgvs_string):
self.is_lost_stop = True
self.is_non_silent = True
else:
self.is_lost_stop = False | def __set_lost_stop_status(self, hgvs_string) | Check if the stop codon was mutated to something other than
a stop codon. | 3.556805 | 3.598703 | 0.988357 |
if re.search('.+\*(\d+)?$', hgvs_string):
self.is_premature_stop_codon = True
self.is_non_silent = True
# check if it is also a nonsense mutation
if hgvs_string.endswith('*'):
self.is_nonsense_mutation = True
else:
self.is_nonsense_mutation = False
else:
self.is_premature_stop_codon = False
self.is_nonsense_mutation = False | def __set_premature_stop_codon_status(self, hgvs_string) | Set whether there is a premature stop codon. | 2.678462 | 2.564105 | 1.044599 |
# set indel status
if "ins" in self.hgvs_original:
# mutation is insertion
self.is_insertion = True
self.is_deletion = False
self.is_indel = True
self.is_non_silent = True
elif "del" in self.hgvs_original:
# mutation is deletion
self.is_deletion = True
self.is_insertion = False
self.is_indel = True
self.is_non_silent = True
else:
# not an indel
self.is_deletion = False
self.is_insertion = False
self.is_indel = False | def __set_indel_status(self) | Sets flags related to the mutation being an indel. | 2.01202 | 1.923238 | 1.046163 |
# Standard use by HGVS of indicating unknown effect.
unknown_effect_list = ['?', '(=)', '='] # unknown effect symbols
if hgvs_string in unknown_effect_list:
self.unknown_effect = True
elif "(" in hgvs_string:
# parethesis in HGVS indicate expected outcomes
self.unknown_effect = True
else:
self.unknown_effect = False
# detect if there are missing information. commonly COSMIC will
# have insertions with p.?_?ins? or deleteions with ?del indicating
# missing information.
if "?" in hgvs_string:
self.is_missing_info = True
else:
self.is_missing_info = False | def __set_unkown_effect(self, hgvs_string) | Sets a flag for unkown effect according to HGVS syntax. The
COSMIC database also uses unconventional questionmarks to denote
missing information.
Args:
hgvs_string (str): hgvs syntax with "p." removed | 7.450627 | 7.086362 | 1.051404 |
no_protein_list = ['0', '0?'] # no protein symbols
if hgvs_string in no_protein_list:
self.is_no_protein = True
self.is_non_silent = True
else:
self.is_no_protein = False | def __set_no_protein(self, hgvs_string) | Set a flag for no protein expected. ("p.0" or "p.0?")
Args:
hgvs_string (str): hgvs syntax with "p." removed | 3.71568 | 4.028877 | 0.922262 |
mycontexts = context_counts.index.tolist()
somatic_base = [base
for one_context in mycontexts
for base in context_to_mut[one_context]]
# calculate the # of batches for simulations
max_batch = min(num_permutations, max_batch)
num_batches = num_permutations // max_batch
remainder = num_permutations % max_batch
batch_sizes = [max_batch] * num_batches
if remainder:
batch_sizes += [remainder]
num_sim = 0
null_del_ct = 0
for j, batch_size in enumerate(batch_sizes):
# stop iterations if reached sufficient precision
if null_del_ct >= stop_criteria:
#j = j - 1
break
# get random positions determined by sequence context
tmp_contxt_pos = seq_context.random_pos(context_counts.iteritems(),
batch_size)
tmp_mut_pos = np.hstack(pos_array for base, pos_array in tmp_contxt_pos)
# determine result of random positions
for i, row in enumerate(tmp_mut_pos):
# get info about mutations
tmp_mut_info = mc.get_aa_mut_info(row,
somatic_base,
gene_seq)
# calc deleterious mutation info
tmp_del_count = cutils.calc_deleterious_info(tmp_mut_info['Reference AA'],
tmp_mut_info['Somatic AA'],
tmp_mut_info['Codon Pos'])
# update empricial null distribution
if tmp_del_count >= obs_del: null_del_ct += 1
# stop if reach sufficient precision on p-value
if null_del_ct >= stop_criteria:
break
# update number of simulations
num_sim += i + 1
#num_sim = j*max_batch + i+1
del_pval = float(null_del_ct) / (num_sim)
return del_pval | def deleterious_permutation(obs_del,
context_counts,
context_to_mut,
seq_context,
gene_seq,
num_permutations=10000,
stop_criteria=100,
pseudo_count=0,
max_batch=25000) | Performs null-permutations for deleterious mutation statistics
in a single gene.
Parameters
----------
context_counts : pd.Series
number of mutations for each context
context_to_mut : dict
dictionary mapping nucleotide context to a list of observed
somatic base changes.
seq_context : SequenceContext
Sequence context for the entire gene sequence (regardless
of where mutations occur). The nucleotide contexts are
identified at positions along the gene.
gene_seq : GeneSequence
Sequence of gene of interest
num_permutations : int, default: 10000
number of permutations to create for null
pseudo_count : int, default: 0
Pseudo-count for number of deleterious mutations for each
permutation of the null distribution. Increasing pseudo_count
makes the statistical test more stringent.
Returns
-------
del_count_list : list
list of deleterious mutation counts under the null | 4.389572 | 4.41595 | 0.994027 |
mycontexts = context_counts.index.tolist()
somatic_base = [base
for one_context in mycontexts
for base in context_to_mut[one_context]]
# get random positions determined by sequence context
tmp_contxt_pos = seq_context.random_pos(context_counts.iteritems(),
num_permutations)
tmp_mut_pos = np.hstack(pos_array for base, pos_array in tmp_contxt_pos)
# calculate position-based statistics as a result of random positions
effect_entropy_list, recur_list, inactivating_list = [], [], []
for row in tmp_mut_pos:
# get info about mutations
tmp_mut_info = mc.get_aa_mut_info(row,
somatic_base,
gene_seq)
# calculate position info
tmp_entropy, tmp_recur, tmp_inactivating = cutils.calc_effect_info(tmp_mut_info['Codon Pos'],
tmp_mut_info['Reference AA'],
tmp_mut_info['Somatic AA'],
pseudo_count=pseudo_count,
is_obs=0)
effect_entropy_list.append(tmp_entropy)
recur_list.append(tmp_recur)
inactivating_list.append(tmp_inactivating)
return effect_entropy_list, recur_list, inactivating_list | def effect_permutation(context_counts,
context_to_mut,
seq_context,
gene_seq,
num_permutations=10000,
pseudo_count=0) | Performs null-permutations for effect-based mutation statistics
in a single gene.
Parameters
----------
context_counts : pd.Series
number of mutations for each context
context_to_mut : dict
dictionary mapping nucleotide context to a list of observed
somatic base changes.
seq_context : SequenceContext
Sequence context for the entire gene sequence (regardless
of where mutations occur). The nucleotide contexts are
identified at positions along the gene.
gene_seq : GeneSequence
Sequence of gene of interest
num_permutations : int, default: 10000
number of permutations to create for null
pseudo_count : int, default: 0
Pseudo-count for number of recurrent missense mutations for each
permutation for the null distribution. Increasing pseudo_count
makes the statistical test more stringent.
Returns
-------
effect_entropy_list : list
list of entropy of effect values under the null
recur_list : list
number of recurrent missense mutations
inactivating_list : list
number of inactivating mutations | 4.344762 | 3.690628 | 1.177242 |
mycontexts = context_counts.index.tolist()
somatic_base = [base
for one_context in mycontexts
for base in context_to_mut[one_context]]
# get random positions determined by sequence context
tmp_contxt_pos = seq_context.random_pos(context_counts.iteritems(),
num_permutations)
tmp_mut_pos = np.hstack(pos_array for base, pos_array in tmp_contxt_pos)
# determine result of random positions
non_silent_count_list = []
for row in tmp_mut_pos:
# get info about mutations
tmp_mut_info = mc.get_aa_mut_info(row,
somatic_base,
gene_seq)
# calc deleterious mutation info
tmp_non_silent = cutils.calc_non_silent_info(tmp_mut_info['Reference AA'],
tmp_mut_info['Somatic AA'],
tmp_mut_info['Codon Pos'])
non_silent_count_list.append(tmp_non_silent)
return non_silent_count_list | def non_silent_ratio_permutation(context_counts,
context_to_mut,
seq_context,
gene_seq,
num_permutations=10000) | Performs null-permutations for non-silent ratio across all genes.
Parameters
----------
context_counts : pd.Series
number of mutations for each context
context_to_mut : dict
dictionary mapping nucleotide context to a list of observed
somatic base changes.
seq_context : SequenceContext
Sequence context for the entire gene sequence (regardless
of where mutations occur). The nucleotide contexts are
identified at positions along the gene.
gene_seq : GeneSequence
Sequence of gene of interest
num_permutations : int, default: 10000
number of permutations to create for null
Returns
-------
non_silent_count_list : list of tuples
list of non-silent and silent mutation counts under the null | 4.758523 | 4.565446 | 1.042291 |
mycontexts = context_counts.index.tolist()
somatic_base = [base
for one_context in mycontexts
for base in context_to_mut[one_context]]
# get random positions determined by sequence context
tmp_contxt_pos = seq_context.random_pos(context_counts.iteritems(),
num_permutations)
tmp_mut_pos = np.hstack(pos_array for base, pos_array in tmp_contxt_pos)
# determine result of random positions
gene_name = gene_seq.bed.gene_name
gene_len = gene_seq.bed.cds_len
summary_info_list = []
for i, row in enumerate(tmp_mut_pos):
# get info about mutations
tmp_mut_info = mc.get_aa_mut_info(row,
somatic_base,
gene_seq)
# Get all metrics summarizing each gene
tmp_summary = cutils.calc_summary_info(tmp_mut_info['Reference AA'],
tmp_mut_info['Somatic AA'],
tmp_mut_info['Codon Pos'],
gene_name,
score_dir,
min_frac=min_frac,
min_recur=min_recur)
# drop silent if needed
if drop_silent:
# silent mutation count is index 1
tmp_summary[1] = 0
# limit the precision of floats
#pos_ent = tmp_summary[-1]
#tmp_summary[-1] = '{0:.5f}'.format(pos_ent)
summary_info_list.append([gene_name, i+1, gene_len]+tmp_summary)
return summary_info_list | def summary_permutation(context_counts,
context_to_mut,
seq_context,
gene_seq,
score_dir,
num_permutations=10000,
min_frac=0.0,
min_recur=2,
drop_silent=False) | Performs null-permutations and summarizes the results as features over
the gene.
Parameters
----------
context_counts : pd.Series
number of mutations for each context
context_to_mut : dict
dictionary mapping nucleotide context to a list of observed
somatic base changes.
seq_context : SequenceContext
Sequence context for the entire gene sequence (regardless
of where mutations occur). The nucleotide contexts are
identified at positions along the gene.
gene_seq : GeneSequence
Sequence of gene of interest
num_permutations : int, default: 10000
number of permutations to create for null
drop_silent : bool, default=False
Flage on whether to drop all silent mutations. Some data sources
do not report silent mutations, and the simulations should match this.
Returns
-------
summary_info_list : list of lists
list of non-silent and silent mutation counts under the null along
with information on recurrent missense counts and missense positional
entropy. | 4.910311 | 4.486738 | 1.094406 |
mycontexts = context_counts.index.tolist()
somatic_base, base_context = zip(*[(base, one_context)
for one_context in mycontexts
for base in context_to_mut[one_context]])
# get random positions determined by sequence context
tmp_contxt_pos = seq_context.random_pos(context_counts.iteritems(),
num_permutations)
tmp_mut_pos = np.hstack(pos_array for base, pos_array in tmp_contxt_pos)
# info about gene
gene_name = gene_seq.bed.gene_name
strand = gene_seq.bed.strand
chrom = gene_seq.bed.chrom
gene_seq.bed.init_genome_coordinates() # map seq pos to genome
# determine result of random positions
maf_list = []
for row in tmp_mut_pos:
# get genome coordinate
pos2genome = np.vectorize(lambda x: gene_seq.bed.seqpos2genome[x]+1)
genome_coord = pos2genome(row)
# get info about mutations
tmp_mut_info = mc.get_aa_mut_info(row,
somatic_base,
gene_seq)
# get string describing variant
var_class = cutils.get_variant_classification(tmp_mut_info['Reference AA'],
tmp_mut_info['Somatic AA'],
tmp_mut_info['Codon Pos'])
# prepare output
for k, mysomatic_base in enumerate(somatic_base):
# format DNA change
ref_nuc = tmp_mut_info['Reference Nuc'][k]
nuc_pos = row[k]
dna_change = 'c.{0}{1}>{2}'.format(ref_nuc, nuc_pos, mysomatic_base)
# format protein change
ref_aa = tmp_mut_info['Reference AA'][k]
somatic_aa = tmp_mut_info['Somatic AA'][k]
codon_pos = tmp_mut_info['Codon Pos'][k]
protein_change = 'p.{0}{1}{2}'.format(ref_aa, codon_pos, somatic_aa)
# reverse complement if on negative strand
if strand == '-':
ref_nuc = utils.rev_comp(ref_nuc)
mysomatic_base = utils.rev_comp(mysomatic_base)
# append results
if drop_silent and var_class[k].decode() == 'Silent': continue
maf_line = [gene_name, strand, chrom, genome_coord[k], genome_coord[k],
ref_nuc, mysomatic_base, base_context[k], dna_change,
protein_change, var_class[k].decode()]
maf_list.append(maf_line)
return maf_list | def maf_permutation(context_counts,
context_to_mut,
seq_context,
gene_seq,
num_permutations=10000,
drop_silent=False) | Performs null-permutations across all genes and records the results in
a format like a MAF file. This could be useful for examining the null
permutations because the alternative approaches always summarize the results.
With the simulated null-permutations, novel metrics can be applied to create
an empirical null-distribution.
Parameters
----------
context_counts : pd.Series
number of mutations for each context
context_to_mut : dict
dictionary mapping nucleotide context to a list of observed
somatic base changes.
seq_context : SequenceContext
Sequence context for the entire gene sequence (regardless
of where mutations occur). The nucleotide contexts are
identified at positions along the gene.
gene_seq : GeneSequence
Sequence of gene of interest
num_permutations : int, default: 10000
number of permutations to create for null
drop_silent : bool, default=False
Flage on whether to drop all silent mutations. Some data sources
do not report silent mutations, and the simulations should match this.
Returns
-------
maf_list : list of tuples
list of null mutations with mutation info in a MAF like format | 3.492712 | 3.418955 | 1.021573 |
return mark_safe(markdown_module.markdown(
force_text(value), extensions=extensions,
extension_configs=extension_configs, safe_mode=safe)) | def markdown(value, extensions=settings.MARKDOWN_EXTENSIONS,
extension_configs=settings.MARKDOWN_EXTENSION_CONFIGS,
safe=False) | Render markdown over a given value, optionally using varios extensions.
Default extensions could be defined which MARKDOWN_EXTENSIONS option.
:returns: A rendered markdown | 2.80089 | 6.308524 | 0.443985 |
init_template = loader.get_template(
settings.MARKDOWN_EDITOR_INIT_TEMPLATE)
options = dict(
previewParserPath=reverse('django_markdown_preview'),
**settings.MARKDOWN_EDITOR_SETTINGS)
options.update(extra_settings)
ctx = dict(
selector=selector,
extra_settings=simplejson.dumps(options)
)
return init_template.render(ctx) | def editor_js_initialization(selector, **extra_settings) | Return script tag with initialization code. | 4.108282 | 3.834177 | 1.07149 |
if settings.MARKDOWN_PROTECT_PREVIEW:
user = getattr(request, 'user', None)
if not user or not user.is_staff:
from django.contrib.auth.views import redirect_to_login
return redirect_to_login(request.get_full_path())
return render(
request, settings.MARKDOWN_PREVIEW_TEMPLATE, dict(
content=request.POST.get('data', 'No content posted'),
css=settings.MARKDOWN_STYLE
)) | def preview(request) | Render preview page.
:returns: A rendered preview | 3.523036 | 3.740073 | 0.94197 |
admin.site.unregister(FlatPage)
admin.site.register(FlatPage, LocalFlatPageAdmin) | def register() | Register markdown for flatpages. | 5.785335 | 3.943417 | 1.467087 |
program_name = __programm_name__
program_version = "v%s" % __version__
program_descrption = __programm_description__
try:
# Setup argument parser
parser = ArgumentParser(prog=program_name, description=program_descrption)
parser.add_argument("-v", "--verbose", dest="verbose", action="count", help="run in verbose mode (-vvv for more, -vvvv to enable connection debugging)")
parser.add_argument("-s", "--sudo", action="store_true", help="run supervisorctl actions with sudo (nopasswd))")
parser.add_argument("-V", "--version", action="version", version=program_version)
parser.add_argument("host-pattern", help="A host-pattern usually refers to a group of hosts. For more details, see Ansible documentation about Patterns.")
#parser.add_argument("supervisorctl-action", help="A supervisorctl action (and optional argument). For more details, see Supervisor documentation about the available supervisorctl actions.")
subparsers = parser.add_subparsers(help="One of the available supervisorctl actions.", dest="supervisorctl-action")
subparsers.add_parser("status", help="Get status info of all processes.")
subparsers.add_parser("reread", help="Reread the configuration files of supervisord")
subparsers.add_parser("reload", help="Restart remote supervisord")
subparsers.add_parser("update", help="Reload the configuration files of supervisord and add/remove processes as necessary")
start_subparser = subparsers.add_parser("start", help="Start a process by name")
start_subparser.add_argument("process-name", help="Name of the process")
stop_subparser = subparsers.add_parser("stop", help="Stop a process by name")
stop_subparser.add_argument("process-name", help="Name of the process")
restart_subparser = subparsers.add_parser("restart", help="Restart a process by name")
restart_subparser.add_argument("process-name", help="Name of the process")
remove_subparser = subparsers.add_parser("remove", help="Remove a process by name")
remove_subparser.add_argument("process-name", help="Name of the process")
# Process arguments
args = parser.parse_args(argv)
verbose = args.verbose
host_pattern = getattr(args, "host-pattern")
supervisorctl_action = getattr(args, "supervisorctl-action")
sudo = args.sudo
ansible_executable = "ansible"
supervisorctl_executable = "supervisorctl"
ansible_action_option = "-a"
if sudo:
supervisorctl_command = "sudo " + supervisorctl_executable + " " + supervisorctl_action
else:
supervisorctl_command = supervisorctl_executable + " " + supervisorctl_action
if supervisorctl_action not in ['status', 'reread', 'update', 'reload']:
supervisorctl_argument = getattr(args, "process-name")
supervisorctl_command = supervisorctl_command + ' ' + supervisorctl_argument
if verbose >0:
verbose_level = "-"+ "v"*verbose
print("Verbose mode on: " + verbose_level)
print "Parsed arguments:"
print args
retcode = call([ansible_executable, host_pattern, verbose_level, ansible_action_option, supervisorctl_command,])
else:
retcode = call([ansible_executable, host_pattern, ansible_action_option, supervisorctl_command])
return retcode
except KeyboardInterrupt:
### handle keyboard interrupt ###
return 0 | def main(argv=None) | Command line options. | 2.660912 | 2.654294 | 1.002493 |
extensions = (arg and arg.split(',')) or settings.MARKDOWN_EXTENSIONS
return _markdown(value, extensions=extensions, safe=False) | def markdown(value, arg=None) | Render markdown over a given value, optionally using varios extensions.
Default extensions could be defined which MARKDOWN_EXTENSIONS option.
Syntax: ::
{{value|markdown}}
{{value|markdown:"tables,codehilite"}}
:returns: A rendered markdown | 6.52211 | 9.773273 | 0.667341 |
extensions = (arg and arg.split(',')) or settings.MARKDOWN_EXTENSIONS
return _markdown(value, extensions=extensions, safe=True) | def markdown_safe(value, arg=None) | Render markdown over a given value, optionally using varios extensions.
Default extensions could be defined which MARKDOWN_EXTENSIONS option.
Enables safe mode, which strips raw HTML and only returns HTML generated
by markdown.
:returns: A rendered markdown. | 6.178503 | 8.16984 | 0.756257 |
return dict(
selector=selector,
extra_settings=mark_safe(simplejson.dumps(
dict(previewParserPath=reverse('django_markdown_preview'))))) | def markdown_editor(selector) | Enable markdown editor for given textarea.
:returns: Editor template context. | 10.5602 | 13.980116 | 0.755373 |
return dict(
CSS_SET=posixpath.join(
settings.MARKDOWN_SET_PATH, settings.MARKDOWN_SET_NAME, 'style.css'
),
CSS_SKIN=posixpath.join(
'django_markdown', 'skins', settings.MARKDOWN_EDITOR_SKIN,
'style.css'
)
) | def markdown_media_css() | Add css requirements to HTML.
:returns: Editor template context. | 4.382306 | 4.984385 | 0.879207 |
return _convert(
_read_file, _process_file,
source, to,
format, extra_args,
encoding=encoding) | def convert(source, to, format=None, extra_args=(), encoding='utf-8') | Convert given `source` from `format` `to` another.
`source` may be either a file path or a string to be converted.
It's possible to pass `extra_args` if needed. In case `format` is not
provided, it will try to invert the format based on given `source`.
Raises OSError if pandoc is not found! Make sure it has been installed and
is available at path. | 6.121706 | 9.42179 | 0.649739 |
try:
p = subprocess.Popen(
['pandoc', '-h'],
stdin=subprocess.PIPE,
stdout=subprocess.PIPE)
except OSError:
raise OSError("You probably do not have pandoc installed.")
help_text = p.communicate()[0].decode().splitlines(False)
txt = ' '.join(help_text[1:help_text.index('Options:')])
aux = txt.split('Output formats: ')
in_ = aux[0].split('Input formats: ')[1].split(',')
out = aux[1].split(',')
return [f.strip() for f in in_], [f.strip() for f in out] | def get_pandoc_formats() | Dynamic preprocessor for Pandoc formats.
Return 2 lists. "from_formats" and "to_formats". | 3.449136 | 3.425832 | 1.006802 |
html = super(MarkdownWidget, self).render(name, value, attrs, renderer)
attrs = self.build_attrs(attrs)
html += editor_js_initialization("#%s" % attrs['id'])
return mark_safe(html) | def render(self, name, value, attrs=None, renderer=None) | Render widget.
:returns: A rendered HTML | 3.994495 | 5.154797 | 0.774908 |
md.registerExtension(self)
md.preprocessors.add('graphviz_block',
InlineGraphvizPreprocessor(md),
"_begin") | def extendMarkdown(self, md, md_globals) | Add InlineGraphvizPreprocessor to the Markdown instance. | 5.605569 | 3.338277 | 1.67918 |
text = "\n".join(lines)
while 1:
m = BLOCK_RE.search(text)
if m:
command = m.group('command')
# Whitelist command, prevent command injection.
if command not in SUPPORTED_COMMAMDS:
raise Exception('Command not supported: %s' % command)
filename = m.group('filename')
content = m.group('content')
filetype = filename[filename.rfind('.')+1:]
args = [command, '-T'+filetype]
try:
proc = subprocess.Popen(
args,
stdin=subprocess.PIPE,
stderr=subprocess.PIPE,
stdout=subprocess.PIPE)
proc.stdin.write(content.encode('utf-8'))
output, err = proc.communicate()
if filetype == 'svg':
data_url_filetype = 'svg+xml'
encoding = 'utf-8'
img = output.decode(encoding)
if filetype == 'png':
data_url_filetype = 'png'
encoding = 'base64'
output = base64.b64encode(output)
data_path = "data:image/%s;%s,%s" % (
data_url_filetype,
encoding,
output)
img = "![" + filename + "](" + data_path + ")"
text = '%s\n%s\n%s' % (
text[:m.start()], img, text[m.end():])
except Exception as e:
err = str(e) + ' : ' + str(args)
return (
'<pre>Error : ' + err + '</pre>'
'<pre>' + content + '</pre>').split('\n')
else:
break
return text.split("\n") | def run(self, lines) | Match and generate dot code blocks. | 3.223029 | 3.084988 | 1.044746 |
now = calendar.timegm(datetime.datetime.now().timetuple())
if now > self.expiration:
auth = self.__open("/oauth/token", data=self.oauth)
self.__sethead(auth['access_token'])
return self.__open("%s%s" % (self.api, command),
headers=self.head, data=data) | def post(self, command, data=None) | Post data to API. | 5.462879 | 5.055112 | 1.080664 |
self.access_token = access_token
now = calendar.timegm(datetime.datetime.now().timetuple())
self.expiration = now + 1800
self.head = {"Authorization": "Bearer %s" % access_token,
"User-Agent": self.user_agent
} | def __sethead(self, access_token) | Set HTTP header. | 2.961597 | 2.955369 | 1.002107 |
headers = headers or {}
if not baseurl:
baseurl = self.baseurl
req = Request("%s%s" % (baseurl, url), headers=headers)
_LOGGER.debug(url)
try:
req.data = urlencode(data).encode('utf-8')
except TypeError:
pass
opener = build_opener()
try:
resp = opener.open(req)
charset = resp.info().get('charset', 'utf-8')
data = json.loads(resp.read().decode(charset))
opener.close()
_LOGGER.debug(json.dumps(data))
return data
except HTTPError as exception_:
if exception_.code == 408:
_LOGGER.debug("%s", exception_)
return False
raise TeslaException(exception_.code) | def __open(self, url, headers=None, data=None, baseurl="") | Use raw urlopen command. | 2.773096 | 2.685262 | 1.03271 |
self._controller.update(self._id, wake_if_asleep=False)
data = self._controller.get_drive_params(self._id)
if data:
if not data['shift_state'] or data['shift_state'] == 'P':
self.__state = True
else:
self.__state = False | def update(self) | Update the parking brake sensor. | 6.351341 | 5.139165 | 1.23587 |
self._controller.update(self._id, wake_if_asleep=False)
data = self._controller.get_climate_params(self._id)
if data:
if time.time() - self.__manual_update_time > 60:
self.__is_auto_conditioning_on = (data
['is_auto_conditioning_on'])
self.__is_climate_on = data['is_climate_on']
self.__driver_temp_setting = (data['driver_temp_setting']
if data['driver_temp_setting']
else self.__driver_temp_setting)
self.__passenger_temp_setting = (data['passenger_temp_setting']
if
data['passenger_temp_setting']
else
self.__passenger_temp_setting)
self.__inside_temp = (data['inside_temp'] if data['inside_temp']
else self.__inside_temp)
self.__outside_temp = (data['outside_temp'] if data['outside_temp']
else self.__outside_temp)
self.__fan_status = data['fan_status'] | def update(self) | Update the HVAC state. | 2.690922 | 2.460218 | 1.093774 |
temp = round(temp, 1)
self.__manual_update_time = time.time()
data = self._controller.command(self._id, 'set_temps',
{"driver_temp": temp,
"passenger_temp": temp},
wake_if_asleep=True)
if data['response']['result']:
self.__driver_temp_setting = temp
self.__passenger_temp_setting = temp | def set_temperature(self, temp) | Set both the driver and passenger temperature to temp. | 5.422731 | 4.638 | 1.169196 |
self.__manual_update_time = time.time()
if enabled:
data = self._controller.command(self._id,
'auto_conditioning_start',
wake_if_asleep=True)
if data['response']['result']:
self.__is_auto_conditioning_on = True
self.__is_climate_on = True
else:
data = self._controller.command(self._id,
'auto_conditioning_stop',
wake_if_asleep=True)
if data['response']['result']:
self.__is_auto_conditioning_on = False
self.__is_climate_on = False
self.update() | def set_status(self, enabled) | Enable or disable the HVAC. | 3.207923 | 2.919683 | 1.098723 |
self._controller.update(self._id, wake_if_asleep=False)
data = self._controller.get_climate_params(self._id)
if data:
self.__inside_temp = (data['inside_temp'] if data['inside_temp']
else self.__inside_temp)
self.__outside_temp = (data['outside_temp'] if data['outside_temp']
else self.__outside_temp) | def update(self) | Update the temperature. | 3.760788 | 3.424766 | 1.098115 |
self._controller.update(self._id, wake_if_asleep=False)
data = self._controller.get_charging_params(self._id)
if data and (time.time() - self.__manual_update_time > 60):
if data['charging_state'] != "Charging":
self.__charger_state = False
else:
self.__charger_state = True | def update(self) | Update the charging state of the Tesla Vehicle. | 5.303608 | 4.180938 | 1.268521 |
if not self.__charger_state:
data = self._controller.command(self._id, 'charge_start',
wake_if_asleep=True)
if data and data['response']['result']:
self.__charger_state = True
self.__manual_update_time = time.time() | def start_charge(self) | Start charging the Tesla Vehicle. | 7.802256 | 6.896783 | 1.131289 |
if self.__charger_state:
data = self._controller.command(self._id, 'charge_stop',
wake_if_asleep=True)
if data and data['response']['result']:
self.__charger_state = False
self.__manual_update_time = time.time() | def stop_charge(self) | Stop charging the Tesla Vehicle. | 7.964667 | 7.063333 | 1.127608 |
self._controller.update(self._id, wake_if_asleep=False)
data = self._controller.get_charging_params(self._id)
if data and (time.time() - self.__manual_update_time > 60):
self.__maxrange_state = data['charge_to_max_range'] | def update(self) | Update the status of the range setting. | 8.32191 | 7.4862 | 1.111633 |
if not self.__maxrange_state:
data = self._controller.command(self._id, 'charge_max_range',
wake_if_asleep=True)
if data['response']['result']:
self.__maxrange_state = True
self.__manual_update_time = time.time() | def set_max(self) | Set the charger to max range for trips. | 10.756185 | 8.385867 | 1.282656 |
if self.__maxrange_state:
data = self._controller.command(self._id, 'charge_standard',
wake_if_asleep=True)
if data and data['response']['result']:
self.__maxrange_state = False
self.__manual_update_time = time.time() | def set_standard(self) | Set the charger to standard range for daily commute. | 11.729804 | 9.326102 | 1.257739 |
if self.__lock_state:
data = self._controller.command(self._id, 'door_unlock',
wake_if_asleep=True)
if data['response']['result']:
self.__lock_state = False
self.__manual_update_time = time.time() | def unlock(self) | Unlock the doors and extend handles where applicable. | 8.631649 | 7.802948 | 1.106204 |
if not self.__lock_state:
data = self._controller.command(self._id, 'charge_port_door_close',
wake_if_asleep=True)
if data['response']['result']:
self.__lock_state = True
self.__manual_update_time = time.time() | def lock(self) | Close the charger door. | 10.424943 | 8.296243 | 1.256586 |
# pylint: disable=no-self-argument
# issue is use of wraps on classmethods which should be replaced:
# https://hynek.me/articles/decorators/
@wraps(func)
def wrapped(*args, **kwargs):
# pylint: disable=too-many-branches,protected-access, not-callable
def valid_result(result):
try:
return (result is not None and result is not False and
(result is True or
(isinstance(result, dict) and
isinstance(result['response'], dict) and
('result' in result['response'] and
result['response']['result'] is True) or
('reason' in result['response'] and
result['response']['reason'] !=
'could_not_wake_buses') or
('result' not in result['response']))))
except TypeError as exception:
_LOGGER.error("Result: %s, %s", result, exception)
retries = 0
sleep_delay = 2
inst = args[0]
vehicle_id = args[1]
result = None
if (vehicle_id is not None and vehicle_id in inst.car_online and
inst.car_online[vehicle_id]):
try:
result = func(*args, **kwargs)
except TeslaException:
pass
if valid_result(result):
return result
_LOGGER.debug("wake_up needed for %s -> %s \n"
"Info: args:%s, kwargs:%s, "
"vehicle_id:%s, car_online:%s",
func.__name__, # pylint: disable=no-member
result, args, kwargs, vehicle_id,
inst.car_online)
inst.car_online[vehicle_id] = False
while ('wake_if_asleep' in kwargs and kwargs['wake_if_asleep']
and
# Check online state
(vehicle_id is None or
(vehicle_id is not None and
vehicle_id in inst.car_online and
not inst.car_online[vehicle_id]))):
result = inst._wake_up(vehicle_id)
_LOGGER.debug("%s(%s): Wake Attempt(%s): %s",
func.__name__, # pylint: disable=no-member,
vehicle_id,
retries, result)
if not result:
if retries < 5:
time.sleep(sleep_delay**(retries+2))
retries += 1
continue
else:
inst.car_online[vehicle_id] = False
raise RetryLimitError
else:
break
# try function five more times
retries = 0
while True:
try:
result = func(*args, **kwargs)
_LOGGER.debug("%s(%s): Retry Attempt(%s): %s",
func.__name__, # pylint: disable=no-member,
vehicle_id,
retries, result)
except TeslaException:
pass
finally:
retries += 1
time.sleep(sleep_delay**(retries+1))
if valid_result(result):
return result
if retries >= 5:
raise RetryLimitError
return wrapped | def wake_up(func) | Wrap a API f so it will attempt to wake the vehicle if asleep.
The command f is run once if the vehicle_id was last reported
online. Assuming f returns None and wake_if_asleep is True, 5 attempts
will be made to wake the vehicle to reissue the command. In addition,
if there is a `could_not_wake_buses` error, it will retry the command
Args:
inst (Controller): The instance of a controller
vehicle_id (string): The vehicle to attempt to wake.
TODO: This currently requires a vehicle_id, but update() does not; This
should also be updated to allow that case
wake_if_asleep (bool): Keyword arg to force a vehicle awake. Must be
set in the wrapped function f
Throws:
RetryLimitError | 3.217613 | 2.883716 | 1.115787 |
# pylint: disable=unused-argument
data = data or {}
return self.__connection.post('vehicles/%i/%s' %
(vehicle_id, command), data) | def post(self, vehicle_id, command, data=None, wake_if_asleep=True) | Send post command to the vehicle_id.
This is a wrapped function by wake_up.
Parameters
----------
vehicle_id : string
Identifier for the car on the owner-api endpoint. Confusingly it
is not the vehicle_id field for identifying the car across
different endpoints.
https://tesla-api.timdorr.com/api-basics/vehicles#vehicle_id-vs-id
command : string
Tesla API command. https://tesla-api.timdorr.com/vehicle/commands
data : dict
Optional parameters.
wake_if_asleep : bool
Function for wake_up decorator indicating whether a failed response
should wake up the vehicle or retry.
Returns
-------
dict
Tesla json object. | 6.471847 | 11.120853 | 0.581956 |
# pylint: disable=unused-argument
return self.__connection.get('vehicles/%i/%s' % (vehicle_id, command)) | def get(self, vehicle_id, command, wake_if_asleep=False) | Send get command to the vehicle_id.
This is a wrapped function by wake_up.
Parameters
----------
vehicle_id : string
Identifier for the car on the owner-api endpoint. Confusingly it
is not the vehicle_id field for identifying the car across
different endpoints.
https://tesla-api.timdorr.com/api-basics/vehicles#vehicle_id-vs-id
command : string
Tesla API command. https://tesla-api.timdorr.com/vehicle/commands
wake_if_asleep : bool
Function for wake_up decorator indicating whether a failed response
should wake up the vehicle or retry.
Returns
-------
dict
Tesla json object. | 7.308344 | 12.984923 | 0.562833 |
return self.get(vehicle_id, 'vehicle_data/%s' % name,
wake_if_asleep=wake_if_asleep)['response'] | def data_request(self, vehicle_id, name, wake_if_asleep=False) | Get requested data from vehicle_id.
Parameters
----------
vehicle_id : string
Identifier for the car on the owner-api endpoint. Confusingly it
is not the vehicle_id field for identifying the car across
different endpoints.
https://tesla-api.timdorr.com/api-basics/vehicles#vehicle_id-vs-id
name: string
Name of data to be requested from the data_request endpoint which
rolls ups all data plus vehicle configuration.
https://tesla-api.timdorr.com/vehicle/state/data
wake_if_asleep : bool
Function for underlying api call for whether a failed response
should wake up the vehicle or retry.
Returns
-------
dict
Tesla json object. | 4.140959 | 5.549803 | 0.746145 |
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