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summary.py
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summary.py
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"Module for working with collections of Log objects."
from __future__ import absolute_import
import os
import datetime
import fasta
from textwrap import wrap
from collections import defaultdict
try:
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure as fig
MATPLOTLIB = True
except ImportError:
print("{}tip:{} install Matplotlib (https://matplotlib.org/) to \
get a barplot of the paralog frequency".format("\033[92m", "\033[0m"))
MATPLOTLIB = False
HAVE_DISPLAY = "DISPLAY" in os.environ
if not HAVE_DISPLAY:
print("{}warning:{} no display found; can't generate plot{}".format(
"\033[35m", "\033[37m", "\033[0m"))
MATPLOTLIB = False
TIMESTAMP = datetime.datetime.now().strftime("%Y-%m-%d")
SUM_HEADER = "id;alignments;sequences;otus;meanSequences;meanOtus;meanSeqLen;\
shortestSeq;longestSeq;pctMissingData;catAlignmentLen\n"
SUM_PATH = "/supermatrix_stats.csv"
FREQ_PLOT_FILE = "/paralogy_freq_plot.png"
FREQ_CSV_FILE = "/otu_stats.csv"
class Summary(object):
"Represents a collection of Log objects from previous runs."
def __init__(self, dir_out=None):
self._logs = []
def __len__(self):
return len(self.logs)
def __nonzero__(self):
return True
def __bool__(self):
return True
@property
def logs(self):
"A list of Log objects."
return self._logs
@logs.setter
def logs(self, value):
self._logs = value
def paralogy_frequency(self, dir_out, factor=None):
"""Calculate the paralogy frequency (PF) for all OTUs within this
summary, where paralogy frequency is the number of paralogs divided by
the number of alignments that each OTU is present in. Output the
statistics in form of a CSV file, a PNG plot and return a dictionary.
Parameters
----------
dir_out : str
Path to the directory to which you wish to output the PF plot.
factor : float
Used to visualize where the threshold for frequent paralogy cutoff
was set.
Returns
-------
paralog_freq : dict
Dictionary where a OTU string is key and PF, as a float, is the value.
"""
if os.path.isfile(dir_out + FREQ_PLOT_FILE):
os.remove(dir_out + FREQ_PLOT_FILE)
if os.path.isfile(dir_out + FREQ_CSV_FILE):
os.remove(dir_out + FREQ_CSV_FILE)
threshold = None
seen = set()
paralog_freq = defaultdict(int) # key is OTU, value is no. of paralogs
presence = defaultdict(int) # key is OTU, value is
divergent = defaultdict(int) # key is OTU, divergent sequences is value
first_iteration = True
for log in self.logs:
otus_in_alignment = log.msa.otus()
for otu in otus_in_alignment:
if otu:
presence[otu] += 1
for paralog in log.paralogs:
otu = paralog.otu()
# start counting at the first multiple of an OTU
if otu and otu in seen:
paralog_freq[otu] += 1
seen.add(otu)
for otu in log.divergent:
divergent[otu] += 1
# normalize paralogy frequency by how often the OTU is present
for otu in presence:
if not otu in paralog_freq:
paralog_freq[otu] = 0
else:
paralog_freq[otu] = round(
(float(paralog_freq[otu]) / float(presence[otu])) * 100, 1)
if factor:
threshold = _std(list(paralog_freq.values())) * factor
with open(dir_out + FREQ_CSV_FILE, "w") as csv_out:
csv_out.write("otu;paralogyFrequency;timesAboveDivergenceThreshold\n")
for otu, freq in paralog_freq.items():
csv_out.write("{};{};{}\n".format(
otu, freq, divergent[otu]))
otus = list(paralog_freq.keys())
indexes = range(len(otus))
freq = list(paralog_freq.values())
if MATPLOTLIB:
plt.barh(y=indexes, width=freq, color="black",
edgecolor=['black']*len(freq), alpha=0.5)
plt.yticks(list(indexes), otus)
plt.ylabel("OTU")
plt.xlabel("number of paralogs / number of alignments OTU is in")
plt.title("Paralogy Frequency")
if threshold:
plt.axvline(x=threshold,
color="red",
label="cutoff = {}".format(round(threshold, 3)),
linestyle="--")
plt.legend(loc='upper right', fontsize=14)
fig = plt.gcf()
fig.set_size_inches(12.0, len(otus) * 0.17)
plt.savefig(dir_out + FREQ_PLOT_FILE, bbox_inches='tight', dpi=300)
return paralog_freq
def homolog_seq_files(self):
"Returns the number of sequence files within the homolog phylogeny."
no_of_files = 0
no_of_files = sum(1 for log in self.logs)
return no_of_files
def homolog_seqs(self):
"Returns the number of sequences within the homolog phylogeny."
no_of_seqs = 0
for log in self.logs:
no_of_seqs += len(log.msa)
return no_of_seqs
def homolog_avg_seqs(self):
"Returns the average number of sequences within the homolog phylogeny."
seq_files = self.homolog_seq_files()
if seq_files > 0:
return int(self.homolog_seqs() / seq_files)
else:
return 0
def homolog_avg_seq_len(self):
"Returns the average sequence length within the homolog phylogeny."
seq_lens = 0
sequences = 0
for log in self.logs:
for sequence in log.msa.sequences:
sequences += 1
seq_lens += len(sequence.ungapped())
if sequences > 0:
return int(seq_lens / sequences)
else:
return 0
def sequence_files(self):
"Returns the number of sequence files contained within the Log objects."
no_of_files = 0
for log in self.logs:
for ortholog in log.orthologs:
if ortholog:
no_of_files += 1
return no_of_files
def sequences(self):
"Returns the number of sequences within the Log objects."
no_of_seqs = 0
for log in self.logs:
for msa_out in log.msas_out:
no_of_seqs += len(msa_out)
return no_of_seqs
def shortest_sequence(self):
"Returns the shortest sequence within all of the Log objects."
shortest = None
for log in self.logs:
for msa_out in log.msas_out:
for sequence in msa_out.sequences:
if not shortest or shortest > len(sequence.ungapped()):
shortest = len(sequence.ungapped())
return shortest
def longest_sequence(self):
"Returns the longest sequence within all of the Log objects."
longest = None
for log in self.logs:
for msa_out in log.msas_out:
for sequence in msa_out.sequences:
if not longest or longest < len(sequence.ungapped()):
longest = len(sequence.ungapped())
return longest
def homolog_shortest_sequence(self):
"Returns the shortest sequence within the homologs."
shortest = None
for log in self.logs:
for sequence in log.msa.sequences:
if not shortest or shortest > len(sequence.ungapped()):
shortest = len(sequence.ungapped())
return shortest
def homolog_longest_sequence(self):
"Returns the longest sequence within all of the homologs."
longest = None
for log in self.logs:
for sequence in log.msa.sequences:
if not longest or longest < len(sequence):
longest = len(sequence.ungapped())
return longest
def avg_sequences(self):
"Returns the average number of sequences per sequence file."
no_of_files = self.sequence_files()
if no_of_files > 0:
return int(self.sequences() / self.sequence_files())
else:
return 0
def avg_otus(self):
"Returns the average number of OTUs in this summary."
otus_total = 0
ortholog_count = 0
for log in self.logs:
otus = None
for ortholog in log.orthologs:
if ortholog:
otus = set(ortholog.iter_otus())
otus_total += len(otus)
ortholog_count += 1
if ortholog_count > 0:
return int(otus_total / ortholog_count)
else:
return 0
def avg_seq_len(self):
"Returns the average sequence length of all MSAs combined."
seq_lens = 0
sequences = 0
for log in self.logs:
for msa_out in log.msas_out:
for sequence in msa_out.sequences:
sequences += 1
seq_lens += len(sequence.ungapped())
if sequences > 0:
return int(seq_lens / sequences)
else:
return 0
def homolog_avg_otus(self):
"Returns the average number of OTUs within the homologs."
otus_total = 0
ortholog_count = 0
for log in self.logs:
otus = set(log.tree.iter_otus())
otus_total += len(otus)
ortholog_count += 1
if ortholog_count > 0:
return int(otus_total / ortholog_count)
else:
return 0
def homolog_missing_data(self):
"Returns the percent missing data within the homologs."
pct_missing = 0.0
no_of_alignments = 0
no_of_otus = len(self.homolog_otus())
for log in self.logs:
no_of_alignments += 1
otus_missing = no_of_otus - len(log.msa.otus())
if not no_of_otus == 0:
pct_missing += float(otus_missing) / float(no_of_otus)
pct_missing += log.msa.missing_data()
if no_of_alignments > 0:
return round((pct_missing / no_of_alignments) * 100, 1)
else:
return 0
def missing_data(self):
"Returns the percent missing data within the orthologs."
pct_missing = 0.0
no_of_alignments = 0
no_of_otus = len(self.otus())
for log in self.logs:
for msa_out in log.msas_out:
no_of_alignments += 1
otus_missing = no_of_otus - len(msa_out.otus())
pct_missing += float(otus_missing) / float(no_of_otus)
pct_missing += msa_out.missing_data()
if no_of_alignments > 0:
return round((pct_missing / no_of_alignments) * 100, 1)
else:
return 0
def cat_alignment(self):
"""
Returns the length of the concatenated alignment, if we were to
concatenate all orthologs together.
"""
cat_alignment_len = 0
for log in self.logs:
for msa_out in log.msas_out:
cat_alignment_len += msa_out.alignment_len()
return cat_alignment_len
def homolog_cat_alignment(self):
"""
Returns the length of the concatenated alignment, if we were to
concatenate all homologous sequences together.
"""
seq_lens = 0
for log in self.logs:
sequence = log.msa.sequences[0]
seq_lens += len(sequence)
return seq_lens
def homolog_report(self, dir_out):
"""Output a summary of the input alignments. Title is always
'homologs'.
Parameters
----------
dir_out : str
Path to the output directory that the summary file is saved to.
Return
------
report : str
Overview statistics of the summary.
"""
homolog_report = """No. of alignments: {}
No. of sequences: {}
No. of OTUs: {}
Avg no. of sequences / alignment: {}
Avg no. of OTUs / alignment: {}
Avg sequence length (ungapped): {}
Shortest sequence (ungapped): {}
Longest sequence (ungapped): {}
% missing data: {}
concatenated alignment length: {}
""".format(
self.homolog_seq_files(),
self.homolog_seqs(),
len(self.homolog_otus()),
self.homolog_avg_seqs(),
self.homolog_avg_otus(),
self.homolog_avg_seq_len(),
self.homolog_shortest_sequence(),
self.homolog_longest_sequence(),
self.homolog_missing_data(),
self.homolog_cat_alignment())
row = "{};{};{};{};{};{};{};{};{};{};{}\n".format(
"input",
self.homolog_seq_files(),
self.homolog_seqs(),
len(self.homolog_otus()),
self.homolog_avg_seqs(),
self.homolog_avg_otus(),
self.homolog_avg_seq_len(),
self.homolog_shortest_sequence(),
self.homolog_longest_sequence(),
self.homolog_missing_data(),
self.homolog_cat_alignment())
with open(dir_out + SUM_PATH, "a") as sum_out_file:
sum_out_file.write(row)
return homolog_report
def report(self, title, dir_out):
"""Output a summary of the files for this run.
Parameters
----------
title : str
The ID of the summary file.
dir_out : str
Path to the output directory that the summary file is saved to.
Return
------
report : str
Overview statistics of the summary.
"""
report = """
Output Alignments
-----------------
No. of alignments: {}
No. of sequences: {}
No. of OTUs: {}
Avg no. of sequences / alignment: {}
Avg no. of OTUs / alignment: {}
Avg sequence length (ungapped): {}
Shortest sequence (ungapped): {}
Longest sequence (ungapped): {}
% missing data: {}
concatenated alignment length: {}""".format(
self.sequence_files(),
self.sequences(),
len(self.otus()),
self.avg_sequences(),
self.avg_otus(),
self.avg_seq_len(),
self.shortest_sequence(),
self.longest_sequence(),
self.missing_data(),
self.cat_alignment())
row = "{};{};{};{};{};{};{};{};{};{};{}\n".format(
title,
self.sequence_files(),
self.sequences(),
len(self.otus()),
self.avg_sequences(),
self.avg_otus(),
self.avg_seq_len(),
self.shortest_sequence(),
self.longest_sequence(),
self.missing_data(),
self.cat_alignment())
with open(dir_out + SUM_PATH, "a") as sum_out_file:
sum_out_file.write(row)
return report
def write_msas(self, wrap=None):
"""
Write the alignments within the Log objects in this summary's logs, if any,
into one or more alignments file. If wrap has been specified, wrap
sequence data at the provided column.
"""
for log in self.logs:
for msa in log.msas_out:
fasta.write(msa, wrap)
def homolog_otus(self):
"Returns a set of all OTUs within the homologs."
otus = set()
for log in self.logs:
tree = log.masked_tree
if tree:
for leaf in tree.iter_leaves():
if leaf.name:
otus.add(leaf.otu())
return otus
def otus(self):
"Returns a set of all OTUs within this Summary object."
otus_in_summary = set()
for log in self.logs:
for msa_out in log.msas_out:
otus_in_summary.update(msa_out.otus())
return otus_in_summary
def mk_sum_out_title(dir_out):
"""
Takes the path to an output directory as an input and writes
"""
if os.path.isfile(dir_out + SUM_PATH):
os.remove(dir_out + SUM_PATH)
with open(dir_out + SUM_PATH, "w") as sum_out_file:
sum_out_file.write(SUM_HEADER)
def _mean(data):
"""Returns the sample arithmetic mean of data. 0 is returned if an empty
list was provided.
Parameters
----------
data : list of floats
Returns
_______
out: float
The sample arithmetic mean of data.
"""
return float(sum(data)) / max(len(data), 1)
def _sdm(data):
"""Returns the squared deviations from the mean (SDM) of data.
Parameters
----------
data : list of floats
Returns
-------
out : float
The sum of square deviations of data.
"""
return sum((x - _mean(data))**2 for x in data)
def _std(data):
"""Return the population standard deviation of data.
Parameters
----------
data : list of floats
Returns
-------
out : float
The population standard deviation of data.
"""
if len(data) < 2:
raise ValueError('variance requires at least two data points')
return (_sdm(data) / len(data)) ** 0.5