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#!/usr/bin/python3
# -*- coding: utf-8 -*-
import os
import numpy as np
# own packages
from deeptools import bamHandler
import deeptools.countReadsPerBin as countR
old_settings = np.seterr(all='ignore')
debug = 0
def estimateScaleFactor(bamFilesList, binLength, numberOfSamples,
normalizationLength,
avg_method='median', blackListFileName=None, numberOfProcessors=1,
verbose=False, chrsToSkip=[], mappingStatsList=[]):
r"""
Subdivides the genome into chunks to be analyzed in parallel
using several processors. The code handles the creation of
workers that compute fragment counts (coverage) for different
regions and then collect and integrates the results.
Parameters
----------
bamFilesList : list
list of bam files to normalize
binLength : int
the window size in bp, where reads are going to be
counted.
numberOfSamples : int
number of sites to sample from the genome. For more info see
the documentation of the CountReadsPerBin class
normalizationLength : int
length, in bp, to normalize the data.
For a value of 1, on average
1 read per base pair is found
avg_method : str
defines how the different values are to be summarized.
The options are 'mean' and 'median'
chrsToSkip : list
name of the chromosomes to be excluded from the
scale estimation. Usually the chrX is included.
blackListFileName : str
BED file containing blacklisted regions
mappingStatsList : list
List of the number of mapped reads per file
Returns
-------
dict
Dictionary with the following keys::
'size_factors'
'size_factors_based_on_mapped_reads'
'size_factors_SES'
'size_factors_based_on_mean'
'size_factors_based_on_median'
'mean'
'meanSES'
'median'
'reads_per_bin'
'std'
'sites_sampled'
Examples
--------
>>> test = Tester()
>>> bin_length = 50
>>> num_samples = 4
>>> _dict = estimateScaleFactor([test.bamFile1, test.bamFile2], bin_length, num_samples, 1)
>>> _dict['size_factors']
array([1. , 0.5])
>>> _dict['size_factors_based_on_mean']
array([1. , 0.5])
"""
assert len(bamFilesList) == 2, "SES scale factors are only defined for 2 files"
if len(mappingStatsList) == len(bamFilesList):
mappedReads = mappingStatsList
else:
mappedReads = []
for fname in bamFilesList:
mappedReads.append(bamHandler.openBam(fname, returnStats=True, nThreads=numberOfProcessors)[1])
sizeFactorBasedOnMappedReads = np.array(mappedReads, dtype='float64')
sizeFactorBasedOnMappedReads = sizeFactorBasedOnMappedReads.min() / sizeFactorBasedOnMappedReads
cr = countR.CountReadsPerBin(bamFilesList,
binLength=binLength,
numberOfSamples=numberOfSamples,
extendReads=False,
blackListFileName=blackListFileName,
numberOfProcessors=numberOfProcessors,
verbose=verbose,
chrsToSkip=chrsToSkip)
try:
num_reads_per_bin = cr.run()
except Exception as detail:
exit("*ERROR*: {}".format(detail))
sitesSampled = len(num_reads_per_bin)
# the transpose is taken to easily iterate by columns which are now
# converted to rows
num_reads_per_bin = num_reads_per_bin.transpose()
# size factors based on order statistics
# see Signal extraction scaling (SES) method in: Diaz et al (2012)
# Normalization, bias correction, and peak calling for ChIP-seq.
# Statistical applications in genetics and molecular biology, 11(3).
# using the same names as in Diaz paper
# p refers to ChIP, q to input
p = np.sort(num_reads_per_bin[0, :]).cumsum()
q = np.sort(num_reads_per_bin[1, :]).cumsum()
# p[-1] and q[-1] are the maximum values in the arrays.
# both p and q are normalized by this value
diff = np.abs(p / p[-1] - q / q[-1])
# get the lowest rank for wich the difference is the maximum
maxIndex = np.flatnonzero(diff == diff.max())[0]
# Take a lower rank to move to a region with probably
# less peaks and more background.
maxIndex = int(maxIndex * 0.8)
while maxIndex < len(p):
# in rare cases the maxIndex maps to a zero value.
# In such cases, the next index is used until
# a non zero value appears.
cumSum = np.array([float(p[maxIndex]), float(q[maxIndex])])
if cumSum.min() > 0:
break
maxIndex += 1
meanSES = [np.mean(np.sort(num_reads_per_bin[0, :])[:maxIndex]),
np.mean(np.sort(num_reads_per_bin[1, :])[:maxIndex])]
# the maxIndex may be too close to the the signal regions
# so i take a more conservative approach by taking a close number
sizeFactorsSES = cumSum.min() / cumSum
median = np.median(num_reads_per_bin, axis=1)
# consider only those read numbers that are below the 90
# percentile to stimate the
# mean and std
mean = []
std = []
for values in num_reads_per_bin:
maxNumReads = (np.percentile(values, 90))
if maxNumReads == 0:
maxNumReads = (np.percentile(values, 99))
if maxNumReads == 0:
print("all genomic regions sampled from one ")
"of the bam files have no reads.\n"
values = values[values <= maxNumReads]
mean.append(np.mean(values))
std.append(np.std(values))
mean = np.array(mean)
readsPerBin = mean if avg_method == 'mean' else median
if min(median) == 0:
idx_zero = [ix + 1 for ix, value in enumerate(median) if value == 0]
exit("\n*ERROR*: The median coverage computed is zero for sample(s) #{}\n"
"Try selecting a larger sample size or a region with coverage\n".format(idx_zero))
sizeFactor = sizeFactorsSES
return {'size_factors': sizeFactor,
'size_factors_based_on_mapped_reads': sizeFactorBasedOnMappedReads,
'size_factors_SES': sizeFactorsSES,
'size_factors_based_on_mean': mean.min() / mean,
'size_factors_based_on_median': median.min() / median,
'mean': mean,
'meanSES': meanSES,
'median': median,
'reads_per_bin': readsPerBin,
'std': std,
'sites_sampled': sitesSampled}
class Tester(object):
def __init__(self):
self.root = os.path.dirname(os.path.abspath(__file__)) + "/test/test_data/"
self.bamFile1 = self.root + "testA.bam"
self.bamFile2 = self.root + "testB.bam"
global debug
debug = 0
self.chrom = '3R'
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