#!/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'