#!/usr/bin/env python # Time-stamp: <2013-10-18 16:27:40 Tao Liu> import os import sys import unittest from MACS2.IO.cCallPeakUnit import * import numpy as np from MACS2.IO.cPeakIO import PeakIO from math import factorial from random import normalvariate class Test_CallSummits ( unittest.TestCase ): def setUp( self ): self.range = [ 0, 2000 ] self.binding_sites = [ 300, 500, 700 ] self.binding_strength = [ 60, 45, 55 ] # approximate binding affility self.binding_width = [ 150, 150, 150 ]# binding width, left and right sides are cutting sites self.cutting_variation = 50 # variation at the correct cutting sites self.tag_size = 50 self.test_tags_file = "random_test.bed" self.genome_size = 10000 self.plus_tags = [ ] self.minus_tags = [ ] for i in range( len(self.binding_sites) ): j = 0 while j <= self.binding_strength[ i ]: x = int( normalvariate( self.binding_sites[ i ] - self.binding_width[ i ]/2, self.cutting_variation ) ) if x > self.range[ 0 ] and x + self.tag_size < self.range[ 1 ]: self.plus_tags.append( x ) j += 1 j = 0 while j <= self.binding_strength[ i ]: x = int( normalvariate( self.binding_sites[ i ] + self.binding_width[ i ]/2, self.cutting_variation ) ) if x - self.tag_size > self.range[ 0 ] and x < self.range[ 1 ]: self.minus_tags.append( x ) j += 1 self.plus_tags = sorted(self.plus_tags) self.minus_tags = sorted(self.minus_tags) #print self.plus_tags #print self.minus_tags self.result_peak = PeakIO() # write reads in bed files fhd = open( self.test_tags_file, "w" ) for x in self.plus_tags: fhd.write( "chr1\t%d\t%d\t.\t0\t+\n" % ( x, x + self.tag_size ) ) for x in self.minus_tags: fhd.write( "chr1\t%d\t%d\t.\t0\t-\n" % ( x - self.tag_size, x ) ) def test_pileup ( self ): pass # def test_wo_subpeak ( self ): # peak_content = self.test_peak_content # tsummit = [] # summit_pos = 0 # summit_value = 0 # for i in range(len(peak_content)): # (tstart, tend, ttreat_p, tctrl_p, tlist_scores_p) = peak_content[i] # tscore = ttreat_p #self.pqtable[ get_pscore(int(ttreat_p), tctrl_p) ] # use qscore as general score to find summit # if not summit_value or summit_value < tscore: # tsummit = [(tend + tstart) / 2, ] # tsummit_index = [ i, ] # summit_value = tscore # elif summit_value == tscore: # # remember continuous summit values # tsummit.append(int((tend + tstart) / 2)) # tsummit_index.append( i ) # # the middle of all highest points in peak region is defined as summit # print "wo, all:",tsummit # midindex = int((len(tsummit) + 1) / 2) - 1 # summit_pos = tsummit[ midindex ] # summit_index = tsummit_index[ midindex ] # print "wo:",summit_pos # def test_w_subpeak ( self ): # peak_content = self.test_peak_content # smoothlen = 20 # peak_length = peak_content[ -1 ][ 1 ] - peak_content[ 0 ][ 0 ] # # Add 10 bp padding to peak region so that we can get true minima # end = peak_content[ -1 ][ 1 ] + 10 # start = peak_content[ 0 ][ 0 ] - 10 # if start < 0: # start_boundary = 5 + start # this is the offset of original peak boundary in peakdata list. # start = 0 # else: # start_boundary = 5 # this is the offset of original peak boundary in peakdata list. # peakdata = np.zeros(end - start, dtype='float32') # save the scores (qscore) for each position in this region # peakindices = np.zeros(end - start, dtype='int32') # save the indices for each position in this region # for i in range(len(peak_content)): # (tstart, tend, ttreat_p, tctrl_p, tlist_scores_p) = peak_content[i] # #tscore = self.pqtable[ get_pscore(int(ttreat_p), tctrl_p) ] # use qscore as general score to find summit # tscore = ttreat_p # use pileup as general score to find summit # m = tstart - start + start_boundary # n = tend - start + start_boundary # peakdata[m:n] = tscore # peakindices[m:n] = i # np.set_printoptions(precision=1, suppress=True) # #print "before smoothed data:", len(peakdata), peakdata # print "maximum points:", np.where(peakdata == peakdata.max()) # summit_offsets = maxima(peakdata, smoothlen) # offsets are the indices for summits in peakdata/peakindices array. # print "summit_offsets:", summit_offsets # m = np.searchsorted(summit_offsets, start_boundary) # n = np.searchsorted(summit_offsets, peak_length + start_boundary, 'right') # summit_offsets = summit_offsets[m:n] # print "summit_offsets adjusted:", summit_offsets # summit_offsets = enforce_peakyness(peakdata, summit_offsets) # print "summit_offsets enforced:", summit_offsets # summit_indices = peakindices[summit_offsets] # indices are those point to peak_content # summit_offsets -= start_boundary # print "summit_offsets final:", summit_offsets # for summit_offset, summit_index in zip(summit_offsets, summit_indices): # print "w:",start+summit_offset def maxima ( signal, window_size=51 ): """return the local maxima in a signal after applying a 2nd order Savitsky-Golay (polynomial) filter using window_size specified """ #data1 = savitzky_golay(signal, window_size, order=2, deriv=1) #data2 = savitzky_golay(signal, window_size, order=2, deriv=2) #m = np.where(np.diff(np.sign( data1 )) <= -1)[0].astype('int32') #data1 = savitzky_golay_order2(signal, window_size, deriv=1) data1 = savitzky_golay(signal, window_size, order=2, deriv=1) m = np.where( np.diff( np.sign( data1 ) ) <= -1)[0].astype('int32') #m = np.where( np.logical_and( data2 < 0 , abs(data1) <= 1e-10) ) [0].astype('int32') return m def savitzky_golay_order2(signal, window_size, deriv=0): """Smooth (and optionally differentiate) data with a Savitzky-Golay filter. The Savitzky-Golay filter removes high frequency noise from data. It has the advantage of preserving the original shape and features of the signal better than other types of filtering approaches, such as moving averages techhniques. Parameters ---------- y : array_like, shape (N,) the values of the time history of the signal. window_size : int the length of the window. Must be an odd integer number. deriv: int the order of the derivative to compute (default = 0 means only smoothing) Returns ------- ys : ndarray, shape (N) the smoothed signal (or it's n-th derivative). Notes ----- The Savitzky-Golay is a type of low-pass filter, particularly suited for smoothing noisy data. The main idea behind this approach is to make for each point a least-square fit with a polynomial of high order over a odd-sized window centered at the point. References ---------- .. [1] A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry, 1964, 36 (8), pp 1627-1639. .. [2] Numerical Recipes 3rd Edition: The Art of Scientific Computing W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery Cambridge University Press ISBN-13: 9780521880688 """ if window_size % 2 != 1: window_size += 1 half_window = (window_size - 1) / 2 # precompute coefficients b = np.mat([[1, k, k**2] for k in range(-half_window, half_window+1)], dtype='int64') m = np.linalg.pinv(b).A[deriv] # pad the signal at the extremes with # values taken from the signal itself firstvals = signal[0] - np.abs(signal[1:half_window+1][::-1] - signal[0]) lastvals = signal[-1] + np.abs(signal[-half_window-1:-1][::-1] - signal[-1]) signal = np.concatenate((firstvals, signal, lastvals)) ret = np.convolve( m, signal.astype('float64'), mode='valid').astype('float32') return ret def savitzky_golay(y, window_size, order=2, deriv=0, rate=1): if window_size % 2 != 1: window_size += 1 try: window_size = np.abs(np.int(window_size)) order = np.abs(np.int(order)) except ValueError, msg: raise ValueError("window_size and order have to be of type int") if window_size % 2 != 1 or window_size < 1: raise TypeError("window_size size must be a positive odd number") if window_size < order + 2: raise TypeError("window_size is too small for the polynomials order") order_range = range(order+1) half_window = (window_size -1) // 2 # precompute coefficients b = np.mat([[k**i for i in order_range] for k in range(-half_window, half_window+1)]) m = np.linalg.pinv(b).A[deriv] * rate**deriv * factorial(deriv) # pad the signal at the extremes with # values taken from the signal itself firstvals = y[0] - np.abs( y[1:half_window+1][::-1] - y[0] ) lastvals = y[-1] + np.abs(y[-half_window-1:-1][::-1] - y[-1]) y = np.concatenate((firstvals, y, lastvals)) return np.convolve( m[::-1], y, mode='valid') if __name__ == '__main__': unittest.main()