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ssf_caller.py
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ssf_caller.py
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from traverse_contours import get_contours
#from edge_cluster import *
from c_hierarchical_edge_merge import *
import numpy as np
from sys import stderr
import scipy.ndimage as ndi
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import matplotlib.pyplot as plt
import time
import pysam
import bisect
from null_distribution import null_distribution
from get_windowed_variance import get_windowed_variance
class call:
def __init__(self, chr, start, end, value, wnd_start, wnd_end, values):
self.chr = chr
self.start = start
self.end = end
self.value = value
self.wnd_start = wnd_start
self.wnd_end = wnd_end
self.width = self.wnd_end - self.wnd_start + 1
self.values = values
self.p_value = None
self.fdr_significant = False
self.significance_level = -1
self.t_stats = None
class callset:
def __init__(self):
self.calls = []
def add_call(self, chr, start, end, val, wnd_end, wnd_start, values):
self.calls.append(call(chr, start, end, val, wnd_end, wnd_start, values))
def __iadd__(self, other):
self.calls += other.calls
return self
def get_t_stats(self,null_dist):
print "getting t-values..."
t = time.time()
for call in self.calls:
call.t_stats = null_dist.get_t_stats(call)
print "done in %fs"%(time.time() - t)
def get_p_values(self,null_dist):
print "getting p-values..."
t = time.time()
for call in self.calls:
#call.p_value = null_dist.get_t_test_p_value(call)
call.p_value = null_dist.get_corrected_t_test_p_value(call)
#call.p_value = null_dist.get_rank_sum_p_value(call)
#call.p_value = null_dist.get_mu_based_p_value(call)
#call.p_value = null_dist.get_ll_based_p_value(call)
print "done in %fs"%(time.time() - t)
def get_bh_corrected_significant(self,fdr):
"""
Benjamini Hochberg correction for
significance at some fdr
"""
print "correcting p-values by Benjamini Hochberg procedure at fdr %f..."%fdr
self.calls = sorted(self.calls,key=lambda x: x.p_value)
m = float(len(self.calls))
max_k = 0
for k, call in enumerate(self.calls):
call.significance_level = k
if call.p_value < (fdr * (k+1)/m):
max_k = k
for k in xrange(max_k+1):
self.calls[k].fdr_significant = True
print "done"
def get_calls_in_range(self,wnd_start,wnd_end):
calls = np.array([ call for call in self.calls if (call.wnd_end>wnd_start and call.wnd_start<wnd_end ) ])
return calls
def output(self,fn_out,t_stats=False):
FOUT = open(fn_out,'w')
additional=""
for i,call in enumerate(sorted(self.calls,key=lambda x:x.p_value)):
if t_stats:
additional = "\t%(mu1)f\t%(var1)f\t%(n1)d\t%(null_mu)f\t%(null_var)f\t%(null_n)d"%(call.t_stats)
print >>FOUT, "%d\t%s\t%d\t%d\t%f\t%.8e\t%s\t%d%s"%(i,
call.chr,
call.start,
call.end,
call.value,
call.p_value,
call.fdr_significant and "*" or "-",
(call.wnd_end-call.wnd_start),
additional)
class ssf_caller:
def __init__(self,chr,cp_data, starts, ends, cutoff_scale, **kwargs):
max_merge=kwargs.get("max_merge",0.5)
use_means=kwargs.get("use_means",False)
n_scales=kwargs.get("n_scales",51)
#n_scales=kwargs.get("n_scales",30)
scale_width=kwargs.get('scale_width',1)
n_bin_smoothings=kwargs.get('-n_bin_smoothings',0)
smoothing_kernel=kwargs.get('smoothing_kernel',np.array([1,2,1]))
self.chr = chr
self.cutoff_scale = cutoff_scale
self.scales = list(np.arange(1,n_scales,scale_width))
self.starts = starts
self.ends = ends
self.n_wnds = self.starts.shape[0]
self.cp_data = cp_data
self.der1=np.zeros((len(self.scales),self.n_wnds),dtype=np.float32)
self.der2=np.zeros((len(self.scales),self.n_wnds),dtype=np.int8)
self.vars = get_windowed_variance(cp_data.astype(np.float64),500)
self.l_vars = np.roll(self.vars,501)
self.r_vars = np.roll(self.vars,-501)
print >>stderr, "scales range from %f-%f"%(self.scales[0],self.scales[-1])
for i in xrange(n_bin_smoothings):
print >>stderr,"doing binomial smooth #%d"%i
cp_data=ndi.convolve1d(cp_data,smoothing_kernel)/np.sum(smoothing_kernel)
transitions_by_scale = {}
print >>stderr, "finding contours..."
for i_scale,scale in enumerate(self.scales):
stderr.write("%.2f "%(scale))
stderr.flush()
g1=ndi.gaussian_filter1d(cp_data,scale,order=1)
g2=ndi.gaussian_filter1d(cp_data,scale,order=2)
edges,pos_edges,neg_edges = self.get_n_edges(g1,g2)
self.der1[i_scale,:]=g1
self.der2[i_scale,:]=pos_edges-neg_edges
transitions_by_scale[scale]=(edges,pos_edges,neg_edges)
stderr.write("done\n")
self.contour_intersects,x_intercept_to_scale=get_contours(self.der2)
######NOW we have all the per-scale contours
#print contour_intersects
edges_passing_cutoff =[]
curr_all_edges=[]
curr_all_edges_scales=[]
#take all the edges discovered at some scale
for scale,edges in self.contour_intersects.iteritems():
curr_all_edges.extend(edges)
curr_all_edges_scales.extend([scale for i in xrange(len(edges))])
if scale >=cutoff_scale:
edges_passing_cutoff.extend(edges)
edges_passing_cutoff=sorted(set(edges_passing_cutoff))
all_edges_scales=sorted(zip(curr_all_edges,curr_all_edges_scales))
stderr.write("hierarchically merging segments\n")
t = time.time()
segments_s, segments_e, cps = c_hierarch_merge_edges(cp_data,
edges_passing_cutoff,
max_merge,
use_means,
self.n_wnds,
self.starts,
self.ends)
#segments_s, segments_e, cps = hierarch_merge_edges(cp_data,
# edges_passing_cutoff,
# max_merge,use_means)
self.segment_edges=(segments_s,segments_e,cps)
print >>stderr, "hierarchical clustering completed in %fs"%(time.time()-t)
def get_exclude_coords(self, ex_starts, ex_ends):
mx=self.starts.shape[0]-1
n_exclude = len(ex_ends)
ex_wnd_starts = np.searchsorted(self.starts, ex_starts)
ex_wnd_ends = np.searchsorted(self.ends, ex_ends)
ex_wnd_starts = np.amax(np.c_[ex_wnd_starts-1,np.zeros(n_exclude)],1).astype(int)
ex_wnd_ends = np.amin(np.c_[ex_wnd_ends+1,np.ones(n_exclude)*mx],1).astype(int)
ex_starts = self.starts[ex_wnd_starts]
ex_ends = self.ends[ex_wnd_ends]
ex_coords = []
curr_s = ex_starts[0]
curr_e = ex_ends[0]
#print ex_wnd_starts
#print ex_wnd_ends
for i in xrange(1, n_exclude):
if ex_starts[i] < curr_e:
curr_e = ex_ends[i]
else:
ex_coords.append(tuple([curr_s,curr_e]))
curr_s = ex_starts[i]
curr_e = ex_ends[i]
ex_coords.append(tuple([curr_s,curr_e]))
return ex_coords
def subtract_excluded(self, wnd_start, wnd_end, ex_coords):
"""
subtract the exclusion coordinates from the full call
ea. output '.'s exclude 'x's over cal '-'s
------------------------
xxxx....xx....xxx....xxxxxx
"""
start = self.starts[wnd_start]
end = self.ends[wnd_end]
#wnd_start-wnd_end totally encompassed by gap
if start >= ex_coords[0][0] and end <= ex_coords[0][1]:
return []
#starting wnd, either the end of the first gap, or the start of the wnd
if start >= ex_coords[0][0]:
init = ex_coords[0][1]
ex_coords.pop(0)
else:
init = start
#ending wnd, either the start of the last gap, or the end of the wnd
if len(ex_coords)>0 and end<=ex_coords[-1][1]:
final = ex_coords[-1][0]
ex_coords.pop()
else:
final = end
regions = [init, final]
for ex in ex_coords:
regions.append(ex[0])
regions.append(ex[1])
regions = sorted(regions)
final_wnds = []
i=0
while i<len(regions):
final_wnds.append(tuple([np.where(self.starts == regions[i])[0][0],
np.where(self.ends == regions[i+1])[0][0]
]))
i+=2
return final_wnds
def get_callset(self, exclude_tbxs=[], min_exclude_ratio=0.3, min_exclude_len=20000):
"""
return segments and their copies in genome
coordinates
adding subtraction of gaps
"""
c=callset()
wnd_starts,wnd_ends,cps = self.segment_edges
wnd_starts,wnd_ends,cps = np.array(wnd_starts), np.array(wnd_ends), np.array(cps)
for i in xrange(len(wnd_starts)-1):
start, end = self.starts[wnd_starts[i]], self.ends[wnd_ends[i]]
wnd_start, wnd_end = wnd_starts[i], wnd_ends[i]
#exclude totally anything in these tbxs
for exclude_tbx in exclude_tbxs:
ex_starts, ex_ends = [], []
for l in exclude_tbx.fetch(self.chr,start,end,parser=pysam.asTuple()):
_chr,_s,_e = l
_s, _e = int(_s), int(_e)
if _e-_s > min_exclude_len:
ex_starts.append(_s)
ex_ends.append(_e)
n_exclude = len(ex_starts)
if n_exclude:
ex_coords = self.get_exclude_coords(ex_starts, ex_ends)
wnd_start_ends = self.subtract_excluded(wnd_start, wnd_end, ex_coords)
else:
wnd_start_ends = [tuple([wnd_start, wnd_end])]
for i in xrange(len(wnd_start_ends)):
wnd_start = wnd_start_ends[i][0]
wnd_end = wnd_start_ends[i][1]
c.add_call(self.chr,
self.starts[wnd_start],
self.ends[wnd_end],
np.mean(self.cp_data[wnd_start:wnd_end]),
wnd_start,
wnd_end,
self.cp_data[wnd_start:wnd_end])
return c
def get_n_edges(self,der1,der2,n=0):
"""
get the edges from the derivatives
der1 is magnitudes
der2 is 0 crossings
d_der2=diff(der2)
_________________________
/ \
/ \
------ --------
d1 0 to +ve 0 to -ve
d2 +ve to 0 -ve -ve to 0 +ve
pm -ve +ve
ff -ve (and should be +1) +ve
pos to a neg (left to right) results in a -ve pm
neg to a pos (left to right) results ina +ve pm
+1+1+1-1-1-1+1+1+1
0 0-2 0 0+2 0 0
"""
if n==0:
n=der1.shape[0]
der2_pos=der2>0
der2_neg=der2<0
pm_array=np.zeros(der2.shape[0])
pm_array=pm_array+der2_pos-der2_neg
diffarray=np.diff(pm_array)
#GET INNER THEN OUTER
"""
this means the first bp is INSIDE the variant
on the left hand side,
on the right side the call is outside the variant
"""
intercepts_rise=np.where(diffarray<0)[0]+1
intercepts_fall=np.where(diffarray>0)[0]+1
#GET INNER COORDS
#intercepts_rise=np.where(diffarray<0)[0]+1
#intercepts_fall=np.where(diffarray>0)[0]
#GET OUTER COORDS
#intercepts_rise=np.where(diffarray<0)[0]
#intercepts_fall=np.where(diffarray>0)[0]+1
all_intercepts = np.unique(np.r_[intercepts_rise,
intercepts_fall,
0,pm_array.shape[0]-1])
intercepts=all_intercepts
#magnitudes at each intercept
mags = np.abs(der1[intercepts])
len_mags=mags.shape[0]
#locations in the intercepts array of the highest mags
top_sorted_inds=np.argsort(mags)[(len_mags-n):len_mags]
#locations in the intercepts array of the highest mags
#top_sorted_inds=np.argsort(mags)
edge_array=np.zeros(der1.shape[0])
edge_array[intercepts[top_sorted_inds]]=1
neg_edges=edge_array*-1*der2_neg
pos_edges=edge_array*der2_pos
return edge_array,neg_edges,pos_edges