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print("WARNING: %s >50% bins filtered, check: %s. " % (genome_hic_file, mseq_str))
# set blacklist to NaNs
if mseq.chr in black_chr_trees:
for black_interval in black_chr_trees[mseq.chr][mseq.start:mseq.end]:
# adjust for sequence indexes
black_seq_start = (black_interval.begin - mseq.start)// options.pool_width
black_seq_end = int( np.ceil( (black_interval.end - mseq.start)/ options.pool_width ) )
seq_hic_raw[:,black_seq_start:black_seq_end] = np.nan
seq_hic_raw[black_seq_start:black_seq_end,:] = np.nan
seq_hic_nan = np.isnan(seq_hic_raw)
# clip first diagonals and high values
clipval = np.nanmedian(np.diag(seq_hic_raw,options.diagonal_offset))
for i in range(-options.diagonal_offset+1,options.diagonal_offset):
set_diag(seq_hic_raw, clipval, i)
seq_hic_raw = np.clip(seq_hic_raw, 0, clipval)
seq_hic_raw[seq_hic_nan] = np.nan
# adaptively coarsegrain based on raw counts
seq_hic_smoothed = adaptive_coarsegrain(
seq_hic_raw,
genome_hic_cool.matrix(balance=False).fetch(mseq_str),
cutoff=2, max_levels=8)
seq_hic_nan = np.isnan(seq_hic_smoothed)
#todo: pass an option to add a certain pseudocount value, or the minimum nonzero value
if options.as_obsexp:
# compute obs/exp
if options.global_obsexp: # compute global obs/exp
exp_chr = genome_hic_expected.iloc[ genome_hic_expected['chrom'].values ==mseq.chr][0:seq_len_pool]
if len(exp_chr) ==0:
cutoff=2, max_levels=8)
seq_hic_nan = np.isnan(seq_hic_smoothed)
#todo: pass an option to add a certain pseudocount value, or the minimum nonzero value
if options.as_obsexp:
# compute obs/exp
if options.global_obsexp: # compute global obs/exp
exp_chr = genome_hic_expected.iloc[ genome_hic_expected['chrom'].values ==mseq.chr][0:seq_len_pool]
if len(exp_chr) ==0:
raise ValueError('no expected values found for chr:'+mseq.chr)
exp_map= np.zeros((seq_len_pool,seq_len_pool))
for i in range(seq_len_pool):
set_diag(exp_map,exp_chr['balanced.avg'].values[i],i)
set_diag(exp_map,exp_chr['balanced.avg'].values[i],-i)
seq_hic_obsexp = seq_hic_smoothed / exp_map
for i in range(-options.diagonal_offset+1,options.diagonal_offset): set_diag(seq_hic_obsexp,1.0,i)
seq_hic_obsexp[seq_hic_nan] = np.nan
else: # compute local obs/exp
seq_hic_obsexp = observed_over_expected(seq_hic_smoothed, ~seq_hic_nan)[0]
# log
if options.no_log==False:
seq_hic_obsexp = np.log(seq_hic_obsexp)
seq_hic_obsexp = np.clip(seq_hic_obsexp, -options.clip, options.clip)
seq_hic_obsexp = interp_nan(seq_hic_obsexp)
for i in range(-options.diagonal_offset+1, options.diagonal_offset): set_diag(seq_hic_obsexp, 0,i)
else:
seq_hic_obsexp = np.clip(seq_hic_obsexp, 0, options.clip)
seq_hic_obsexp = interp_nan(seq_hic_obsexp)
for i in range(-options.diagonal_offset+1, options.diagonal_offset): set_diag(seq_hic_obsexp, 1,i)
for i in range(seq_len_pool):
set_diag(exp_map,exp_chr['balanced.avg'].values[i],i)
set_diag(exp_map,exp_chr['balanced.avg'].values[i],-i)
seq_hic_obsexp = seq_hic_smoothed / exp_map
for i in range(-options.diagonal_offset+1,options.diagonal_offset): set_diag(seq_hic_obsexp,1.0,i)
seq_hic_obsexp[seq_hic_nan] = np.nan
else: # compute local obs/exp
seq_hic_obsexp = observed_over_expected(seq_hic_smoothed, ~seq_hic_nan)[0]
# log
if options.no_log==False:
seq_hic_obsexp = np.log(seq_hic_obsexp)
seq_hic_obsexp = np.clip(seq_hic_obsexp, -options.clip, options.clip)
seq_hic_obsexp = interp_nan(seq_hic_obsexp)
for i in range(-options.diagonal_offset+1, options.diagonal_offset): set_diag(seq_hic_obsexp, 0,i)
else:
seq_hic_obsexp = np.clip(seq_hic_obsexp, 0, options.clip)
seq_hic_obsexp = interp_nan(seq_hic_obsexp)
for i in range(-options.diagonal_offset+1, options.diagonal_offset): set_diag(seq_hic_obsexp, 1,i)
# apply kernel
if kernel is not None:
seq_hic = convolve(seq_hic_obsexp, kernel)
else:
seq_hic = seq_hic_obsexp
else:
# interpolate all missing bins
seq_hic_interpolated = interp_nan(seq_hic_smoothed)
# rescale, reclip
seq_hic_smoothed = adaptive_coarsegrain(
seq_hic_raw,
genome_hic_cool.matrix(balance=False).fetch(mseq_str),
cutoff=2, max_levels=8)
seq_hic_nan = np.isnan(seq_hic_smoothed)
#todo: pass an option to add a certain pseudocount value, or the minimum nonzero value
if options.as_obsexp:
# compute obs/exp
if options.global_obsexp: # compute global obs/exp
exp_chr = genome_hic_expected.iloc[ genome_hic_expected['chrom'].values ==mseq.chr][0:seq_len_pool]
if len(exp_chr) ==0:
raise ValueError('no expected values found for chr:'+mseq.chr)
exp_map= np.zeros((seq_len_pool,seq_len_pool))
for i in range(seq_len_pool):
set_diag(exp_map,exp_chr['balanced.avg'].values[i],i)
set_diag(exp_map,exp_chr['balanced.avg'].values[i],-i)
seq_hic_obsexp = seq_hic_smoothed / exp_map
for i in range(-options.diagonal_offset+1,options.diagonal_offset): set_diag(seq_hic_obsexp,1.0,i)
seq_hic_obsexp[seq_hic_nan] = np.nan
else: # compute local obs/exp
seq_hic_obsexp = observed_over_expected(seq_hic_smoothed, ~seq_hic_nan)[0]
# log
if options.no_log==False:
seq_hic_obsexp = np.log(seq_hic_obsexp)
seq_hic_obsexp = np.clip(seq_hic_obsexp, -options.clip, options.clip)
seq_hic_obsexp = interp_nan(seq_hic_obsexp)
for i in range(-options.diagonal_offset+1, options.diagonal_offset): set_diag(seq_hic_obsexp, 0,i)
else:
seq_hic_obsexp = np.clip(seq_hic_obsexp, 0, options.clip)
for i in range(-options.diagonal_offset+1,options.diagonal_offset): set_diag(seq_hic_obsexp,1.0,i)
seq_hic_obsexp[seq_hic_nan] = np.nan
else: # compute local obs/exp
seq_hic_obsexp = observed_over_expected(seq_hic_smoothed, ~seq_hic_nan)[0]
# log
if options.no_log==False:
seq_hic_obsexp = np.log(seq_hic_obsexp)
seq_hic_obsexp = np.clip(seq_hic_obsexp, -options.clip, options.clip)
seq_hic_obsexp = interp_nan(seq_hic_obsexp)
for i in range(-options.diagonal_offset+1, options.diagonal_offset): set_diag(seq_hic_obsexp, 0,i)
else:
seq_hic_obsexp = np.clip(seq_hic_obsexp, 0, options.clip)
seq_hic_obsexp = interp_nan(seq_hic_obsexp)
for i in range(-options.diagonal_offset+1, options.diagonal_offset): set_diag(seq_hic_obsexp, 1,i)
# apply kernel
if kernel is not None:
seq_hic = convolve(seq_hic_obsexp, kernel)
else:
seq_hic = seq_hic_obsexp
else:
# interpolate all missing bins
seq_hic_interpolated = interp_nan(seq_hic_smoothed)
# rescale, reclip
seq_hic = 100000*seq_hic_interpolated
clipval = np.nanmedian(np.diag(seq_hic,options.diagonal_offset))
for i in range(-options.diagonal_offset+1, options.diagonal_offset):
set_diag(seq_hic,clipval,i)
# apply kernel
if kernel is not None:
seq_hic = convolve(seq_hic_obsexp, kernel)
else:
seq_hic = seq_hic_obsexp
else:
# interpolate all missing bins
seq_hic_interpolated = interp_nan(seq_hic_smoothed)
# rescale, reclip
seq_hic = 100000*seq_hic_interpolated
clipval = np.nanmedian(np.diag(seq_hic,options.diagonal_offset))
for i in range(-options.diagonal_offset+1, options.diagonal_offset):
set_diag(seq_hic,clipval,i)
seq_hic = np.clip(seq_hic, 0, clipval)
#extra smoothing. todo pass kernel specs
if kernel is not None:
seq_hic = convolve(seq_hic, kernel)
except ValueError:
print("WARNING: %s doesn't see %s. Setting to all zeros." % (genome_hic_file, mseq_str))
seq_hic = np.zeros((seq_len_pool,seq_len_pool), dtype='float16')
# crop
if options.crop_bp > 0:
seq_hic = seq_hic[crop_start:crop_end,:]
seq_hic = seq_hic[:,crop_start:crop_end]
# unroll upper triangular
seq_hic_raw,
genome_hic_cool.matrix(balance=False).fetch(mseq_str),
cutoff=2, max_levels=8)
seq_hic_nan = np.isnan(seq_hic_smoothed)
#todo: pass an option to add a certain pseudocount value, or the minimum nonzero value
if options.as_obsexp:
# compute obs/exp
if options.global_obsexp: # compute global obs/exp
exp_chr = genome_hic_expected.iloc[ genome_hic_expected['chrom'].values ==mseq.chr][0:seq_len_pool]
if len(exp_chr) ==0:
raise ValueError('no expected values found for chr:'+mseq.chr)
exp_map= np.zeros((seq_len_pool,seq_len_pool))
for i in range(seq_len_pool):
set_diag(exp_map,exp_chr['balanced.avg'].values[i],i)
set_diag(exp_map,exp_chr['balanced.avg'].values[i],-i)
seq_hic_obsexp = seq_hic_smoothed / exp_map
for i in range(-options.diagonal_offset+1,options.diagonal_offset): set_diag(seq_hic_obsexp,1.0,i)
seq_hic_obsexp[seq_hic_nan] = np.nan
else: # compute local obs/exp
seq_hic_obsexp = observed_over_expected(seq_hic_smoothed, ~seq_hic_nan)[0]
# log
if options.no_log==False:
seq_hic_obsexp = np.log(seq_hic_obsexp)
seq_hic_obsexp = np.clip(seq_hic_obsexp, -options.clip, options.clip)
seq_hic_obsexp = interp_nan(seq_hic_obsexp)
for i in range(-options.diagonal_offset+1, options.diagonal_offset): set_diag(seq_hic_obsexp, 0,i)
else:
seq_hic_obsexp = np.clip(seq_hic_obsexp, 0, options.clip)
seq_hic_obsexp = interp_nan(seq_hic_obsexp)