def get_group_likelihood(log_p, allsams, num_present, sam2total):
llhs = []
for sam in allsams:
logmu = log_p + log10(sam2total[sam])
llh = poisson.logsf(1, 10 ** logmu)
llhs.append(llh)
sum_llh = sum([10 ** l for l in llhs])
x = num_present
return poisson.logsf(x, sum_llh), sum_llh
def poisson(self, op_id, log_overflow=1000.):
if bool(self):
from scipy.stats import poisson
const, measures, labeled = self._measure(op_id)
out = list()
with warnings.catch_warnings():
warnings.simplefilter('error')
for row in self.data_.itertuples(index=False):
# Calculate expectation
item = const * measures[labeled[0]][_map(row, labeled[0])]
for col in labeled[1:]:
item *= measures[col][_map(row, col)] / self.total_sum
# Log Fish
p, pexp = self.weight(row), item
if p < pexp:
# Xo < pois(Xexp)
try:
val = poisson.logcdf(p, pexp) # P(pois(Xexp)<Xo), Xo = pt[v], Xexp = pexp[v]
except RuntimeWarning:
val = log_overflow
else:
try:
val = -poisson.logsf(p, pexp) # P(pois(Xexp)>=Xo), Xo = pt[v], Xexp = pexp[v]
except RuntimeWarning:
val = -log_overflow
out.append(self.dump_row(row) + (val,))
return self._weighted_output(out)
else:
return self.__class__()
def aaclones_likelihood(clone2sams, model, db_dir, sam2total, group2sams,
outfile, ingroup, outgroup):
f = open(outfile, 'w')
f.write("sample\tnum_ntclones\tprob_observed\n")
for clone, (insams, outsams) in clone2sams.iteritems():
f.write("#%s\n" % clone)
events = []
event_llhs = []
for i, sams in enumerate([insams, outsams]):
if not sams:
continue
sam2ntclones = get_ntclones(clone, sams, db_dir)
f.write("#Group_%d\n" % (i + 1))
for sam, ntclones in sam2ntclones.iteritems():
total = sam2total[sam]
llhoods = []
for ntclone in ntclones:
clonellhood = rcommon.ntclone_likelihood(ntclone, model)
#prob_observed = clonellhood + log10(total)
logmu = log10(total) + clonellhood
prob_observed = poisson.logsf(1, 10 ** logmu) # prob. observing >=1 ntclone
llhoods.append(prob_observed)
if not rcommon.visited_event(events, ntclone):
events.append(ntclone)
event_llhs.append(clonellhood)
#if clonellhood != float(-inf):
# event_llhs.append(clonellhood)
llhoods_str = ",".join(["%f" % llh for llh in llhoods])
f.write("%s\t%d\t%s\n" % (sam, len(ntclones), llhoods_str))
# calc prob to observe the aa clones (sum of all nt events)
if sum([10**llh for llh in event_llhs]) > 0:
aa_llh = log10(sum([10**llh for llh in event_llhs]))
avr_total = (sam2total[ingroup] + sam2total[outgroup]) / 2
avr_logmu = aa_llh + log10(avr_total)
avr_aa_llh = poisson.logsf(1, 10 ** avr_logmu)
f.write("#Clone_log_likelihood: %f, %f\n" % (aa_llh, avr_aa_llh))
ingroup_llh = get_group_likelihood(aa_llh, group2sams[ingroup],
insams, sam2total)
outgroup_llh = get_group_likelihood(aa_llh, group2sams[outgroup],
outsams, sam2total)
f.write("#Ingrp vs Outgrp: %f vs %f\n#\n" % (ingroup_llh, outgroup_llh))
f.close()
def clean_p_values(counts, lambdas):
with scipy.errstate(divide='ignore'):
p_values = poisson.logsf(counts, lambdas)
p_values /= -baseEtoTen
p_values[counts == 0] = 0
p_values[np.isinf(p_values)] = 1000
return p_values
def logprob_not_dark_hot(cts, exp, ref, quantile=64):
"""Log-probabilities of pixels being not dark and not hot.
The cumulative Poisson distribution function (for dark pixels) and its
inverse, the survival function (for hot pixels) are calculated and
normalised to account for the number of dead pixels and the additional
scatter due to mismatch of the reference shape. The pixel ``quantile``
is used for shape normalisation.
Arguments
----------
cts : ndarray of ints
counts per pixel
exp : ndarray of floats
exposure per pixel
ref : ndarray of floats
reference intensity per pixel
Keyword arguments
-----------------
quantile
reference pixel for shape normalisation
Returns
-------
(d, h) : log-probabilities of pixels being not (dark, hot)
"""
if hasattr(cts, 'mask'):
invalid = cts.mask
valid = np.logical_not(invalid)
else:
invalid = []
valid = Ellipsis
ref_cts = ref / ref[valid].mean() * cts[valid].mean()
logcdf = poisson.logcdf(cts, ref_cts)
logsf = poisson.logsf(cts, ref_cts)
logcdf[invalid] = 0
logsf[invalid] = 0
logcdf_s = np.sort(logcdf[valid])
logsf_s = np.sort(logsf[valid])
logcdf, logsf = \
[f(cts, ref_cts) for f in (poisson.logcdf, poisson.logsf)]
for f in logcdf, logsf:
f[invalid] = 0
logcdf_s, logsf_s = \
[np.sort(f[valid]) for f in (logcdf, logsf)]
norm_cdf, norm_sf = \
[np.log((cts.count() - quantile) / quantile) / logf_s[quantile]
for logf_s in (logcdf_s, logsf_s)]
offset = np.log(1 - ((128**2 - cts.count()) / 128**2))
return (offset - logcdf * norm_cdf,
offset - logsf * norm_sf)
def computeLambda(tileCoverage, args):
"""
This function is called by the writeBedGraph workers for every
tile in the genome that is considered
"""
treatmentWindowTags = tileCoverage[0]
controlWindowTags = tileCoverage[1]
treatmentExtraSignalTags = treatmentWindowTags - args['treatmentMean']
controlLambda = args['controlMean'] + (treatmentExtraSignalTags * args['controlSignalRatio'])
log10pvalue = -1* poisson.logsf(controlWindowTags, controlLambda) / np.log(10)
return log10pvalue
def aaclone_llh(clone, cloneinfo, model, lencount_dir, group2sams, outfile, ingroup, outgroup, len2llh, aa_llh):
f = open(outfile, "w")
f.write("sample\tnum_ntclones\tprob_observed\n")
items = clone.split("_")
v = items[0]
l = len(items[1])
len_llh = len2llh[l]
aa_llh_cond = aa_llh - len_llh
f.write("#Log_len_llh: %f\n" % len_llh)
f.write("#Log_aaclone_llh_cond: %f\n" % aa_llh_cond)
# sam2numlen = get_numclone_fixedlen(db_dir, l)
sam2numlen = get_lencount_fixedlen(lencount_dir, l)
# for i, sams in enumerate([insams, outsams]):
# sam2ntclones = get_ntclones(clone, sams, db_dir)
# f.write("#Group_%d\n" % (i + 1))
# for sam, ntclones in sam2ntclones.iteritems():
# totallen = sam2numlen[sam]
# f.write("%s\t%d\n" % (sam, len(ntclones)))
# calc prob to observe the aa clones (sum of all nt events)
avr_totallen = sum(sam2numlen.values()) / len(sam2numlen)
avr_logmu = aa_llh_cond + log10(avr_totallen)
avr_aa_llh = poisson.logsf(1, 10 ** avr_logmu)
f.write("#Clone_log_likelihood: %f, %f\n" % (aa_llh_cond, avr_aa_llh))
if isinstance(cloneinfo, int):
obs_numsam = cloneinfo # observed # samples
allsams = []
for sams in group2sams.values():
allsams.extend(sams)
group_llh, expected_sams = get_group_likelihood(aa_llh_cond, allsams, obs_numsam, sam2numlen)
f.write("#Llh,Obs vs Exp:\t%f\t%d\t%f\n#\n" % (group_llh, obs_numsam, expected_sams))
else:
insams = cloneinfo[0]
outsams = cloneinfo[1]
ingroup_llh, in_expected_sams = get_group_likelihood(aa_llh_cond, group2sams[ingroup], len(insams), sam2numlen)
outgroup_llh, out_expected_sams = get_group_likelihood(
aa_llh_cond, group2sams[outgroup], len(outsams), sam2numlen
)
f.write("#Ingrp vs Outgrp: %f vs %f\n" % (ingroup_llh, outgroup_llh))
f.write("#Expected Ingrp, Outgrp:\t%f\t%f\n" % (in_expected_sams, out_expected_sams))
f.write("#Observed Ingrp, Outgrp:\t%d\t%d\n#\n" % (len(insams), len(outsams)))
f.close()
def get_p_dict(self):
p_value_dict = defaultdict(dict)
count_dict = defaultdict(int)
baseEtoTen = np.log(10)
for node_id, valued_indexes in self.data.items():
start = 0
val = valued_indexes.start_value
for start, end, val in valued_indexes:
if val[1] not in p_value_dict[val[0]]:
log_p_val = poisson.logsf(val[1], val[0])
p_value_dict[val[0]][val[1]] = -log_p_val / baseEtoTen
p = p_value_dict[val[0]][val[1]]
count_dict[p] += end - start
self.p_value_dict = p_value_dict
self.count_dict = count_dict
def computePvalue(tileCoverage, args):
"""
This function is called by the writeBedGraph workers for every
tile in the genome that is considered
It computes a pvalue based on an expected lambda comming from
the correction of treatment when the input is considered.
"""
treatmentWindowTags = tileCoverage[0]
controlWindowTags = tileCoverage[1]
treatmentLambda = controlWindowTags * args['treatmentControlRatio']
log10pvalue = min(300, -1* poisson.logsf(treatmentWindowTags, treatmentLambda) / np.log(10))
return log10pvalue
