def write_modeled_regions(modeled_clusters, p_cutoff, out_fh):
"""
Write a region bed file suitable for use in :func:`~evaluate_modeled_regions`.
Parameters
----------
modeled_clusters : list
output from :func:`~model_clusters`
p_cutoff : float
values < this are set as true
out_fh : filehandle
where to write the data
"""
fmt = "{chrom}\t{start}\t{end}\t{truth}\t{size}\n"
out_fh.write(ts.fmt2header(fmt))
for mc in modeled_clusters:
c = mc['cluster']
truth = ['false', 'true'][int(mc['p'] < p_cutoff)]
for f in c:
out_fh.write(fmt.format(**dict(chrom=f.chrom, start=f.position - 1,
end=f.position,
truth=truth,
size=len(c))))
def write_modeled_regions(modeled_clusters, p_cutoff, out_fh):
"""
Write a region bed file suitable for use in :func:`~evaluate_modeled_regions`.
Parameters
----------
modeled_clusters : list
output from :func:`~model_clusters`
p_cutoff : float
values < this are set as true
out_fh : filehandle
where to write the data
"""
fmt = "{chrom}\t{start}\t{end}\t{truth}\t{size}\n"
out_fh.write(ts.fmt2header(fmt))
for mc in modeled_clusters:
c = mc['cluster']
truth = ['false', 'true'][int(mc['p'] < p_cutoff)]
for f in c:
out_fh.write(
fmt.format(**dict(chrom=f.chrom,
start=f.position - 1,
end=f.position,
truth=truth,
size=len(c))))
def write_region_bed(feature_iter, true_regions, out_fh):
"""
Write a region bed file suitable for use in :func:`~evaluate`.
given true regions (likely from an external program, otherwise use
:func:`~write_modeled_regions`).
Parameters
----------
feature_iter : iterable of Features
true_regions : file
BED file containing true regions
out_fh : filehandle
where to write the data
"""
fmt = "{chrom}\t{start}\t{end}\t{truth}\t{size}\n"
out_fh.write(ts.fmt2header(fmt))
regions = defaultdict(InterLap)
for i, toks in enumerate(ts.reader(true_regions, header=False)):
# see if it's a header.
if i == 0 and not (toks[1] + toks[2]).isdigit(): continue
chrom, start, end = toks[0], int(toks[1]), int(toks[2])
regions[chrom].add((start, end))
for f in feature_iter:
truth = 'true' if (f.position, f.position) in regions[f.chrom] else 'false'
out_fh.write(fmt.format(chrom=f.chrom, start=f.position - 1,
end=f.position, truth=truth, size=1))
out_fh.flush()
def write_region_bed(feature_iter, true_regions, out_fh):
"""
Write a region bed file suitable for use in :func:`~evaluate`.
given true regions (likely from an external program, otherwise use
:func:`~write_modeled_regions`).
Parameters
----------
feature_iter : iterable of Features
true_regions : file
BED file containing true regions
out_fh : filehandle
where to write the data
"""
fmt = "{chrom}\t{start}\t{end}\t{truth}\t{size}\n"
out_fh.write(ts.fmt2header(fmt))
regions = defaultdict(InterLap)
for i, toks in enumerate(ts.reader(true_regions, header=False)):
# see if it's a header.
if i == 0 and not (toks[1] + toks[2]).isdigit(): continue
chrom, start, end = toks[0], int(toks[1]), int(toks[2])
regions[chrom].add((start, end))
for f in feature_iter:
truth = 'true' if (f.position,
f.position) in regions[f.chrom] else 'false'
out_fh.write(
fmt.format(chrom=f.chrom,
start=f.position - 1,
end=f.position,
truth=truth,
size=1))
out_fh.flush()
formula = 'methylation ~ age + gender'
coef = 'gender'
covs = pd.read_csv(covariates_file)
def feature_gen(fname):
for i, d in enumerate(ts.reader(fname, header=False)):
if i == 0: continue
chrom, pos = d[0].split(":")
yield crystal.Feature(chrom, int(pos),
crystal.logit(np.array(map(float, d[1:]))))
cluster_iter = mclust(feature_gen(methylation_file), max_dist=100, max_skip=0)
fmt = "{chrom}\t{start}\t{end}\t{p:.4g}\t{coef:.3f}\t{n_sites:d}"
print(ts.fmt2header(fmt))
for i, c in enumerate(
crystal.model_clusters(cluster_iter,
covs,
formula,
coef,
model_fn=crystal.zscore_cluster,
n_cpu=1)):
print(fmt.format(**c))
if c['p'] < 1e-3 and abs(c['coef']) > 0.2 and c['n_sites'] > 3:
crystal.plot.spaghetti_plot(c, covs)
plt.savefig('/tmp/figure-1.eps')
break
def simulate_regions(clust_list, region_fh, sizes=SIZES, class_order=None,
seed=42, get_reduced_residuals=None, get_reduced_residuals_args=()):
"""Simulate regions and randomize others.
Parameters
----------
clust_list : list of clusters
should include clusters of length 1.
region_fh : filehandle
a BED file of all position will be written to this
file. The 4th column will indicate true/false indicating
if it was simulated to have a difference. The fifth
column will indicate the size of the cluster it was in.
size : dict
keys of the clust_size and values of how many clusters
to create of that size. Default is to create 100 of each
size from 3 to 8 and 200 clusters of size one and 2. All
others are randomized.
classes : np.array
same length as cluster[i].values indicating
which group each sample belongs to.
seed: int
get_reduced_residuals : function
If this parameter is None, then they values are shuffled as they
are received.
A function that accepts a cluster and returns residuals of the reduced
model. e.g. if the full model of interest is:
methylation ~ disease + age + gender
the reduced model would be:
methylation ~ age + gender
so that only the residuals of the reduced model are shuffled and the
other effects should remain. This will implement the bootsrap for
linear models from Efron and Tibshirani.
An Example function would be: :func:`~rr_cluster`
Returns
-------
generator of clusters in the same order as clust_list.
"""
np.random.seed(seed)
from math import log
assert isinstance(clust_list, list), ("need a list due to multiple \
iterations")
if class_order is not None:
class_order = np.array(class_order)
classes = np.unique(class_order)
assert len(classes) == 2, (classes, "should have 2 unique")
classes = {classes[0]: 0, classes[1]: 1}
class_order = np.array([classes[c] for c in class_order])
clusts = defaultdict(list)
for clust in clust_list:
clusts[len(clust)].append(clust)
clusts = dict(clusts)
sim_idxs = {}
# for each size of clust, choose n random indices based on how
# many of that cluster we saw.
for size, n in sizes.items():
idxs = np.arange(len(clusts[size]))
# get the indexes of the clusters we want
sim_idxs[size] = frozenset(np.random.choice(idxs, size=min(n,
len(idxs)), replace=False))
del clusts
fmt = "{chrom}\t{start}\t{end}\t{truth}\t{size}\n"
region_fh.write(ts.fmt2header(fmt))
seen = defaultdict(int)
changed = defaultdict(int)
for c in clust_list:
l = len(c)
w = 0
# need this if block in case we get a cluster longer
# than we have in sizes
if l in sim_idxs:
s = seen[l]
# denominator sets larger DMRs to have a smaller per-probe effect.
#w = 2 * int(s in sim_idxs[l])
#w = int(s in sim_idxs[l]) / (log(l + 1))
#w = 2.0 * int(s in sim_idxs[l]) / (1 + log(l, 2))
#w = int(s in sim_idxs[l]) * 2 ** (1.0 / l)
#w = (3. * int(s in sim_idxs[l])) ** (1.0 / l)
w = 2 * int(s in sim_idxs[l]) / log(l * 2)
seen[l] += 1
if w > 0: changed[l] += 1
truth = l in sim_idxs and s in sim_idxs[l]
for f in c:
region_fh.write(fmt.format(chrom=f.chrom,
start=f.position - 1,
end=f.position,
truth="true" if truth else "false",
size=len(c)))
yield simulate_cluster(c, w, class_order, get_reduced_residuals,
get_reduced_residuals_args)
sys.stderr.write("changed:" + str(dict(changed)) + "\n")
sys.stderr.write("total:" + str(dict(seen)) + "\n")
region_fh.flush()
covariates_file = "../../../crystal/tests/covs.csv"
methylation_file = "../../../crystal/tests/meth.txt.gz"
formula = "methylation ~ age + gender"
coef = "gender"
covs = pd.read_csv(covariates_file)
def feature_gen(fname):
for i, d in enumerate(ts.reader(fname, header=False)):
if i == 0:
continue
chrom, pos = d[0].split(":")
yield crystal.Feature(chrom, int(pos), crystal.logit(np.array(map(float, d[1:]))))
cluster_iter = mclust(feature_gen(methylation_file), max_dist=100, max_skip=0)
fmt = "{chrom}\t{start}\t{end}\t{p:.4g}\t{coef:.3f}\t{n_sites:d}"
print(ts.fmt2header(fmt))
for i, c in enumerate(
crystal.model_clusters(cluster_iter, covs, formula, coef, model_fn=crystal.zscore_cluster, n_cpu=1)
):
print(fmt.format(**c))
if c["p"] < 1e-3 and abs(c["coef"]) > 0.2 and c["n_sites"] > 3:
crystal.plot.spaghetti_plot(c, covs)
plt.savefig("/tmp/figure-1.eps")
break