def main(regions, bams, reads=None, flags="-F%i" % (0x100 | 0x4 | 0x200 | 0x400),
pad=100):
r2 = open(tempfile.mktemp(), 'w')
for toks in reader(regions, header=False):
if toks[0][0] == "@" or not (toks[1] + toks[2]).isdigit(): continue
toks[1] = str(max(0, int(toks[1]) - pad))
toks[2] = str(int(toks[2]) + pad)
print >>r2, "\t".join(toks)
r2.flush()
regions = r2.name
if reads.isdigit():
reads = int(reads)
elif reads != "bam":
reads = int(nopen("|bioawk -c fastx 'END { print NR }' %s" % reads).next()) * 2.0
counts = {}
colors = cycle('rgbkmy')
bam_reads = {}
counts = dict(pmap(count_both, ((bam, regions, flags)
for bam in bams)))
if reads == "bam":
bam_reads = dict(pmap(count_bam, ((bam, flags) for bam in bams)))
out = sys.stdout
out.write("qual\tmethod\toff\ton\n")
for bam in bams:
nreads = float(bam_reads[bam] if reads == "bam" else reads)
symbol = 'o' if len(set(counts[bam][0])) < 3 else '.'
pl.plot(counts[bam][0] / float(nreads), counts[bam][1] / float(nreads),
'%s%s' % (colors.next(), symbol), label=name(bam))
for qual in range(0, 256):
off, on = counts[bam][0][qual], counts[bam][1][qual]
if off + on == 0: continue
out.write("{qual}\t{bam}\t{off}\t{on}\n".format(
qual=qual, bam=name(bam),
off=off / nreads,
on=on / nreads))
pl.xlabel('off target')
pl.ylabel('on target')
pl.legend(loc='lower right')
pl.xlim(xmin=0)
pl.ylim(ymin=0)
pl.savefig('roc.png')
os.unlink(r2.name)
print >>sys.stderr, "wrote", out.name
def model_clusters(clust_iter,
clin_df,
formula,
coef,
model_fn=gee_cluster,
pool=None,
transform=None,
n_cpu=None,
**kwargs):
"""For each cluster in an iterable, evaluate the chosen model and
yield a dictionary of information
Parameters
----------
clust_iter : iterable
iterable of clusters
clin_df : pandas.DataFrame
Contains covariates from `formula`
formula : str
R (patsy) style formula. Must contain 'methylation': e.g.:
methylation ~ age + gender + race
coef : str
The coefficient of interest in the model, e.g. 'age'
model_fn : fn
A function with signature
fn(formula, methylation, covs, coef, kwargs)
that returns a dictionary with at least p-value and coef
transform: fn
A function that modifies the data before modeling.
n_cpu : int
kwargs: dict
arguments sent to `model_fn`
"""
try:
clin_df.pop('methylation')
except KeyError:
pass
if transform:
tf = lambda cluster: cluster_transform(cluster, transform)
for r in ts.pmap(
wrapper,
((model_fn, formula, tf(cluster) if transform else cluster, clin_df,
coef, kwargs) for cluster in clust_iter),
n_cpu,
p=pool):
yield r
def model_clusters(clust_iter,
clin_df,
model_str,
coef,
model_fn=gee_cluster,
n_cpu=None,
**kwargs):
for r in ts.pmap(wrapper,
((model_fn, model_str, cluster, clin_df, coef, kwargs)
for cluster in clust_iter), n_cpu):
yield r
def model_clusters(clust_iter, clin_df, formula, coef, model_fn=gee_cluster,
pool=None,
transform=None,
n_cpu=None,
**kwargs):
"""For each cluster in an iterable, evaluate the chosen model and
yield a dictionary of information
Parameters
----------
clust_iter : iterable
iterable of clusters
clin_df : pandas.DataFrame
Contains covariates from `formula`
formula : str
R (patsy) style formula. Must contain 'methylation': e.g.:
methylation ~ age + gender + race
coef : str
The coefficient of interest in the model, e.g. 'age'
model_fn : fn
A function with signature
fn(formula, methylation, covs, coef, kwargs)
that returns a dictionary with at least p-value and coef
transform: fn
A function that modifies the data before modeling.
n_cpu : int
kwargs: dict
arguments sent to `model_fn`
"""
try:
clin_df.pop('methylation')
except KeyError:
pass
if transform:
tf = lambda cluster: cluster_transform(cluster, transform)
for r in ts.pmap(wrapper, ((model_fn, formula,
tf(cluster) if transform else cluster,
clin_df, coef,
kwargs) for cluster in clust_iter), n_cpu,
p=pool):
yield r
def model_clusters(clust_iter, clin_df, model_str, coef, model_fn=gee_cluster, n_cpu=None, **kwargs):
for r in ts.pmap(wrapper, ((model_fn, model_str, cluster, clin_df, coef, kwargs) for cluster in clust_iter), n_cpu):
yield r
