def clustermodel(fcovs, fmeth, model,
# clustering args
max_dist=200, linkage='complete', rho_min=0.32,
min_clust_size=1,
merge_linkage=None,
max_merge_dist=0,
counts=False,
sep="\t",
X=None, X_locs=None, X_dist=None,
weights=None,
outlier_sds=None,
combine=False, bumping=False, betareg=False,
gee_args=(), skat=False,
png_path=None):
# an iterable of feature objects
# from here, weights are attached to the feature.
feature_iter = feature_gen(fmeth, rho_min=rho_min, weights=weights)
assert min_clust_size >= 1
cluster_gen = (c for c in mclust(feature_iter,
max_dist=max_dist,
linkage=linkage,
merge_linkage=merge_linkage,
max_merge_dist=max_merge_dist
)
if len(c) >= min_clust_size)
for res in clustermodelgen(fcovs, cluster_gen, model, sep=sep,
X=X, X_locs=X_locs, X_dist=X_dist,
outlier_sds=outlier_sds,
combine=combine, bumping=bumping, betareg=betareg,
gee_args=gee_args, skat=skat, counts=counts, png_path=png_path):
yield res
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
return r
if __name__ == "__main__":
def feature_gen(fname):
for i, toks in enumerate(ts.reader(fname, header=False)):
if i == 0:
continue
chrom, pos = toks[0].split(":")
yield Feature(chrom, int(pos), map(float, toks[1:]))
# fmt = "{chrom}\t{start}\t{end}\t{n_probes}\t{p:5g}\t{t:.4f}\t{coef:.4f}\t{var}"
# print ts.fmt2header(fmt)
clust_iter = (c for c in mclust(feature_gen(sys.argv[1]), max_dist=400, max_skip=1) if len(c) > 2)
df = pd.read_table("meth.clin.txt")
df["id"] = df["StudyID"]
formula = "methylation ~ asthma + age + gender"
np.random.seed(10)
ntrue, nfalse = 10, 10
results = []
for fn in (bump_cluster, liptak_cluster, zscore_cluster):
results.append(evaluate_method(clust_iter, df, formula, "asthma", fn, ntrue, nfalse))
formula = "methylation ~ asthma + age + gender"
for fn in (gee_cluster, mixed_model_cluster):
results.append(evaluate_method(clust_iter, df, formula, "asthma", fn, ntrue, nfalse))
return r
if __name__ == "__main__":
def feature_gen(fname):
for i, toks in enumerate(ts.reader(fname, header=False)):
if i == 0: continue
chrom, pos = toks[0].split(":")
yield Feature(chrom, int(pos), map(float, toks[1:]))
#fmt = "{chrom}\t{start}\t{end}\t{n_probes}\t{p:5g}\t{t:.4f}\t{coef:.4f}\t{var}"
#print ts.fmt2header(fmt)
clust_iter = (
c for c in mclust(feature_gen(sys.argv[1]), max_dist=400, max_skip=1)
if len(c) > 2)
df = pd.read_table('meth.clin.txt')
df['id'] = df['StudyID']
formula = "methylation ~ asthma + age + gender"
np.random.seed(10)
ntrue, nfalse = 10, 10
results = []
for fn in (bump_cluster, liptak_cluster, zscore_cluster):
results.append(
evaluate_method(clust_iter, df, formula, 'asthma', fn, ntrue,
nfalse))
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
