def test_normalized_counts():
from datastore import DataStore
ds = DataStore("../../experiments")
exp_id = "1"
metadata = ds.experiments[exp_id]
cooked = ds.cooked_file_name(exp_id)
exp = parse_cooked(cooked)
params = {
"method": "default",
}
nc, cached = exp.normalized_counts(metadata, params)
print(cached)
print(nc)
def test_differential_abundance():
from datastore import DataStore
ds = DataStore("../../experiments")
exp_id = "8"
metadata = ds.experiments[exp_id]
cooked = ds.cooked_file_name(exp_id)
exp = parse_cooked(cooked)
params = {
"nc_method":
"default",
"method":
"default",
"categories": [
"control cortex-contralateral 24h",
"control cortex-contralateral 48h",
"control cortex-ipsilateral 24h",
"control cortex-ipsilateral 48h",
"control hypothalamus-contralateral 24h",
"control hypothalamus-contralateral 48h",
"control hypothalamus-ipsilateral 24h",
"control hypothalamus-ipsilateral 48h",
"shame-cortex-contralateral 24h",
"shame-cortex-ipsilateral 24h",
"shame-hypothalamus-contralateral 24h",
"shame-hypothalamus-ipsilateral 24h",
"treatment-cortex-contralateral 24h",
"treatment-cortex-ipsilateral 24h",
"treatment-hypothalamus-contralateral 24h",
"treatment-hypothalamus-ipsilateral 24h",
],
"control":
"control cortex-contralateral 48h",
"fc_cutoff":
1,
"mean_cutoff":
0,
}
da, cached = exp.differential_abundance(metadata,
params,
use_cache=False)
print(cached)
print(exp.xhr_da(da))
