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))