split similarity

sc2heoca/sc2heoca.py → sc2heoca/query.py

@@ -25,13 +25,13 @@
 
 
 def seed_everything(TORCH_SEED):
-	random.seed(TORCH_SEED)
-	os.environ['PYTHONHASHSEED'] = str(TORCH_SEED)
-	np.random.seed(TORCH_SEED)
-	torch.manual_seed(TORCH_SEED)
-	torch.cuda.manual_seed_all(TORCH_SEED)
-	torch.backends.cudnn.deterministic = True
-	torch.backends.cudnn.benchmark = False
+    random.seed(TORCH_SEED)
+    os.environ['PYTHONHASHSEED'] = str(TORCH_SEED)
+    np.random.seed(TORCH_SEED)
+    torch.manual_seed(TORCH_SEED)
+    torch.cuda.manual_seed_all(TORCH_SEED)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
 
 def load_colorpalette():
     color_file = os.path.join(PACKAGE_DIR, "db", "gut_scpoli_color.txt")
@@ -275,37 +275,3 @@ def find_de_genes(self, tissue, adata_query, adata_ctrl=None, method='wilcoxauc'
             de_res_final = pd.DataFrame.from_dict(uni_genes, orient='index').T
 
         return de_res_final
-
-class Similarity:
-    def __init__(self, r_path, model_file):
-        self.r_path = r_path
-
-        os.environ["R_HOME"] = self.r_path
-        from rpy2.robjects.packages import importr
-        base = importr('base')
-        miloR = importr('miloR')
-
-        self.milo_model = base.readRDS(f"{model_file}")
-
-    def run_milo(self, adata, sample_name):
-
-        os.environ["R_HOME"] = self.r_path
-        import rpy2.robjects as robjects
-        import anndata2ri
-        anndata2ri.activate()
-
-        adata_sce = anndata2ri.py2rpy(adata)
-
-        r_source = robjects.r['source']
-
-        milo_file = os.path.join(PACKAGE_DIR, "r", "run_milo.R")
-        r_source(milo_file)
-
-        r_getname = robjects.globalenv['run_milo']
-
-        res = r_getname(self.milo_model, adata_sce, 'cell_ontology_class')
-        res['sample']=sample_name
-
-        return res
-
-

sc2heoca/similarity.py

@@ -0,0 +1,58 @@
+import os
+from copy import copy
+
+import numpy as np
+import pandas as pd
+
+import anndata
+import scanpy as sc
+
+from . import PACKAGE_DIR
+
+import torch
+import random
+
+
+
+def seed_everything(TORCH_SEED):
+    random.seed(TORCH_SEED)
+    os.environ['PYTHONHASHSEED'] = str(TORCH_SEED)
+    np.random.seed(TORCH_SEED)
+    torch.manual_seed(TORCH_SEED)
+    torch.cuda.manual_seed_all(TORCH_SEED)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+class Similarity:
+    def __init__(self, r_path, model_file):
+        self.r_path = r_path
+
+        os.environ["R_HOME"] = self.r_path
+        from rpy2.robjects.packages import importr
+        base = importr('base')
+        miloR = importr('miloR')
+
+        self.milo_model = base.readRDS(f"{model_file}")
+
+    def run_milo(self, adata, sample_name):
+
+        os.environ["R_HOME"] = self.r_path
+        import rpy2.robjects as robjects
+        import anndata2ri
+        anndata2ri.activate()
+
+        adata_sce = anndata2ri.py2rpy(adata)
+
+        r_source = robjects.r['source']
+
+        milo_file = os.path.join(PACKAGE_DIR, "r", "run_milo.R")
+        r_source(milo_file)
+
+        r_getname = robjects.globalenv['run_milo']
+
+        res = r_getname(self.milo_model, adata_sce, 'cell_ontology_class')
+        res['sample']=sample_name
+
+        return res
+
+