Adding normalize step to recipe

1.generate-profiles/2.normalize.py

@@ -0,0 +1,60 @@
+import os
+import sys
+import pathlib
+import argparse
+import warnings
+import pandas as pd
+
+from pycytominer import normalize
+
+sys.path.append("../scripts")
+from config_utils import process_config_file
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--config_file",
+    help="configuration yaml file for the profiling pipeline",
+    default="profiling_config.yaml",
+)
+args = parser.parse_args()
+config_file = args.config_file
+
+config = process_config_file(config_file)
+
+# Extract config arguments
+core_args = config["core"]
+batch = core_args["batch"]
+aggregate_args = config["aggregate"]
+normalize_args = config["normalize"]
+
+ignore_files = core_args["ignore_files"]
+float_format = core_args["float_format"]
+compression = core_args["compression"]
+
+normalize_singlecell_from_single_file = core_args["output_one_single_cell_file_only"]
+normalize_levels = normalize_args["levels"]
+normalize_by_samples = normalize_args["by_samples"]
+normalize_these_features = normalize_args["features"]
+normalize_method = normalize_args["method"]
+normalize_input_files = aggregate_args["aggregate_output_files"]
+normalize_output_files = normalize_args["normalize_output_files"]
+
+for data_level in normalize_levels:
+    if data_level == "single_cell":
+        if not normalize_singlecell_from_single_file:
+            continue
+
+    file_to_normalize = normalize_input_files[data_level]
+    output_file = normalize_output_files[data_level]
+
+    print(f"Now normalizing {data_level}...with operation: {normalize_method}")
+
+    normalize_df = normalize(
+        profiles=file_to_normalize,
+        features=normalize_these_features,
+        samples=normalize_by_samples,
+        method=normalize_method,
+        output_file=output_file,
+        compression=compression,
+        float_format=float_format,
+    )

1.generate-profiles/profiling_config.yaml

@@ -10,6 +10,7 @@ core:
   site_dir: ../0.preprocess-sites/data/
   output_single_cell_dir: single_cell/
   output_profile_dir: profiles/
+  output_one_single_cell_file_only: true
   categorize_cell_quality: simple
   compression: gzip
   float_format: "%.5g"
@@ -64,3 +65,14 @@ aggregate:
     guide:
       - Metadata_Foci_Barcode_MatchedTo_GeneCode
       - Metadata_Foci_Barcode_MatchedTo_Barcode
+---
+normalize:
+  perform: true
+  output_basedir: data/profiles
+  method: standardize
+  levels:
+    - gene
+    - guide
+    - single_cell
+  by_samples: all
+  features: infer

scripts/config_utils.py

@@ -83,6 +83,31 @@ def generate_profiles_config(config):
         config["aggregate"]["output_basedir"], batch
     )
 
+    # Build aggregated output files
+    config["aggregate"]["aggregate_output_files"] = {}
+    for aggregate_level, aggregate_columns in config["aggregate"]["levels"].items():
+        config["aggregate"]["aggregate_output_files"][aggregate_level] = pathlib.Path(
+            config["aggregate"]["aggregate_output_dir"],
+            f"{batch}_{aggregate_level}.csv.gz",
+        )
+
+    config["aggregate"]["aggregate_output_files"]["single_cell"] = config[
+        "single_cell"
+    ]["single_file_only_output_file"]
+
+    # Build paths to normalize yaml document
+    config["normalize"]["normalize_output_dir"] = pathlib.Path(
+        config["normalize"]["output_basedir"], batch
+    )
+
+    # Build normalized output files
+    config["normalize"]["normalize_output_files"] = {}
+    for normalize_level in config["normalize"]["levels"]:
+        config["normalize"]["normalize_output_files"][normalize_level] = pathlib.Path(
+            config["normalize"]["normalize_output_dir"],
+            f"{batch}_{normalize_level}_normalized.csv.gz",
+        )
+
     return config