Merge pull request #79 from Sage-Bionetworks/jbeck/AG-1157/create_bio…

…domains_info_transform

Added transform for biodomain_info

config.yaml

@@ -1,6 +1,19 @@
 - destination: &dest syn12177492
 - staging_path: ./staging
 - datasets:
+    - biodomain_info:
+        files:
+          - name: genes_biodomains
+            id: syn44151254.1
+            format: csv
+        final_format: json
+        custom_transformations: 1
+        provenance:
+          - syn44151254.1
+        column_rename:
+          biodomain: name
+        destination: *dest
+
     - genes_biodomains:
         files:
           - name: genes_biodomains

src/agoradatatools/etl/transform/__init__.py

@@ -4,6 +4,9 @@
     transform_distribution_data,
 )
 from agoradatatools.etl.transform.gene_info import transform_gene_info
+from agoradatatools.etl.transform.biodomain_info import (
+    transform_biodomain_info,
+)
 from agoradatatools.etl.transform.genes_biodomains import (
     transform_genes_biodomains,
 )
@@ -22,6 +25,7 @@
 __all__ = [
     "transform_distribution_data",
     "transform_gene_info",
+    "transform_biodomain_info",
     "transform_genes_biodomains",
     "transform_overall_scores",
     "create_proteomics_distribution_data",

src/agoradatatools/etl/transform/biodomain_info.py

@@ -0,0 +1,25 @@
+import pandas as pd
+
+
+def transform_biodomain_info(datasets: dict) -> pd.DataFrame:
+    """Takes dictionary of dataset DataFrames, extracts the genes_biodomains
+    DataFrame, gets a unique list of biodomain names, and outputs the list as
+    a single-column DataFrame with column "name".
+
+    Args:
+        datasets (dict[str, pd.DataFrame]): dictionary of dataset names mapped to their DataFrame
+
+    Returns:
+        pd.DataFrame: 1-column DataFrame with column "name"
+    """
+    genes_biodomains = datasets["genes_biodomains"]
+    biodomain_info = (
+        genes_biodomains["name"]
+        .dropna()
+        .drop_duplicates()
+        .reset_index()
+        .drop(columns="index")
+        .sort_values(by="name", ignore_index=True)
+    )
+
+    return biodomain_info

src/agoradatatools/process.py

@@ -14,6 +14,8 @@
 def apply_custom_transformations(datasets: dict, dataset_name: str, dataset_obj: dict):
     if not isinstance(datasets, dict) or not isinstance(dataset_name, str):
         return None
+    if dataset_name == "biodomain_info":
+        return transform.transform_biodomain_info(datasets=datasets)
     if dataset_name == "genes_biodomains":
         return transform.transform_genes_biodomains(datasets=datasets)
     if dataset_name == "overall_scores":

test_config.yaml

@@ -1,6 +1,19 @@
 - destination: &dest syn17015333
 - staging_path: ./staging
 - datasets:
+    - biodomain_info:
+        files:
+          - name: genes_biodomains
+            id: syn44151254.1
+            format: csv
+        final_format: json
+        custom_transformations: 1
+        provenance:
+          - syn44151254.1
+        column_rename:
+          biodomain: name
+        destination: *dest
+
     - genes_biodomains:
         files:
           - name: genes_biodomains

tests/test_assets/biodomain_info/input/biodomain_info_good_input.csv

@@ -0,0 +1,7 @@
+name,abbr,label,color,go_id,goterm_name,ensembl_id
+Proteostasis,Pr,Proteostasis [Pr],#c8b269,GO:0048199,"vesicle targeting, to, from or within Golgi",ENSG00000170348
+Proteostasis,Pr,Proteostasis [Pr],#c8b269,GO:0007030,Golgi organization,ENSG00000243414
+Immune Response,IR,Immune Response [IR],#9ccdcc,GO:0006955,immune response,ENSG00000115008
+Immune Response,IR,Immune Response [IR],#9ccdcc,GO:0006955,immune response,ENSG00000278006
+Immune Response,IR,Immune Response [IR],#9ccdcc,GO:0006955,immune response,ENSG00000275313
+Tau Homeostasis,TH,Tau Homeostasis [TH],#cb97cb,GO:1902988,neurofibrillary tangle assembly,ENSG00000186868

tests/test_assets/biodomain_info/input/biodomain_info_imperfect_input.csv

@@ -0,0 +1,8 @@
+name,abbr,label,color,go_id,goterm_name,ensembl_id
+,Pr,Proteostasis [Pr],#c8b269,GO:0048199,"vesicle targeting, to, from or within Golgi",ENSG00000170348
+Proteostasis,,Proteostasis [Pr],#c8b269,GO:0007030,Golgi organization,ENSG00000243414
+Immune Response,IR,,#9ccdcc,GO:0006955,immune response,
+Immune Response,IR,Immune Response [IR],,GO:0006955,immune response,ENSG00000278006
+Immune Response,IR,Immune Response [IR],#9ccdcc,,immune response,ENSG00000275313
+Tau Homeostasis,TH,Tau Homeostasis [TH],#cb97cb,GO:1902988,,ENSG00000186868
+,Ap,Apoptosis [Ap],#673399,GO:0006915,apoptotic process,ENSG00000125538

tests/test_assets/biodomain_info/output/biodomain_info_good_output.json

@@ -0,0 +1,11 @@
+[
+  {
+    "name": "Immune Response"
+  },
+  {
+    "name": "Proteostasis"
+  },
+  {
+    "name": "Tau Homeostasis"
+  }
+]

tests/test_assets/biodomain_info/output/biodomain_info_imperfect_output.json

@@ -0,0 +1,11 @@
+[
+  {
+    "name": "Immune Response"
+  },
+  {
+    "name": "Proteostasis"
+  },
+  {
+    "name": "Tau Homeostasis"
+  }
+]

tests/transform/test_biodomain_info.py

@@ -0,0 +1,57 @@
+import os
+import pandas as pd
+import pytest
+
+from agoradatatools.etl.transform import biodomain_info
+
+
+class TestTransformBiodomainInfo:
+    data_files_path = "tests/test_assets/biodomain_info"
+    pass_test_data = [
+        (  # Pass with good data
+            "biodomain_info_good_input.csv",
+            "biodomain_info_good_output.json",
+        ),
+        (  # Pass with values missing from each column
+            "biodomain_info_imperfect_input.csv",
+            "biodomain_info_imperfect_output.json",
+        ),
+    ]
+    pass_test_ids = [
+        "Pass with good data",
+        "Pass with missing values in each column",
+    ]
+    fail_test_data = [
+        # No failure cases for this transform
+    ]
+    fail_test_ids = [
+        # No failure cases for this transform
+    ]
+
+    @pytest.mark.parametrize(
+        "biodomain_info_file, expected_output_file", pass_test_data, ids=pass_test_ids
+    )
+    def test_transform_biodomain_info_should_pass(
+        self, biodomain_info_file, expected_output_file
+    ):
+        biodomain_info_df = pd.read_csv(
+            os.path.join(self.data_files_path, "input", biodomain_info_file)
+        )
+        output_df = biodomain_info.transform_biodomain_info(
+            datasets={"genes_biodomains": biodomain_info_df}
+        )
+        expected_df = pd.read_json(
+            os.path.join(self.data_files_path, "output", expected_output_file)
+        )
+        pd.testing.assert_frame_equal(output_df, expected_df)
+
+    """
+    # Leaving code stub for failure case, in case we want to add this in the future
+    @pytest.mark.parametrize("biodomain_info_file", fail_test_data, ids=fail_test_ids)
+    def test_transform_biodomain_info_should_fail(self, biodomain_info_file):
+        with pytest.raises(<Error type>):
+            biodomain_info_df = pd.read_csv(os.path.join(self.data_files_path, "input", biodomain_info_file))
+            biodomain_info.transform_biodomain_info(
+                datasets={"genes_biodomains": biodomain_info_df}
+            )
+    """