implemented a custom workflow

protzilla/data_analysis/differential_expression_helper.py

@@ -8,7 +8,7 @@
 
 
 def apply_multiple_testing_correction(
-        p_values: list, method: str, alpha: float
+    p_values: list, method: str, alpha: float
 ) -> tuple:
     """
     Applies a multiple testing correction method to a list of p-values
@@ -50,9 +50,9 @@ def _map_log_base(log_base: str) -> int | None:
 
 
 def log_transformed_check(
-        intensity_df: pd.DataFrame,
-        intensity_name: str = None,
-        value_threshold: int = 1000,
+    intensity_df: pd.DataFrame,
+    intensity_name: str = None,
+    value_threshold: int = 1000,
 ) -> bool:
     """
     This function checks a intensity dataframe for a previous log transformation based on the intensity values.
@@ -63,7 +63,7 @@ def log_transformed_check(
         intensity_name = default_intensity_column(intensity_df)
 
     if (
-            intensity_df[intensity_name].max() < value_threshold
+        intensity_df[intensity_name].max() < value_threshold
         or intensity_df[intensity_name].min() < 0
     ):
         return True
@@ -74,7 +74,7 @@ def log_transformed_check(
 INVALID_PROTEINGROUP_DATA_MSG = {
     "level": logging.WARNING,
     "msg": "Due do missing or identical values, the p-values for some protein groups could not be calculated. These groups were omitted from the analysis. "
-           "To prevent this, please add filtering and imputation steps to your workflow before running the analysis.",
+    "To prevent this, please add filtering and imputation steps to your workflow before running the analysis.",
 }
 LOG_TRANSFORMATION_MESSAGE_MSG = {
     "level": logging.INFO,

protzilla/data_analysis/differential_expression_linear_model.py

@@ -6,6 +6,7 @@
 
 from protzilla.data_preprocessing.transformation import by_log
 from protzilla.utilities import default_intensity_column, exists_message
+
 from .differential_expression_helper import (
     BAD_LOG_BASE_INPUT_MSG,
     INVALID_PROTEINGROUP_DATA_MSG,

protzilla/data_analysis/differential_expression_t_test.py

@@ -6,6 +6,7 @@
 
 from protzilla.data_preprocessing.transformation import by_log
 from protzilla.utilities import default_intensity_column, exists_message
+
 from .differential_expression_helper import (
     BAD_LOG_BASE_INPUT_MSG,
     INVALID_PROTEINGROUP_DATA_MSG,

protzilla/run_helper.py

@@ -167,7 +167,6 @@ def log_message(level: int = 40, msg: str = "", trace: str | list[str] = ""):
         log_function(f"{msg}{trace}")
 
 
-
 def log_messages(messages: list[dict] = None):
     """
     Logs a list of messages to the console.
@@ -181,4 +180,4 @@ def log_messages(messages: list[dict] = None):
             message["level"],
             message["msg"],
             message["trace"] if "trace" in message else "",
-        )
+        )

ui/runs/utilities/alert.py

@@ -6,4 +6,4 @@ def build_trace_alert(trace):
         aria-expanded="false" aria-controls="flush-collapseOne">\
         Trace</button></h2><div id="flush-collapseOne" class="accordion-collapse collapse" \
         aria-labelledby="flush-headingOne" data-bs-parent="#accordionFlushExample">\
-        <div class="accordion-body bg-white"><pre>{trace}</pre></div></div></div></div>'
+        <div class="accordion-body bg-white"><pre>{trace}</pre></div></div></div></div>'

ui/runs/views.py

@@ -41,12 +41,7 @@
     make_plot_fields,
     make_sidebar,
 )
-from ui.runs.views_helper import (
-    clear_messages,
-    parameters_from_post,
-    display_messages,
-    display_message,
-)
+from ui.runs.views_helper import display_messages, parameters_from_post
 
 active_runs = {}
 

user_data/workflows/custom.json

@@ -0,0 +1,33 @@
+{
+  "analysis_level": "protein",
+  "sections": {
+    "importing": {
+      "steps": [
+        {
+          "name": "ms_data_import",
+          "method": "max_quant_import",
+          "parameters": {
+            "intensity_name": "iBAQ"
+          }
+        },
+        {
+          "name": "metadata_import",
+          "method": "metadata_import_method",
+          "parameters": {
+            "feature_orientation": "Columns (samples in rows, features in columns)"
+          }
+        }
+      ]
+    },
+    "data_preprocessing": {
+      "steps": []
+    },
+    "data_analysis": {
+      "steps": []
+    },
+    "data_integration": {
+      "steps": []
+    }
+  }
+}
+