merge inh/act into feature

dashboard/data/bmg_plate.py

@@ -5,12 +5,6 @@
 from enum import Enum, auto
 
 
-class Mode(Enum):
-    ACTIVATION = auto()
-    INHIBITION = auto()
-    ALL = auto()
-
-
 PlateSummary = namedtuple(
     "PlateSummary",
     [
@@ -194,7 +188,7 @@ def parse_bmg_files(files: tuple[str, io.StringIO]) -> tuple[pd.DataFrame, np.nd
 def calculate_activation_inhibition_zscore(
     values: np.ndarray,
     stats: dict,
-    mode: Mode = Mode.ALL,
+    mode: str,
     without_pos: bool = False,
 ) -> tuple[np.ndarray]:
     """
@@ -207,8 +201,7 @@ def calculate_activation_inhibition_zscore(
     :return: activation, inhibition and z-score values
     """
     activation, inhibition = None, None
-    if mode == Mode.ACTIVATION or mode == Mode.ALL:
-        # NOTE: for now `without_pos` is not used
+    if mode == "activation":
         if without_pos:
             activation = (values - stats["mean_neg"]) / (stats["mean_neg"]) * 100
         else:
@@ -218,25 +211,28 @@ def calculate_activation_inhibition_zscore(
                 * 100
             )
 
-    if mode == Mode.INHIBITION or mode == Mode.ALL:
+    if mode == "inhibition":
         inhibition = (
-            1 - ((values - stats["mean_pos"])) / (stats["mean_neg"] - stats["mean_pos"])
-        ) * 100
+            (values - stats["mean_neg"]) / (stats["mean_pos"] - stats["mean_neg"]) * 100
+        )
 
     z_score = (values - stats["mean_cmpd"]) / stats["std_cmpd"]
 
     return activation, inhibition, z_score
 
 
 def get_activation_inhibition_zscore_dict(
-    df_stats: pd.DataFrame, plate_values: np.ndarray, modes: dict[Mode]
+    df_stats: pd.DataFrame,
+    plate_values: np.ndarray,
+    mode: str,
+    without_pos: bool = False,
 ) -> dict[str, dict[str, float]]:
     """
     Calculates activation and inhibition for each compound in the plates.
 
     :param df_stats: dataframe with statistics for each plate
     :param plate_values: array with values in the plate
-    :param mode: list of modes to calculate activation and inhibition
+    :param mode: mode to calculate activation and inhibition
     :return: dictionary with activation and inhibition values for each compound in the plate
     """
     stats = {}
@@ -251,11 +247,8 @@ def get_activation_inhibition_zscore_dict(
 
     act_inh_dict = {}
     for (_, row_stats), v in zip(df_stats.iterrows(), plate_values):
-        mode = (
-            modes[row_stats["barcode"]] if row_stats["barcode"] in modes else Mode.ALL
-        )
         activation, inhibition, z_score = calculate_activation_inhibition_zscore(
-            v[0], stats, mode=mode
+            v[0], stats, mode, without_pos
         )
         act_inh_dict[row_stats["barcode"]] = {
             "activation": activation,

dashboard/data/combine.py

@@ -3,7 +3,7 @@
 import numpy as np
 import pandas as pd
 
-from dashboard.data.bmg_plate import Mode, get_activation_inhibition_zscore_dict
+from dashboard.data.bmg_plate import get_activation_inhibition_zscore_dict
 
 
 def values_array_to_column(
@@ -93,23 +93,24 @@ def combine_bmg_echo_data(
     echo_df: pd.DataFrame,
     df_stats: pd.DataFrame,
     plate_values: np.ndarray,
-    modes: dict[Mode] = None,
+    mode: str,
+    without_pos: bool = False,
 ) -> pd.DataFrame:
     """
     Combine Echo data with activation and inhibition values.
 
     :param echo_df: dataframe with Echo data
     :param df_stats: dataframe containing statistics for each plate
     :param plate_values: numpy array with activation and inhibition values - shape: (#plates, 2, 16, 24)
-    :param modes: dictionary with modes for each plate
+    :param mode: mode to calculate activation and inhibition
     :return: dataframe with Echo data and activation and inhibition values
     """
     PLATE = "Destination Plate Barcode"
     WELL = "Destination Well"
 
-    if modes is None:
-        modes = dict()
-    act_inh_dict = get_activation_inhibition_zscore_dict(df_stats, plate_values, modes)
+    act_inh_dict = get_activation_inhibition_zscore_dict(
+        df_stats, plate_values, mode, without_pos
+    )
     dfs = []
     for barcode, values_dict in act_inh_dict.items():
         activation_inhibition_df = get_activation_inhibition_zscore_df(
@@ -150,7 +151,7 @@ def split_compounds_controls(df: pd.DataFrame) -> tuple[pd.DataFrame]:
 
 
 def aggregate_well_plate_stats(
-    df: pd.DataFrame, assign_x_coords: bool = False
+    df: pd.DataFrame, key: str, assign_x_coords: bool = False
 ) -> tuple[pd.DataFrame]:
     """
     Aggregates the statistics (mean and std) per plate needed for the plots.
@@ -163,33 +164,27 @@ def aggregate_well_plate_stats(
     """
 
     PLATE = "Destination Plate Barcode"
-    ACTIVATION = "% ACTIVATION"
-    INHIBITION = "% INHIBITION"
     Z_SCORE = "Z-SCORE"
 
     stats_df = (
-        df.groupby(PLATE)[[ACTIVATION, INHIBITION, Z_SCORE]]
+        df.groupby(PLATE)[[key, Z_SCORE]]
         .agg(["mean", "std", "min", "max"])
         .reset_index()
     )
     stats_df.columns = [
         PLATE,
-        f"{ACTIVATION}_mean",
-        f"{ACTIVATION}_std",
-        f"{ACTIVATION}_min",
-        f"{ACTIVATION}_max",
-        f"{INHIBITION}_mean",
-        f"{INHIBITION}_std",
-        f"{INHIBITION}_min",
-        f"{INHIBITION}_max",
+        f"{key}_mean",
+        f"{key}_std",
+        f"{key}_min",
+        f"{key}_max",
         f"{Z_SCORE}_mean",
         f"{Z_SCORE}_std",
         f"{Z_SCORE}_min",
         f"{Z_SCORE}_max",
     ]
 
     if assign_x_coords:
-        for col in [ACTIVATION, INHIBITION, Z_SCORE]:
+        for col in [key, Z_SCORE]:
             stats_df = stats_df.sort_values(by=f"{col}_mean")  # sort by mean
             stats_df[f"{col}_x"] = range(len(stats_df))  # get the x coordinates
 

dashboard/pages/components.py

@@ -22,6 +22,7 @@
         dcc.Store(id="z-slider-value", storage_type="local"),
         dcc.Store(id="report-data-correlation-plots", storage_type="local"),
         dcc.Store(id="report-data-hit-validation-input", storage_type="local"),
+        dcc.Store(id="activation-inhibition-screening-options", storage_type="local"),
     ],
 )