minor updates

screenpro/phenoScore.py

@@ -273,57 +273,3 @@ def matrixTest(x, y, x_ctrl, y_ctrl, math, ave_reps, test = 'ttest', growth_rate
     p_values = matrixStat(x, y, test=test, ave_reps=ave_reps)
 
     return scores, p_values
-
-
-def ann_score_df(df_in, up_hit='resistance_hit', down_hit='sensitivity_hit', ctrl_label='non-targeting', threshold=10):
-    """
-    Annotate score dataframe with hit labels using given `threshold`
-    (i.e. `score/pseudo_sd * -np.log10(pvalue) >= threshold`).
-    Args:
-        df_in (pd.DataFrame): score dataframe
-        up_hit (str): up hit label
-        down_hit (str): down hit label
-        ctrl_label (str): control label
-        threshold (int): threshold
-    Returns:
-        pd.DataFrame: annotated score dataframe
-    """
-    # make a copy of input dataframe
-    df = df_in.copy()
-
-    # rename/reformat columns
-    df.columns = ['target', 'score', 'pvalue']
-    df['score'] = df['score'].astype(float)
-    df['pvalue'] = df['pvalue'].astype(float)
-
-    # calculate pseudo_sd
-    pseudo_sd = df[df['target'].str.contains(ctrl_label)]['score'].tolist()
-    pseudo_sd = np.std(pseudo_sd)
-
-    df['label'] = '.'
-
-    # annotate hits: up
-    df.loc[
-        (df['score'] > 0) & (~df['target'].str.contains(ctrl_label)) &
-        (df['score']/pseudo_sd * -np.log10(df['pvalue']) >= threshold), 'label'
-    ] = up_hit
-
-    # annotate hits: down
-    df.loc[
-        (df['score'] < 0) & (~df['target'].str.contains(ctrl_label)) &
-        (df['score']/pseudo_sd * -np.log10(df['pvalue']) <= -threshold), 'label'
-    ] = down_hit
-
-    # annotate control
-    df.loc[df['target'].str.contains(ctrl_label), 'label'] = ctrl_label
-
-    # annotate non-hit
-    df.loc[df['label'] == '.', 'label'] = 'target_non_hit'
-
-    # reorder factors
-    df['label'] = pd.Categorical(
-        df['label'],
-        categories=[down_hit, up_hit, ctrl_label, 'target_non_hit']
-    )
-
-    return df

screenpro/plotting.py

@@ -3,7 +3,7 @@
 import scanpy as sc
 import matplotlib.pyplot as plt
 import matplotlib
-from phenoScore import ann_score_df
+from .utils import ann_score_df
 
 # variables
 almost_black = '#111111'