From b36085dce3eca956ab4ea44e4f8efceb2a55b375 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Tue, 16 Jul 2024 19:21:48 -0700
Subject: [PATCH 01/34] reorganize plotting features

---
 screenpro/__init__.py                         |  11 +-
 .../dashboard.py => dashboard/__init__.py}    |   6 +
 screenpro/plotting/__init__.py                |   9 +
 screenpro/{visualize => plotting}/qc_plots.py |   2 -
 screenpro/{visualize => plotting}/utils.py    |   3 +
 screenpro/plotting/volcano.py                 | 149 +++++++++
 screenpro/visualize/__init__.py               | 308 ------------------
 screenpro/visualize/rank.py                   |  70 ----
 8 files changed, 174 insertions(+), 384 deletions(-)
 rename screenpro/{visualize/dashboard.py => dashboard/__init__.py} (98%)
 create mode 100644 screenpro/plotting/__init__.py
 rename screenpro/{visualize => plotting}/qc_plots.py (99%)
 rename screenpro/{visualize => plotting}/utils.py (97%)
 create mode 100644 screenpro/plotting/volcano.py
 delete mode 100644 screenpro/visualize/__init__.py
 delete mode 100644 screenpro/visualize/rank.py

diff --git a/screenpro/__init__.py b/screenpro/__init__.py
index 13ad8d1..f51c26b 100644
--- a/screenpro/__init__.py
+++ b/screenpro/__init__.py
@@ -18,15 +18,18 @@
 - datasets: API for accessing pre-processed datasets
 '''
 
-from . import phenoscore as ps
-from . import preprocessing as pp
 from . import ngs
-from . import assays
 from . import load
-from . import visualize as viz
+from . import preprocessing as pp
+from . import phenoscore as ps
+from . import assays
+from . import plotting as pl
+from . import dashboard
 
 from .ngs import GuideCounter
 from .assays import PooledScreens, GImaps
+from .dashboard import DrugScreenDashboard
+
 
 __version__ = "0.4.5"
 __author__ = "Abe Arab"
diff --git a/screenpro/visualize/dashboard.py b/screenpro/dashboard/__init__.py
similarity index 98%
rename from screenpro/visualize/dashboard.py
rename to screenpro/dashboard/__init__.py
index a36851a..594392d 100644
--- a/screenpro/visualize/dashboard.py
+++ b/screenpro/dashboard/__init__.py
@@ -1,3 +1,9 @@
+## Copyright (c) 2022-2024 ScreenPro2 Development Team.
+## All rights reserved.
+## Gilbart Lab, UCSF / Arc Institute.
+## Multi-Omics Tech Center, Arc Insititue.
+
+
 import numpy as np
 import pandas as pd
 import bokeh
diff --git a/screenpro/plotting/__init__.py b/screenpro/plotting/__init__.py
new file mode 100644
index 0000000..ccb5576
--- /dev/null
+++ b/screenpro/plotting/__init__.py
@@ -0,0 +1,9 @@
+## Copyright (c) 2022-2024 ScreenPro2 Development Team.
+## All rights reserved.
+## Gilbart Lab, UCSF / Arc Institute.
+## Multi-Omics Tech Center, Arc Insititue.
+
+import numpy as np
+import scanpy as sc
+from .qc_plots import plotReplicateScatter
+from .rank import rankPlot
diff --git a/screenpro/visualize/qc_plots.py b/screenpro/plotting/qc_plots.py
similarity index 99%
rename from screenpro/visualize/qc_plots.py
rename to screenpro/plotting/qc_plots.py
index 5f00606..dab57cc 100644
--- a/screenpro/visualize/qc_plots.py
+++ b/screenpro/plotting/qc_plots.py
@@ -38,5 +38,3 @@ def plotReplicateScatter(ax, adat_in, x, y, title, min_val=None, max_val=None, l
     ax.get_legend().remove()
 
     ax.grid(False)
-
-
diff --git a/screenpro/visualize/utils.py b/screenpro/plotting/utils.py
similarity index 97%
rename from screenpro/visualize/utils.py
rename to screenpro/plotting/utils.py
index 84b9c7e..34fa620 100644
--- a/screenpro/visualize/utils.py
+++ b/screenpro/plotting/utils.py
@@ -20,6 +20,9 @@
     'YlBu', [(0, '#0000ff'), (.49, '#000000'), (.51, '#000000'), (1, '#ffff00')])
 yellow_blue.set_bad('#999999', 1)
 
+up_hit_color = '#fcae91'
+down_hit_color = '#bdd7e7'
+
 # plt.rcParams['font.sans-serif'] = [
 #     'Helvetica', 'Arial', 'Verdana', 'Bitstream Vera Sans'
 # ]
diff --git a/screenpro/plotting/volcano.py b/screenpro/plotting/volcano.py
new file mode 100644
index 0000000..3aeba09
--- /dev/null
+++ b/screenpro/plotting/volcano.py
@@ -0,0 +1,149 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from .utils import yellow_blue, up_hit_color, down_hit_color
+
+
+class ScatterPlot:
+    def __init__(
+            self, df, up_hit, down_hit, ax,
+            score_col, pvalue_col, xlabel, ylabel,
+            ctrl_label='negative_control',
+            dot_size=1,
+            xlims='auto',
+            ylims='auto',
+            **args
+            ):
+        if f'-log10({pvalue_col})' not in df.columns:
+            df[f'-log10({pvalue_col})'] = np.log10(df[pvalue_col]) * -1
+
+        if xlims == 'auto':
+            xlims = (df[score_col].min() - 0.1, df[score_col].max() + 0.1)
+        if ylims == 'auto':
+            ylims = (df[f'-log10({pvalue_col})'].min() - 0.1, df[f'-log10({pvalue_col})'].max() + 0.1)
+        
+        # Scatter plot for each category
+        ax.scatter( df.loc[df['label'] == 'target_non_hit', score_col],
+                    df.loc[df['label'] == 'target_non_hit', f'-log10({pvalue_col})'],
+                    alpha=0.1, s=dot_size, c='black', label='target_non_hit',
+                    **args)
+        
+        ax.scatter( df.loc[df['label'] == up_hit, score_col], 
+                    df.loc[df['label'] == up_hit, f'-log10({pvalue_col})'],
+                    alpha=0.9, s=dot_size, c=up_hit_color, label=up_hit,
+                    **args)
+        
+        ax.scatter( df.loc[df['label'] == down_hit, score_col], 
+                    df.loc[df['label'] == down_hit, f'-log10({pvalue_col})'],
+                    alpha=0.9, s=dot_size, c=down_hit_color, label=down_hit,
+                    **args)
+        
+        ax.scatter( df.loc[df['label'] == ctrl_label, score_col],
+                    df.loc[df['label'] == ctrl_label, f'-log10({pvalue_col})'],
+                    alpha=0.1, s=dot_size, c='gray', label=ctrl_label,
+                    **args)
+
+        # Set x-axis and y-axis labels
+        ax.set_xlabel(xlabel)
+        ax.set_ylabel(ylabel)
+
+        # Set x-axis limits
+        ax.set_xlim(xlims)
+
+        # Set y-axis limits
+        ax.set_ylim(ylims)
+
+        # Add legend
+        ax.legend()
+
+        return ax
+
+    def _label_by_color(self, ax, df_in, label, 
+                        x_col, y_col,
+                        size=2, size_txt="auto",
+                        edgecolors='black', facecolors='black',
+                        textcolor='black',
+                        t_x=.5, t_y=-0.1, **args):
+        
+        df = df_in.copy()
+        target_data = df[df['target'] == label]
+
+        # Scatter plot for labeled data
+        ax.scatter(
+            target_data[x_col], target_data[y_col],
+            s=size, linewidth=0.5, 
+            edgecolors=edgecolors, 
+            facecolors=facecolors, label='target',
+            **args
+        )
+
+        if size_txt == None:
+            pass
+        elif size_txt == 'auto':
+            size_txt = size * 2
+        else:
+            # Annotate the points
+            for i, _ in enumerate(target_data['target']):
+                txt = target_data['target'].iloc[i]
+                ax.annotate(txt, (target_data[x_col].iloc[i] + t_x, target_data[y_col].iloc[i] + t_y),
+                            color=textcolor, size=size_txt)
+
+    def label_as_black(self, ax, df_in, label, 
+                       x_col='score', y_col='-log10(pvalue)',
+                       size=2, size_txt="auto",
+                       t_x=.5, t_y=-0.1,
+                       **args):
+        self._label_by_color(ax, df_in, label, 
+                             x_col=x_col, y_col=y_col,
+                             size=size, size_txt=size_txt,
+                             edgecolors='black', facecolors='black',
+                             textcolor='black',
+                             t_x=t_x, t_y=t_y,
+                             **args)
+    
+    def label_sensitivity_hit(self, ax, df_in, label, 
+                              x_col='score', y_col='-log10(pvalue)',
+                              size=2, size_txt="auto",
+                              t_x=.5, t_y=-0.1,
+                              **args):
+        self._label_by_color(ax, df_in, label, 
+                             x_col=x_col, y_col=y_col,
+                             size=size, size_txt=size_txt,
+                             edgecolors='black', facecolors='#3182bd',
+                             textcolor='black',
+                             t_x=t_x, t_y=t_y,
+                             **args)
+    
+    def label_resistance_hit(self, ax, df_in, label, 
+                             x_col='score', y_col='-log10(pvalue)',
+                             size=2, size_txt="auto",
+                             t_x=.5, t_y=-0.1,
+                             **args):
+        self._label_by_color(ax, df_in, label, 
+                             x_col=x_col, y_col=y_col,
+                             size=size, size_txt=size_txt,
+                             edgecolors='black', facecolors='#de2d26',
+                             textcolor='black',
+                             t_x=t_x, t_y=t_y,
+                             **args)
+
+
+class VolcanoPlot(ScatterPlot):
+    def __init__(
+            self, df, up_hit, down_hit, ax,
+            score_col='score', pvalue_col='pvalue',
+            xlabel='phenotype score',
+            ylabel='-log10(pvalue)',
+            ctrl_label='negative_control',
+            dot_size=1,
+            xlims='auto',
+            ylims='auto',
+            **args
+            ):
+        super().__init__(df, up_hit, down_hit, ax,
+                         score_col=score_col, pvalue_col=pvalue_col,
+                         xlabel=xlabel, ylabel=ylabel,
+                         ctrl_label=ctrl_label,
+                         dot_size=dot_size,
+                         xlims=xlims, ylims=ylims,
+                         **args)
diff --git a/screenpro/visualize/__init__.py b/screenpro/visualize/__init__.py
deleted file mode 100644
index 92c5a5a..0000000
--- a/screenpro/visualize/__init__.py
+++ /dev/null
@@ -1,308 +0,0 @@
-## Copyright (c) 2022-2024 ScreenPro2 Development Team.
-## All rights reserved.
-## Gilbart Lab, UCSF / Arc Institute.
-## Multi-Omics Tech Center, Arc Insititue.
-
-import numpy as np
-import scanpy as sc
-from .qc_plots import plotReplicateScatter
-from .rank import rankPlot
-
-
-## Phenotype plotting functions
-class DrugScreenPlotter:
-    def __init__(self, screen, treated, untreated, t0='T0', threshold=3, ctrl_label='negative_control',run_name='auto'):
-        self.screen = screen
-        self.threshold = threshold
-        self.ctrl_label = ctrl_label
-        self.run_name = run_name
-        self.gamma_score_name = f'gamma:{untreated}_vs_{t0}'
-        self.rho_score_name = f'rho:{treated}_vs_{untreated}'
-        self.tau_score_name = f'tau:{treated}_vs_{t0}'
-
-    def _label_by_color(self, ax, df_in, label, 
-                        x_col, y_col,
-                        size=2, size_txt="auto",
-                        edgecolors='black', facecolors='black',
-                        textcolor='black',
-                        t_x=.5, t_y=-0.1, **args):
-        
-        df = df_in.copy()
-        target_data = df[df['target'] == label]
-
-        # Scatter plot for labeled data
-        ax.scatter(
-            target_data[x_col], target_data[y_col],
-            s=size, linewidth=0.5, 
-            edgecolors=edgecolors, 
-            facecolors=facecolors, label='target',
-            **args
-        )
-
-        if size_txt == None:
-            pass
-        elif size_txt == 'auto':
-            size_txt = size * 2
-        else:
-            # Annotate the points
-            for i, _ in enumerate(target_data['target']):
-                txt = target_data['target'].iloc[i]
-                ax.annotate(txt, (target_data[x_col].iloc[i] + t_x, target_data[y_col].iloc[i] + t_y),
-                            color=textcolor, size=size_txt)
-
-    def _prep_data(self, score_col='score', pvalue_col='pvalue'):
-
-        gamma = self.screen.getPhenotypeScores(
-            run_name=self.run_name,
-            score_name=self.gamma_score_name,
-            threshold=self.threshold,
-            ctrl_label=self.ctrl_label,
-            score_col=score_col,
-            pvalue_col=pvalue_col
-        )
-
-        gamma[f'-log10({pvalue_col})'] = np.log10(gamma[pvalue_col]) * -1
-
-        tau = self.screen.getPhenotypeScores(
-            run_name=self.run_name,
-            score_name=self.tau_score_name,
-            threshold=self.threshold,
-            ctrl_label=self.ctrl_label,
-            score_col=score_col,
-            pvalue_col=pvalue_col
-        )
-        tau[f'-log10({pvalue_col})'] = np.log10(tau[pvalue_col]) * -1
-
-        rho = self.screen.getPhenotypeScores(
-            run_name=self.run_name,
-            score_name=self.rho_score_name,
-            threshold=self.threshold,
-            ctrl_label=self.ctrl_label,
-            score_col=score_col,
-            pvalue_col=pvalue_col
-        )
-        rho[f'-log10({pvalue_col})'] = np.log10(rho[pvalue_col]) * -1
-
-        return gamma, tau, rho
-    
-    def _volcano(
-            self, ax, df, up_hit, down_hit,
-            score_col='score', pvalue_col='pvalue',
-            xlabel='phenotype score',
-            ylabel='-log10(pvalue)',
-            dot_size=1,
-            xlims='auto',
-            ylims='auto',
-            **args
-            ):
-        
-        if f'-log10({pvalue_col})' not in df.columns:
-            df[f'-log10({pvalue_col})'] = np.log10(df[pvalue_col]) * -1
-
-        if xlims == 'auto':
-            xlims = (df[score_col].min() - 0.1, df[score_col].max() + 0.1)
-        if ylims == 'auto':
-            ylims = (df[f'-log10({pvalue_col})'].min() - 0.1, df[f'-log10({pvalue_col})'].max() + 0.1)
-        
-        # Scatter plot for each category
-        ax.scatter( df.loc[df['label'] == 'target_non_hit', score_col],
-                    df.loc[df['label'] == 'target_non_hit', f'-log10({pvalue_col})'],
-                    alpha=0.1, s=dot_size, c='black', label='target_non_hit',
-                    **args)
-        
-        ax.scatter( df.loc[df['label'] == up_hit, score_col], 
-                    df.loc[df['label'] == up_hit, f'-log10({pvalue_col})'],
-                    alpha=0.9, s=dot_size, c='#fcae91', label=up_hit,
-                    **args)
-        
-        ax.scatter( df.loc[df['label'] == down_hit, score_col], 
-                    df.loc[df['label'] == down_hit, f'-log10({pvalue_col})'],
-                    alpha=0.9, s=dot_size, c='#bdd7e7', label=down_hit,
-                    **args)
-        
-        ax.scatter( df.loc[df['label'] == self.ctrl_label, score_col],
-                    df.loc[df['label'] == self.ctrl_label, f'-log10({pvalue_col})'],
-                    alpha=0.1, s=dot_size, c='gray', label=self.ctrl_label,
-                    **args)
-
-        # Set x-axis and y-axis labels
-        ax.set_xlabel(xlabel)
-        ax.set_ylabel(ylabel)
-
-        # Set x-axis limits
-        ax.set_xlim(xlims)
-
-        # Set y-axis limits
-        ax.set_ylim(ylims)
-
-        # Add legend
-        ax.legend()
-
-    def drawVolcanoRho(
-            self, ax,
-            rho_df=None,
-            dot_size=1,
-            score_col='score', 
-            pvalue_col='pvalue',
-            xlabel='auto',
-            ylabel='-log10(pvalue)',
-            xlims='auto',
-            ylims='auto',
-            **args
-            ):
-        if rho_df is None:
-            _, _, rho_df = self._prep_data()
-        if xlabel == 'auto':
-            xlabel = self.rho_score_name.replace(':', ': ').replace('_', ' ')
-        
-        self._volcano(ax, rho_df, 
-                      up_hit='resistance_hit', down_hit='sensitivity_hit',
-                      score_col=score_col, pvalue_col=pvalue_col,
-                      xlabel=xlabel, ylabel=ylabel,
-                      dot_size=dot_size, xlims=xlims, ylims=ylims,
-                      **args)
-    
-    def drawVolcanoGamma(
-            self, ax,
-            gamma_df=None,
-            dot_size=1,
-            score_col='score', 
-            pvalue_col='pvalue',
-            xlabel='auto',
-            ylabel='-log10(pvalue)',
-            xlims='auto',
-            ylims='auto',
-            **args
-            ):
-        if gamma_df is None:
-            gamma_df, _, _ = self._prep_data()
-        if xlabel == 'auto':
-            xlabel = self.gamma_score_name.replace(':', ': ').replace('_', ' ')
-        
-        self._volcano(ax, gamma_df, 
-                      up_hit='up_hit', down_hit='essential_hit',
-                      score_col=score_col, pvalue_col=pvalue_col,
-                      xlabel=xlabel, ylabel=ylabel,
-                      dot_size=dot_size, xlims=xlims, ylims=ylims,
-                      **args)
-        
-    def drawVolcanoTau(
-            self, ax,
-            tau_df=None,
-            dot_size=1,
-            score_col='score', 
-            pvalue_col='pvalue',
-            xlabel='auto',
-            ylabel='-log10(pvalue)',
-            xlims='auto',
-            ylims='auto',
-            **args
-            ):
-        if tau_df is None:
-            _, tau_df, _, = self._prep_data()
-        if xlabel == 'auto':
-            xlabel = self.tau_score_name.replace(':', ': ').replace('_', ' ')
-        
-        self._volcano(ax, tau_df, 
-                      up_hit='up_hit', down_hit='down_hit',
-                      score_col=score_col, pvalue_col=pvalue_col,
-                      xlabel=xlabel, ylabel=ylabel,
-                      dot_size=dot_size, xlims=xlims, ylims=ylims,
-                      **args)
-
-    def drawRhoGammaScatter(
-            self, ax,
-            rho_df=None, gamma_df=None,
-            dot_size=1,
-            score_col='score',
-            xlabel='auto',
-            ylabel='auto',
-            xlims='auto',
-            ylims='auto',
-            **args
-            ):
-        #TODO: fix by making a single dataframe with both rho and gamma scores
-        if rho_df is None:
-            _, _, rho_df = self._prep_data()
-        if gamma_df is None:
-            gamma_df, _, _ = self._prep_data()
-
-        if xlabel == 'auto':
-            xlabel = self.rho_score_name.replace(':', ': ').replace('_', ' ')
-        if ylabel == 'auto':
-            ylabel = self.gamma_score_name.replace(':', ': ').replace('_', ' ')
-        
-        # color by rho score labels
-        up_hit = 'resistance_hit'
-        down_hit = 'sensitivity_hit'
-
-        # Scatter plot for each category
-        ax.scatter( rho_df.loc[rho_df['label'] == 'target_non_hit', score_col],
-                    gamma_df.loc[rho_df['label'] == 'target_non_hit', score_col],
-                    alpha=0.1, s=dot_size, c='black', label='target_non_hit',
-                    **args)
-        
-        ax.scatter( rho_df.loc[rho_df['label'] == up_hit, score_col],
-                    gamma_df.loc[rho_df['label'] == up_hit, score_col],
-                    alpha=0.9, s=dot_size, c='#fcae91', label=up_hit,
-                    **args)
-        
-        ax.scatter( rho_df.loc[rho_df['label'] == down_hit, score_col],
-                    gamma_df.loc[rho_df['label'] == down_hit, score_col],
-                    alpha=0.9, s=dot_size, c='#bdd7e7', label=down_hit,
-                    **args)
-        
-        ax.scatter( rho_df.loc[rho_df['label'] == self.ctrl_label, score_col],
-                    gamma_df.loc[rho_df['label'] == self.ctrl_label, score_col],
-                    alpha=0.1, s=dot_size, c='gray', label=self.ctrl_label,
-                    **args)
-        
-        # Set x-axis and y-axis labels
-        ax.set_xlabel(xlabel)
-        ax.set_ylabel(ylabel)
-
-        # Set x-axis limits
-        ax.set_xlim(xlims)
-        ax.set_ylim(ylims)
-
-        # Add legend
-        ax.legend()
-        
-    def label_as_black(self, ax, df_in, label, 
-                       x_col='score', y_col='-log10(pvalue)',
-                       size=2, size_txt="auto",
-                       t_x=.5, t_y=-0.1,
-                       **args):
-        self._label_by_color(ax, df_in, label, 
-                             x_col=x_col, y_col=y_col,
-                             size=size, size_txt=size_txt,
-                             edgecolors='black', facecolors='black',
-                             textcolor='black',
-                             t_x=t_x, t_y=t_y,
-                             **args)
-    
-    def label_sensitivity_hit(self, ax, df_in, label, 
-                              x_col='score', y_col='-log10(pvalue)',
-                              size=2, size_txt="auto",
-                              t_x=.5, t_y=-0.1,
-                              **args):
-        self._label_by_color(ax, df_in, label, 
-                             x_col=x_col, y_col=y_col,
-                             size=size, size_txt=size_txt,
-                             edgecolors='black', facecolors='#3182bd',
-                             textcolor='black',
-                             t_x=t_x, t_y=t_y,
-                             **args)
-    
-    def label_resistance_hit(self, ax, df_in, label, 
-                             x_col='score', y_col='-log10(pvalue)',
-                             size=2, size_txt="auto",
-                             t_x=.5, t_y=-0.1,
-                             **args):
-        self._label_by_color(ax, df_in, label, 
-                             x_col=x_col, y_col=y_col,
-                             size=size, size_txt=size_txt,
-                             edgecolors='black', facecolors='#de2d26',
-                             textcolor='black',
-                             t_x=t_x, t_y=t_y,
-                             **args)
diff --git a/screenpro/visualize/rank.py b/screenpro/visualize/rank.py
deleted file mode 100644
index b3707c7..0000000
--- a/screenpro/visualize/rank.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import pandas as pd
-import matplotlib.pyplot as plt
-from .utils import yellow_blue
-
-
-def rankPlot(df, rank_col, color_col=None, name_col='target', highlight_values_dict=None, xlabel='Rank', ylabel='Values', title='Rank Plot', ax=None, dot_size=1.5, highlight_size_factor=100, **args):
-    """
-    Plot the ranks against their values with specified color.
-
-    Args:
-        df (DataFrame): The input DataFrame.
-        rank_col (str): The column name containing the values to be ranked.
-        color_col (str): The column name containing the values to be used for color coding. Default is None.
-        name_col (str, optional): The column name containing the names of the values. Default is 'target'.
-        highlight_values_dict (dict, optional): A dictionary specifying the values to be highlighted. 
-            The keys are the highlight colors and the values are dictionaries with 'genes' and 'text' keys. 
-            'genes' is a list of values to be highlighted and 'text' is a boolean indicating whether to display 
-            the names of the highlighted values. Default is None.
-        xlabel (str, optional): The label for the x-axis. Default is 'Rank'.
-        ylabel (str, optional): The label for the y-axis. Default is 'Values'.
-        title (str, optional): The title of the plot. Default is 'Rank Plot'.
-        ax (matplotlib.axes.Axes, optional): The axis object to plot on. If not provided, a new axis will be created.
-        dot_size (float, optional): The size of the dots in the scatter plot. Default is 1.5.
-        highlight_size_factor (int, optional): The size factor for the highlighted dots. Default is 100.
-        **args: Additional keyword arguments to be passed to the scatter plot.
-
-    Returns:
-        matplotlib.axes.Axes: The axis object containing the plot.
-    """
-    # Create a new DataFrame with the values and their corresponding ranks
-    rank_df = df.copy()
-    rank_df['Rank'] = rank_df[rank_col].rank()
-    rank_df.sort_values('Rank', inplace=True)
-
-    # Use a color that is suitable for publications
-    if color_col is None:
-        color_col = 'darkgray'
-
-    # If no axis is provided, create one
-    if ax is None:
-        _, ax = plt.subplots()
-
-    # Plot the ranks against their values with specified color
-    rank_df.plot.scatter(
-        'Rank', rank_col, marker='o',
-        colormap=yellow_blue,
-        s=dot_size,
-        c=color_col, ax=ax,
-        colorbar=False,
-        **args
-    )
-
-    if highlight_values_dict is not None:
-        for highlight_color, highlight_values in highlight_values_dict.items():
-            highlight_ranks = rank_df[rank_df[name_col].isin(highlight_values['genes'])]
-            ax.plot(highlight_ranks['Rank'], highlight_ranks[rank_col], 'o', color=highlight_color, markersize=dot_size * highlight_size_factor)
-    
-            if highlight_values['text'] is not False:
-                for i, row in highlight_ranks.iterrows():
-                    ax.text(row['Rank'] + .01, row[rank_col] + .001, row[name_col], fontsize=8, color=highlight_color, ha='right')
-
-    # Add labels and title
-    ax.set_xlabel(xlabel)
-    ax.set_ylabel(ylabel)
-    ax.set_title(title)
-
-    # Customize the grid lines for a clean look
-    ax.grid(False)
-
-    return ax

From 7f41f2ead1d55c1a60a4e7b1311be97cbc867a98 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Tue, 16 Jul 2024 19:39:41 -0700
Subject: [PATCH 02/34] reorganize plotting features

---
 screenpro/plotting/__init__.py                    | 4 ++--
 screenpro/plotting/{volcano.py => pheno_plots.py} | 7 ++++---
 screenpro/plotting/utils.py                       | 2 --
 3 files changed, 6 insertions(+), 7 deletions(-)
 rename screenpro/plotting/{volcano.py => pheno_plots.py} (96%)

diff --git a/screenpro/plotting/__init__.py b/screenpro/plotting/__init__.py
index ccb5576..d3dcd1e 100644
--- a/screenpro/plotting/__init__.py
+++ b/screenpro/plotting/__init__.py
@@ -5,5 +5,5 @@
 
 import numpy as np
 import scanpy as sc
-from .qc_plots import plotReplicateScatter
-from .rank import rankPlot
+from .qc_plots import *
+from .pheno_plots import *
diff --git a/screenpro/plotting/volcano.py b/screenpro/plotting/pheno_plots.py
similarity index 96%
rename from screenpro/plotting/volcano.py
rename to screenpro/plotting/pheno_plots.py
index 3aeba09..33b191c 100644
--- a/screenpro/plotting/volcano.py
+++ b/screenpro/plotting/pheno_plots.py
@@ -1,7 +1,7 @@
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
-from .utils import yellow_blue, up_hit_color, down_hit_color
+from .utils import yellow_blue
 
 
 class ScatterPlot:
@@ -30,12 +30,12 @@ def __init__(
         
         ax.scatter( df.loc[df['label'] == up_hit, score_col], 
                     df.loc[df['label'] == up_hit, f'-log10({pvalue_col})'],
-                    alpha=0.9, s=dot_size, c=up_hit_color, label=up_hit,
+                    alpha=0.9, s=dot_size, c='#fcae91', label=up_hit,
                     **args)
         
         ax.scatter( df.loc[df['label'] == down_hit, score_col], 
                     df.loc[df['label'] == down_hit, f'-log10({pvalue_col})'],
-                    alpha=0.9, s=dot_size, c=down_hit_color, label=down_hit,
+                    alpha=0.9, s=dot_size, c='#bdd7e7', label=down_hit,
                     **args)
         
         ax.scatter( df.loc[df['label'] == ctrl_label, score_col],
@@ -147,3 +147,4 @@ def __init__(
                          dot_size=dot_size,
                          xlims=xlims, ylims=ylims,
                          **args)
+
diff --git a/screenpro/plotting/utils.py b/screenpro/plotting/utils.py
index 34fa620..316f460 100644
--- a/screenpro/plotting/utils.py
+++ b/screenpro/plotting/utils.py
@@ -20,8 +20,6 @@
     'YlBu', [(0, '#0000ff'), (.49, '#000000'), (.51, '#000000'), (1, '#ffff00')])
 yellow_blue.set_bad('#999999', 1)
 
-up_hit_color = '#fcae91'
-down_hit_color = '#bdd7e7'
 
 # plt.rcParams['font.sans-serif'] = [
 #     'Helvetica', 'Arial', 'Verdana', 'Bitstream Vera Sans'

From c2447bf0737f6fb2c7805d4e649ef2242ce739ed Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Tue, 16 Jul 2024 19:46:24 -0700
Subject: [PATCH 03/34] reorganize plotting features

---
 screenpro/plotting/_rank.py                | 70 ++++++++++++++++++++++
 screenpro/plotting/{utils.py => _utils.py} |  0
 2 files changed, 70 insertions(+)
 create mode 100644 screenpro/plotting/_rank.py
 rename screenpro/plotting/{utils.py => _utils.py} (100%)

diff --git a/screenpro/plotting/_rank.py b/screenpro/plotting/_rank.py
new file mode 100644
index 0000000..7eb04b9
--- /dev/null
+++ b/screenpro/plotting/_rank.py
@@ -0,0 +1,70 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+from ._utils import yellow_blue
+
+
+def rank_plot(df, rank_col, color_col=None, name_col='target', highlight_values_dict=None, xlabel='Rank', ylabel='Values', title='Rank Plot', ax=None, dot_size=1.5, highlight_size_factor=100, **args):
+    """
+    Plot the ranks against their values with specified color.
+
+    Args:
+        df (DataFrame): The input DataFrame.
+        rank_col (str): The column name containing the values to be ranked.
+        color_col (str): The column name containing the values to be used for color coding. Default is None.
+        name_col (str, optional): The column name containing the names of the values. Default is 'target'.
+        highlight_values_dict (dict, optional): A dictionary specifying the values to be highlighted. 
+            The keys are the highlight colors and the values are dictionaries with 'genes' and 'text' keys. 
+            'genes' is a list of values to be highlighted and 'text' is a boolean indicating whether to display 
+            the names of the highlighted values. Default is None.
+        xlabel (str, optional): The label for the x-axis. Default is 'Rank'.
+        ylabel (str, optional): The label for the y-axis. Default is 'Values'.
+        title (str, optional): The title of the plot. Default is 'Rank Plot'.
+        ax (matplotlib.axes.Axes, optional): The axis object to plot on. If not provided, a new axis will be created.
+        dot_size (float, optional): The size of the dots in the scatter plot. Default is 1.5.
+        highlight_size_factor (int, optional): The size factor for the highlighted dots. Default is 100.
+        **args: Additional keyword arguments to be passed to the scatter plot.
+
+    Returns:
+        matplotlib.axes.Axes: The axis object containing the plot.
+    """
+    # Create a new DataFrame with the values and their corresponding ranks
+    rank_df = df.copy()
+    rank_df['Rank'] = rank_df[rank_col].rank()
+    rank_df.sort_values('Rank', inplace=True)
+
+    # Use a color that is suitable for publications
+    if color_col is None:
+        color_col = 'darkgray'
+
+    # If no axis is provided, create one
+    if ax is None:
+        _, ax = plt.subplots()
+
+    # Plot the ranks against their values with specified color
+    rank_df.plot.scatter(
+        'Rank', rank_col, marker='o',
+        colormap=yellow_blue,
+        s=dot_size,
+        c=color_col, ax=ax,
+        colorbar=False,
+        **args
+    )
+
+    if highlight_values_dict is not None:
+        for highlight_color, highlight_values in highlight_values_dict.items():
+            highlight_ranks = rank_df[rank_df[name_col].isin(highlight_values['genes'])]
+            ax.plot(highlight_ranks['Rank'], highlight_ranks[rank_col], 'o', color=highlight_color, markersize=dot_size * highlight_size_factor)
+    
+            if highlight_values['text'] is not False:
+                for i, row in highlight_ranks.iterrows():
+                    ax.text(row['Rank'] + .01, row[rank_col] + .001, row[name_col], fontsize=8, color=highlight_color, ha='right')
+
+    # Add labels and title
+    ax.set_xlabel(xlabel)
+    ax.set_ylabel(ylabel)
+    ax.set_title(title)
+
+    # Customize the grid lines for a clean look
+    ax.grid(False)
+
+    return ax
\ No newline at end of file
diff --git a/screenpro/plotting/utils.py b/screenpro/plotting/_utils.py
similarity index 100%
rename from screenpro/plotting/utils.py
rename to screenpro/plotting/_utils.py

From 5cb38ddec80fce5b90173a647ed8e3007ba07994 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Tue, 16 Jul 2024 19:54:39 -0700
Subject: [PATCH 04/34] reorganize plotting features

---
 screenpro/plotting/pheno_plots.py | 272 +++++++++++++++++++++++-------
 1 file changed, 213 insertions(+), 59 deletions(-)

diff --git a/screenpro/plotting/pheno_plots.py b/screenpro/plotting/pheno_plots.py
index 33b191c..4dcaff7 100644
--- a/screenpro/plotting/pheno_plots.py
+++ b/screenpro/plotting/pheno_plots.py
@@ -1,19 +1,97 @@
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
-from .utils import yellow_blue
+from ._utils import yellow_blue
+from ._rank import rank_plot
 
 
-class ScatterPlot:
-    def __init__(
-            self, df, up_hit, down_hit, ax,
-            score_col, pvalue_col, xlabel, ylabel,
-            ctrl_label='negative_control',
+## Phenotype plotting functions
+class DrugScreenPlotter:
+    def __init__(self, screen, treated, untreated, t0='T0', threshold=3, ctrl_label='negative_control',run_name='auto'):
+        self.screen = screen
+        self.threshold = threshold
+        self.ctrl_label = ctrl_label
+        self.run_name = run_name
+        self.gamma_score_name = f'gamma:{untreated}_vs_{t0}'
+        self.rho_score_name = f'rho:{treated}_vs_{untreated}'
+        self.tau_score_name = f'tau:{treated}_vs_{t0}'
+
+    def _label_by_color(self, ax, df_in, label, 
+                        x_col, y_col,
+                        size=2, size_txt="auto",
+                        edgecolors='black', facecolors='black',
+                        textcolor='black',
+                        t_x=.5, t_y=-0.1, **args):
+        
+        df = df_in.copy()
+        target_data = df[df['target'] == label]
+
+        # Scatter plot for labeled data
+        ax.scatter(
+            target_data[x_col], target_data[y_col],
+            s=size, linewidth=0.5, 
+            edgecolors=edgecolors, 
+            facecolors=facecolors, label='target',
+            **args
+        )
+
+        if size_txt == None:
+            pass
+        elif size_txt == 'auto':
+            size_txt = size * 2
+        else:
+            # Annotate the points
+            for i, _ in enumerate(target_data['target']):
+                txt = target_data['target'].iloc[i]
+                ax.annotate(txt, (target_data[x_col].iloc[i] + t_x, target_data[y_col].iloc[i] + t_y),
+                            color=textcolor, size=size_txt)
+
+    def _prep_data(self, score_col='score', pvalue_col='pvalue'):
+
+        gamma = self.screen.getPhenotypeScores(
+            run_name=self.run_name,
+            score_name=self.gamma_score_name,
+            threshold=self.threshold,
+            ctrl_label=self.ctrl_label,
+            score_col=score_col,
+            pvalue_col=pvalue_col
+        )
+
+        gamma[f'-log10({pvalue_col})'] = np.log10(gamma[pvalue_col]) * -1
+
+        tau = self.screen.getPhenotypeScores(
+            run_name=self.run_name,
+            score_name=self.tau_score_name,
+            threshold=self.threshold,
+            ctrl_label=self.ctrl_label,
+            score_col=score_col,
+            pvalue_col=pvalue_col
+        )
+        tau[f'-log10({pvalue_col})'] = np.log10(tau[pvalue_col]) * -1
+
+        rho = self.screen.getPhenotypeScores(
+            run_name=self.run_name,
+            score_name=self.rho_score_name,
+            threshold=self.threshold,
+            ctrl_label=self.ctrl_label,
+            score_col=score_col,
+            pvalue_col=pvalue_col
+        )
+        rho[f'-log10({pvalue_col})'] = np.log10(rho[pvalue_col]) * -1
+
+        return gamma, tau, rho
+    
+    def _volcano(
+            self, ax, df, up_hit, down_hit,
+            score_col='score', pvalue_col='pvalue',
+            xlabel='phenotype score',
+            ylabel='-log10(pvalue)',
             dot_size=1,
             xlims='auto',
             ylims='auto',
             **args
             ):
+        
         if f'-log10({pvalue_col})' not in df.columns:
             df[f'-log10({pvalue_col})'] = np.log10(df[pvalue_col]) * -1
 
@@ -38,9 +116,9 @@ def __init__(
                     alpha=0.9, s=dot_size, c='#bdd7e7', label=down_hit,
                     **args)
         
-        ax.scatter( df.loc[df['label'] == ctrl_label, score_col],
-                    df.loc[df['label'] == ctrl_label, f'-log10({pvalue_col})'],
-                    alpha=0.1, s=dot_size, c='gray', label=ctrl_label,
+        ax.scatter( df.loc[df['label'] == self.ctrl_label, score_col],
+                    df.loc[df['label'] == self.ctrl_label, f'-log10({pvalue_col})'],
+                    alpha=0.1, s=dot_size, c='gray', label=self.ctrl_label,
                     **args)
 
         # Set x-axis and y-axis labels
@@ -56,38 +134,136 @@ def __init__(
         # Add legend
         ax.legend()
 
-        return ax
-
-    def _label_by_color(self, ax, df_in, label, 
-                        x_col, y_col,
-                        size=2, size_txt="auto",
-                        edgecolors='black', facecolors='black',
-                        textcolor='black',
-                        t_x=.5, t_y=-0.1, **args):
+    def drawVolcanoRho(
+            self, ax,
+            rho_df=None,
+            dot_size=1,
+            score_col='score', 
+            pvalue_col='pvalue',
+            xlabel='auto',
+            ylabel='-log10(pvalue)',
+            xlims='auto',
+            ylims='auto',
+            **args
+            ):
+        if rho_df is None:
+            _, _, rho_df = self._prep_data()
+        if xlabel == 'auto':
+            xlabel = self.rho_score_name.replace(':', ': ').replace('_', ' ')
         
-        df = df_in.copy()
-        target_data = df[df['target'] == label]
+        self._volcano(ax, rho_df, 
+                      up_hit='resistance_hit', down_hit='sensitivity_hit',
+                      score_col=score_col, pvalue_col=pvalue_col,
+                      xlabel=xlabel, ylabel=ylabel,
+                      dot_size=dot_size, xlims=xlims, ylims=ylims,
+                      **args)
+    
+    def drawVolcanoGamma(
+            self, ax,
+            gamma_df=None,
+            dot_size=1,
+            score_col='score', 
+            pvalue_col='pvalue',
+            xlabel='auto',
+            ylabel='-log10(pvalue)',
+            xlims='auto',
+            ylims='auto',
+            **args
+            ):
+        if gamma_df is None:
+            gamma_df, _, _ = self._prep_data()
+        if xlabel == 'auto':
+            xlabel = self.gamma_score_name.replace(':', ': ').replace('_', ' ')
+        
+        self._volcano(ax, gamma_df, 
+                      up_hit='up_hit', down_hit='essential_hit',
+                      score_col=score_col, pvalue_col=pvalue_col,
+                      xlabel=xlabel, ylabel=ylabel,
+                      dot_size=dot_size, xlims=xlims, ylims=ylims,
+                      **args)
+        
+    def drawVolcanoTau(
+            self, ax,
+            tau_df=None,
+            dot_size=1,
+            score_col='score', 
+            pvalue_col='pvalue',
+            xlabel='auto',
+            ylabel='-log10(pvalue)',
+            xlims='auto',
+            ylims='auto',
+            **args
+            ):
+        if tau_df is None:
+            _, tau_df, _, = self._prep_data()
+        if xlabel == 'auto':
+            xlabel = self.tau_score_name.replace(':', ': ').replace('_', ' ')
+        
+        self._volcano(ax, tau_df, 
+                      up_hit='up_hit', down_hit='down_hit',
+                      score_col=score_col, pvalue_col=pvalue_col,
+                      xlabel=xlabel, ylabel=ylabel,
+                      dot_size=dot_size, xlims=xlims, ylims=ylims,
+                      **args)
 
-        # Scatter plot for labeled data
-        ax.scatter(
-            target_data[x_col], target_data[y_col],
-            s=size, linewidth=0.5, 
-            edgecolors=edgecolors, 
-            facecolors=facecolors, label='target',
+    def drawRhoGammaScatter(
+            self, ax,
+            rho_df=None, gamma_df=None,
+            dot_size=1,
+            score_col='score',
+            xlabel='auto',
+            ylabel='auto',
+            xlims='auto',
+            ylims='auto',
             **args
-        )
+            ):
+        #TODO: fix by making a single dataframe with both rho and gamma scores
+        if rho_df is None:
+            _, _, rho_df = self._prep_data()
+        if gamma_df is None:
+            gamma_df, _, _ = self._prep_data()
 
-        if size_txt == None:
-            pass
-        elif size_txt == 'auto':
-            size_txt = size * 2
-        else:
-            # Annotate the points
-            for i, _ in enumerate(target_data['target']):
-                txt = target_data['target'].iloc[i]
-                ax.annotate(txt, (target_data[x_col].iloc[i] + t_x, target_data[y_col].iloc[i] + t_y),
-                            color=textcolor, size=size_txt)
+        if xlabel == 'auto':
+            xlabel = self.rho_score_name.replace(':', ': ').replace('_', ' ')
+        if ylabel == 'auto':
+            ylabel = self.gamma_score_name.replace(':', ': ').replace('_', ' ')
+        
+        # color by rho score labels
+        up_hit = 'resistance_hit'
+        down_hit = 'sensitivity_hit'
+
+        # Scatter plot for each category
+        ax.scatter( rho_df.loc[rho_df['label'] == 'target_non_hit', score_col],
+                    gamma_df.loc[rho_df['label'] == 'target_non_hit', score_col],
+                    alpha=0.1, s=dot_size, c='black', label='target_non_hit',
+                    **args)
+        
+        ax.scatter( rho_df.loc[rho_df['label'] == up_hit, score_col],
+                    gamma_df.loc[rho_df['label'] == up_hit, score_col],
+                    alpha=0.9, s=dot_size, c='#fcae91', label=up_hit,
+                    **args)
+        
+        ax.scatter( rho_df.loc[rho_df['label'] == down_hit, score_col],
+                    gamma_df.loc[rho_df['label'] == down_hit, score_col],
+                    alpha=0.9, s=dot_size, c='#bdd7e7', label=down_hit,
+                    **args)
+        
+        ax.scatter( rho_df.loc[rho_df['label'] == self.ctrl_label, score_col],
+                    gamma_df.loc[rho_df['label'] == self.ctrl_label, score_col],
+                    alpha=0.1, s=dot_size, c='gray', label=self.ctrl_label,
+                    **args)
+        
+        # Set x-axis and y-axis labels
+        ax.set_xlabel(xlabel)
+        ax.set_ylabel(ylabel)
 
+        # Set x-axis limits
+        ax.set_xlim(xlims)
+        ax.set_ylim(ylims)
+
+        # Add legend
+        ax.legend()
+        
     def label_as_black(self, ax, df_in, label, 
                        x_col='score', y_col='-log10(pvalue)',
                        size=2, size_txt="auto",
@@ -125,26 +301,4 @@ def label_resistance_hit(self, ax, df_in, label,
                              edgecolors='black', facecolors='#de2d26',
                              textcolor='black',
                              t_x=t_x, t_y=t_y,
-                             **args)
-
-
-class VolcanoPlot(ScatterPlot):
-    def __init__(
-            self, df, up_hit, down_hit, ax,
-            score_col='score', pvalue_col='pvalue',
-            xlabel='phenotype score',
-            ylabel='-log10(pvalue)',
-            ctrl_label='negative_control',
-            dot_size=1,
-            xlims='auto',
-            ylims='auto',
-            **args
-            ):
-        super().__init__(df, up_hit, down_hit, ax,
-                         score_col=score_col, pvalue_col=pvalue_col,
-                         xlabel=xlabel, ylabel=ylabel,
-                         ctrl_label=ctrl_label,
-                         dot_size=dot_size,
-                         xlims=xlims, ylims=ylims,
-                         **args)
-
+                             **args)
\ No newline at end of file

From aa354ebf67c83ee9c3580dca858a32ab71ee0b83 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Tue, 16 Jul 2024 21:37:24 -0700
Subject: [PATCH 05/34] reorganize plotting features

---
 screenpro/plotting/pheno_plots.py | 218 ++++++++++++++++--------------
 screenpro/plotting/qc_plots.py    |   7 +-
 2 files changed, 119 insertions(+), 106 deletions(-)

diff --git a/screenpro/plotting/pheno_plots.py b/screenpro/plotting/pheno_plots.py
index 4dcaff7..585bad6 100644
--- a/screenpro/plotting/pheno_plots.py
+++ b/screenpro/plotting/pheno_plots.py
@@ -5,90 +5,15 @@
 from ._rank import rank_plot
 
 
-## Phenotype plotting functions
-class DrugScreenPlotter:
-    def __init__(self, screen, treated, untreated, t0='T0', threshold=3, ctrl_label='negative_control',run_name='auto'):
-        self.screen = screen
-        self.threshold = threshold
-        self.ctrl_label = ctrl_label
-        self.run_name = run_name
-        self.gamma_score_name = f'gamma:{untreated}_vs_{t0}'
-        self.rho_score_name = f'rho:{treated}_vs_{untreated}'
-        self.tau_score_name = f'tau:{treated}_vs_{t0}'
-
-    def _label_by_color(self, ax, df_in, label, 
-                        x_col, y_col,
-                        size=2, size_txt="auto",
-                        edgecolors='black', facecolors='black',
-                        textcolor='black',
-                        t_x=.5, t_y=-0.1, **args):
-        
-        df = df_in.copy()
-        target_data = df[df['target'] == label]
-
-        # Scatter plot for labeled data
-        ax.scatter(
-            target_data[x_col], target_data[y_col],
-            s=size, linewidth=0.5, 
-            edgecolors=edgecolors, 
-            facecolors=facecolors, label='target',
-            **args
-        )
-
-        if size_txt == None:
-            pass
-        elif size_txt == 'auto':
-            size_txt = size * 2
-        else:
-            # Annotate the points
-            for i, _ in enumerate(target_data['target']):
-                txt = target_data['target'].iloc[i]
-                ax.annotate(txt, (target_data[x_col].iloc[i] + t_x, target_data[y_col].iloc[i] + t_y),
-                            color=textcolor, size=size_txt)
-
-    def _prep_data(self, score_col='score', pvalue_col='pvalue'):
-
-        gamma = self.screen.getPhenotypeScores(
-            run_name=self.run_name,
-            score_name=self.gamma_score_name,
-            threshold=self.threshold,
-            ctrl_label=self.ctrl_label,
-            score_col=score_col,
-            pvalue_col=pvalue_col
-        )
-
-        gamma[f'-log10({pvalue_col})'] = np.log10(gamma[pvalue_col]) * -1
-
-        tau = self.screen.getPhenotypeScores(
-            run_name=self.run_name,
-            score_name=self.tau_score_name,
-            threshold=self.threshold,
-            ctrl_label=self.ctrl_label,
-            score_col=score_col,
-            pvalue_col=pvalue_col
-        )
-        tau[f'-log10({pvalue_col})'] = np.log10(tau[pvalue_col]) * -1
-
-        rho = self.screen.getPhenotypeScores(
-            run_name=self.run_name,
-            score_name=self.rho_score_name,
-            threshold=self.threshold,
-            ctrl_label=self.ctrl_label,
-            score_col=score_col,
-            pvalue_col=pvalue_col
-        )
-        rho[f'-log10({pvalue_col})'] = np.log10(rho[pvalue_col]) * -1
-
-        return gamma, tau, rho
-    
-    def _volcano(
-            self, ax, df, up_hit, down_hit,
+def volcano_plot(
+            ax, df, up_hit, down_hit,
             score_col='score', pvalue_col='pvalue',
             xlabel='phenotype score',
             ylabel='-log10(pvalue)',
             dot_size=1,
             xlims='auto',
             ylims='auto',
+            ctrl_label='negative_control',
             **args
             ):
         
@@ -116,9 +41,9 @@ def _volcano(
                     alpha=0.9, s=dot_size, c='#bdd7e7', label=down_hit,
                     **args)
         
-        ax.scatter( df.loc[df['label'] == self.ctrl_label, score_col],
-                    df.loc[df['label'] == self.ctrl_label, f'-log10({pvalue_col})'],
-                    alpha=0.1, s=dot_size, c='gray', label=self.ctrl_label,
+        ax.scatter( df.loc[df['label'] == ctrl_label, score_col],
+                    df.loc[df['label'] == ctrl_label, f'-log10({pvalue_col})'],
+                    alpha=0.1, s=dot_size, c='gray', label=ctrl_label,
                     **args)
 
         # Set x-axis and y-axis labels
@@ -134,6 +59,83 @@ def _volcano(
         # Add legend
         ax.legend()
 
+
+def label_by_color(ax, df_in, label,
+                   x_col, y_col,
+                   size=2, size_txt="auto",
+                   edgecolors='black', facecolors='black',
+                   textcolor='black',
+                   t_x=.5, t_y=-0.1, **args):
+
+    df = df_in.copy()
+    target_data = df[df['target'] == label]
+
+    # Scatter plot for labeled data
+    ax.scatter(
+        target_data[x_col], target_data[y_col],
+        s=size, linewidth=0.5, 
+        edgecolors=edgecolors, 
+        facecolors=facecolors, label='target',
+        **args
+    )
+
+    if size_txt == None:
+        pass
+    elif size_txt == 'auto':
+        size_txt = size * 2
+    else:
+        # Annotate the points
+        for i, _ in enumerate(target_data['target']):
+            txt = target_data['target'].iloc[i]
+            ax.annotate(txt, (target_data[x_col].iloc[i] + t_x, target_data[y_col].iloc[i] + t_y),
+                        color=textcolor, size=size_txt)
+
+
+class DrugScreenPlotter:
+    def __init__(self, screen, treated, untreated, t0='T0', threshold=3, ctrl_label='negative_control',run_name='auto'):
+        self.screen = screen
+        self.threshold = threshold
+        self.ctrl_label = ctrl_label
+        self.run_name = run_name
+        self.gamma_score_name = f'gamma:{untreated}_vs_{t0}'
+        self.rho_score_name = f'rho:{treated}_vs_{untreated}'
+        self.tau_score_name = f'tau:{treated}_vs_{t0}'
+
+    def _prep_data(self, score_col='score', pvalue_col='pvalue'):
+
+        gamma = self.screen.getPhenotypeScores(
+            run_name=self.run_name,
+            score_name=self.gamma_score_name,
+            threshold=self.threshold,
+            ctrl_label=self.ctrl_label,
+            score_col=score_col,
+            pvalue_col=pvalue_col
+        )
+
+        gamma[f'-log10({pvalue_col})'] = np.log10(gamma[pvalue_col]) * -1
+
+        tau = self.screen.getPhenotypeScores(
+            run_name=self.run_name,
+            score_name=self.tau_score_name,
+            threshold=self.threshold,
+            ctrl_label=self.ctrl_label,
+            score_col=score_col,
+            pvalue_col=pvalue_col
+        )
+        tau[f'-log10({pvalue_col})'] = np.log10(tau[pvalue_col]) * -1
+
+        rho = self.screen.getPhenotypeScores(
+            run_name=self.run_name,
+            score_name=self.rho_score_name,
+            threshold=self.threshold,
+            ctrl_label=self.ctrl_label,
+            score_col=score_col,
+            pvalue_col=pvalue_col
+        )
+        rho[f'-log10({pvalue_col})'] = np.log10(rho[pvalue_col]) * -1
+
+        return gamma, tau, rho
+    
     def drawVolcanoRho(
             self, ax,
             rho_df=None,
@@ -151,11 +153,12 @@ def drawVolcanoRho(
         if xlabel == 'auto':
             xlabel = self.rho_score_name.replace(':', ': ').replace('_', ' ')
         
-        self._volcano(ax, rho_df, 
+        volcano_plot(ax, rho_df, 
                       up_hit='resistance_hit', down_hit='sensitivity_hit',
                       score_col=score_col, pvalue_col=pvalue_col,
                       xlabel=xlabel, ylabel=ylabel,
                       dot_size=dot_size, xlims=xlims, ylims=ylims,
+                      ctrl_label=self.ctrl_label,
                       **args)
     
     def drawVolcanoGamma(
@@ -175,11 +178,12 @@ def drawVolcanoGamma(
         if xlabel == 'auto':
             xlabel = self.gamma_score_name.replace(':', ': ').replace('_', ' ')
         
-        self._volcano(ax, gamma_df, 
+        volcano_plot(ax, gamma_df, 
                       up_hit='up_hit', down_hit='essential_hit',
                       score_col=score_col, pvalue_col=pvalue_col,
                       xlabel=xlabel, ylabel=ylabel,
                       dot_size=dot_size, xlims=xlims, ylims=ylims,
+                      ctrl_label=self.ctrl_label,
                       **args)
         
     def drawVolcanoTau(
@@ -199,11 +203,12 @@ def drawVolcanoTau(
         if xlabel == 'auto':
             xlabel = self.tau_score_name.replace(':', ': ').replace('_', ' ')
         
-        self._volcano(ax, tau_df, 
+        volcano_plot(ax, tau_df, 
                       up_hit='up_hit', down_hit='down_hit',
                       score_col=score_col, pvalue_col=pvalue_col,
                       xlabel=xlabel, ylabel=ylabel,
                       dot_size=dot_size, xlims=xlims, ylims=ylims,
+                      ctrl_label=self.ctrl_label,
                       **args)
 
     def drawRhoGammaScatter(
@@ -269,36 +274,39 @@ def label_as_black(self, ax, df_in, label,
                        size=2, size_txt="auto",
                        t_x=.5, t_y=-0.1,
                        **args):
-        self._label_by_color(ax, df_in, label, 
-                             x_col=x_col, y_col=y_col,
-                             size=size, size_txt=size_txt,
-                             edgecolors='black', facecolors='black',
-                             textcolor='black',
-                             t_x=t_x, t_y=t_y,
-                             **args)
+        label_by_color(
+            ax, df_in, label, 
+            x_col=x_col, y_col=y_col,
+            size=size, size_txt=size_txt,
+            edgecolors='black', facecolors='black',
+            textcolor='black',
+            t_x=t_x, t_y=t_y,
+            **args)
     
     def label_sensitivity_hit(self, ax, df_in, label, 
                               x_col='score', y_col='-log10(pvalue)',
                               size=2, size_txt="auto",
                               t_x=.5, t_y=-0.1,
                               **args):
-        self._label_by_color(ax, df_in, label, 
-                             x_col=x_col, y_col=y_col,
-                             size=size, size_txt=size_txt,
-                             edgecolors='black', facecolors='#3182bd',
-                             textcolor='black',
-                             t_x=t_x, t_y=t_y,
-                             **args)
+        label_by_color(
+            ax, df_in, label, 
+            x_col=x_col, y_col=y_col,
+            size=size, size_txt=size_txt,
+            edgecolors='black', facecolors='#3182bd',
+            textcolor='black',
+            t_x=t_x, t_y=t_y,
+            **args)
     
     def label_resistance_hit(self, ax, df_in, label, 
                              x_col='score', y_col='-log10(pvalue)',
                              size=2, size_txt="auto",
                              t_x=.5, t_y=-0.1,
                              **args):
-        self._label_by_color(ax, df_in, label, 
-                             x_col=x_col, y_col=y_col,
-                             size=size, size_txt=size_txt,
-                             edgecolors='black', facecolors='#de2d26',
-                             textcolor='black',
-                             t_x=t_x, t_y=t_y,
-                             **args)
\ No newline at end of file
+        label_by_color(
+            ax, df_in, label, 
+            x_col=x_col, y_col=y_col,
+            size=size, size_txt=size_txt,
+            edgecolors='black', facecolors='#de2d26',
+            textcolor='black',
+            t_x=t_x, t_y=t_y,
+            **args)
diff --git a/screenpro/plotting/qc_plots.py b/screenpro/plotting/qc_plots.py
index dab57cc..594c964 100644
--- a/screenpro/plotting/qc_plots.py
+++ b/screenpro/plotting/qc_plots.py
@@ -1,6 +1,11 @@
 import numpy as np
 import scanpy as sc
-from .utils import almost_black
+from ._utils import almost_black
+
+
+## Histogram of guide counts distribution
+def plotCountDistribution(ax, adat, title, **args):
+    pass
 
 
 ## Scatter plot of replicates

From e22f3ab5455c98b934f6e06e847af5bb88d78a18 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Tue, 16 Jul 2024 21:42:23 -0700
Subject: [PATCH 06/34] fix imports

---
 screenpro/plotting/__init__.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/screenpro/plotting/__init__.py b/screenpro/plotting/__init__.py
index d3dcd1e..b8cb64c 100644
--- a/screenpro/plotting/__init__.py
+++ b/screenpro/plotting/__init__.py
@@ -5,5 +5,6 @@
 
 import numpy as np
 import scanpy as sc
-from .qc_plots import *
-from .pheno_plots import *
+from .qc_plots import plotReplicateScatter, plotCountDistribution
+from .pheno_plots import volcano_plot, label_by_color
+from .pheno_plots import DrugScreenPlotter
\ No newline at end of file

From 1c3869859a0838f49865e313e62c470ab957c454 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Tue, 16 Jul 2024 21:55:41 -0700
Subject: [PATCH 07/34] return rank_df

---
 screenpro/plotting/_rank.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/screenpro/plotting/_rank.py b/screenpro/plotting/_rank.py
index 7eb04b9..b836c4c 100644
--- a/screenpro/plotting/_rank.py
+++ b/screenpro/plotting/_rank.py
@@ -67,4 +67,4 @@ def rank_plot(df, rank_col, color_col=None, name_col='target', highlight_values_
     # Customize the grid lines for a clean look
     ax.grid(False)
 
-    return ax
\ No newline at end of file
+    return rank_df, ax
\ No newline at end of file

From 4b3e01cb6b6368b0714dbbe141881bce2b8ace60 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Tue, 16 Jul 2024 22:27:21 -0700
Subject: [PATCH 08/34] update docs

---
 docs/source/phenotype.md | 29 ++++++++++++++++++++++++++---
 1 file changed, 26 insertions(+), 3 deletions(-)

diff --git a/docs/source/phenotype.md b/docs/source/phenotype.md
index 29a3c96..9d54b76 100644
--- a/docs/source/phenotype.md
+++ b/docs/source/phenotype.md
@@ -1,22 +1,26 @@
 # Phenotype calculation modules
 
+## Phenotype score calculation
+
 Log ratio of {math}`y` vs {math}`x`:
 
 ```{math}
 \Delta =
 \log(\frac
-    {\begin{bmatrix}{N_{y}}\end{bmatrix}_{(a,b)} + 1}
-    {\begin{bmatrix}{N_{x}}\end{bmatrix}_{(a,b)} + 1}
+    {\begin{bmatrix}{N_{y}}\end{bmatrix}_{(a,b)}}
+    {\begin{bmatrix}{N_{x}}\end{bmatrix}_{(a,b)}}
 )
 ```
 
 -   {math}`y \rightarrow` condition {math}`x` (e.g. treated samples)
--   {math}`x \rightarrow` condition {math}`y` (e.g. {math}`t_{0}` samples)
+-   {math}`x \rightarrow` condition {math}`y` (e.g. {math}`t_{0}` samples, or untreated samples)
 -   {math}`a \rightarrow` number of library elements with sgRNAs targeting {math}`T`
 -   {math}`b \rightarrow` number of biological replicates, {math}`R` (e.g. 2 or 3)
 -   {math}`N_{x}` \| {math}`N_{y} \rightarrow` read counts normalized for sequencing
     depth in condition {math}`x` or {math}`y`
 
+___
+
 Here is a formula for V3 library with single library element per gene
 (i.e. dual sgRNAs in one construct targeting same gene).
 
@@ -40,6 +44,8 @@ Phenotype score for each {math}`T` comparing {math}`y` vs {math}`x`:
 -   {math}`d_{growth} \rightarrow` growth factor to normalize the phenotype
     score.
 
+## Phenotype statistics calculation
+
 Statistical test comparing {math}`y` vs {math}`x` per each target, {math}`T`:
 
 ```{math}
@@ -64,4 +70,21 @@ ___
 .. automodule:: screenpro.phenoscore
    :members:
    :show-inheritance:
+
+.. automodule:: screenpro.phenotype.phenostat
+   :members:
+   :show-inheritance:
+
+.. automodule:: screenpro.phenotype.delta
+    :members:
+    :show-inheritance:
+
+.. automodule:: screenpro.phenotype.deseq
+    :members:
+    :show-inheritance:
+
+.. automodule:: screenpro.phenotype.annotate
+    :members:
+    :show-inheritance:
+
 ```

From bd63f542f94732df3936ca9b1d79fc19ae69f276 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Wed, 17 Jul 2024 01:23:36 -0700
Subject: [PATCH 09/34] mend

---
 docs/source/conf.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index da6f885..1648f75 100755
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -36,15 +36,15 @@
     "seaborn",
     "scanpy",
     "anndata",
-    "screenpro",
+    # "screenpro",
     "pyarrow",
     "biobear",
     "click",
     "polars",
     "biobear",
     "numba",
-    "bokeh",
-    "pydeseq2",
+    # "bokeh",
+    # "pydeseq2",
     "watermark"
 ]
 

From b791b098f918211b851dff0fd126d92d86e83abf Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 01:22:16 -0700
Subject: [PATCH 10/34] optional argument allow skipping doubling rate
 `calculateDrugScreen`

---
 screenpro/assays.py | 22 ++++++++++++++++------
 1 file changed, 16 insertions(+), 6 deletions(-)

diff --git a/screenpro/assays.py b/screenpro/assays.py
index d05ea03..efe0b6b 100644
--- a/screenpro/assays.py
+++ b/screenpro/assays.py
@@ -16,6 +16,8 @@
 from .phenoscore import runPhenoScore, runPhenoScoreForReplicate
 from .preprocessing import addPseudoCount, findLowCounts, normalizeSeqDepth
 from .phenoscore.annotate import annotateScoreTable, hit_dict
+
+import warnings
 from copy import copy
 
 
@@ -158,12 +160,20 @@ def calculateDrugScreen(self, t0, untreated, treated, score_level, db_rate_col='
             run_name (str): name for the phenotype calculation run
             **kwargs: additional arguments to pass to runPhenoScore
         """
-        growth_factor_table = self._calculateGrowthFactor(
-            untreated = untreated, treated = treated, db_rate_col = db_rate_col
-        )
+        if 'pop_doublings' not in self.adata.obs.columns or db_rate_col == None:
+            warnings.warn('No doubling rate information provided.')
+            db_untreated = 1
+            db_treated = 1
+            db_treated_vs_untreated = 1
+        
+        else:
+            growth_factor_table = self._calculateGrowthFactor(
+                untreated = untreated, treated = treated, db_rate_col = db_rate_col
+            )
 
-        db_untreated=growth_factor_table.query(f'score=="gamma"')['growth_factor'].mean()
-        db_treated=growth_factor_table.query(f'score=="tau"')['growth_factor'].mean()
+            db_untreated=growth_factor_table.query(f'score=="gamma"')['growth_factor'].mean()
+            db_treated=growth_factor_table.query(f'score=="tau"')['growth_factor'].mean()
+            db_treated_vs_untreated = np.abs(db_untreated - db_treated)
 
         # calculate phenotype scores: gamma, tau, rho
         gamma_name, gamma = runPhenoScore(
@@ -180,7 +190,7 @@ def calculateDrugScreen(self, t0, untreated, treated, score_level, db_rate_col='
         )
         # TO-DO: warning / error if db_untreated and db_treated are too close, i.e. growth_rate ~= 0.
         rho_name, rho = runPhenoScore(
-            self.adata, cond1=untreated, cond2=treated, growth_rate=np.abs(db_untreated - db_treated),
+            self.adata, cond1=untreated, cond2=treated, growth_rate=db_treated_vs_untreated,
             n_reps=self.n_reps,
             transformation=self.fc_transformation, test=self.test, score_level=score_level,
             **kwargs

From 6481196ca2857a7d5fa94cc584bb37777b149d35 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 01:53:42 -0700
Subject: [PATCH 11/34] improve argument

---
 screenpro/assays.py              |  8 ++---
 screenpro/phenoscore/__init__.py | 55 ++++++++++++++++++--------------
 2 files changed, 35 insertions(+), 28 deletions(-)

diff --git a/screenpro/assays.py b/screenpro/assays.py
index efe0b6b..5e86a60 100644
--- a/screenpro/assays.py
+++ b/screenpro/assays.py
@@ -177,20 +177,20 @@ def calculateDrugScreen(self, t0, untreated, treated, score_level, db_rate_col='
 
         # calculate phenotype scores: gamma, tau, rho
         gamma_name, gamma = runPhenoScore(
-            self.adata, cond1=t0, cond2=untreated, growth_rate=db_untreated,
+            self.adata, cond_ref=t0, cond_test=untreated, growth_rate=db_untreated,
             n_reps=self.n_reps,
             transformation=self.fc_transformation, test=self.test, score_level=score_level,
             **kwargs
         )
         tau_name, tau = runPhenoScore(
-            self.adata, cond1=t0, cond2=treated, growth_rate=db_treated,
+            self.adata, cond_ref=t0, cond_test=treated, growth_rate=db_treated,
             n_reps=self.n_reps,
             transformation=self.fc_transformation, test=self.test, score_level=score_level,
             **kwargs
         )
         # TO-DO: warning / error if db_untreated and db_treated are too close, i.e. growth_rate ~= 0.
         rho_name, rho = runPhenoScore(
-            self.adata, cond1=untreated, cond2=treated, growth_rate=db_treated_vs_untreated,
+            self.adata, cond_ref=untreated, cond_test=treated, growth_rate=db_treated_vs_untreated,
             n_reps=self.n_reps,
             transformation=self.fc_transformation, test=self.test, score_level=score_level,
             **kwargs
@@ -242,7 +242,7 @@ def calculateFlowBasedScreen(self, low_bin, high_bin, score_level, run_name=None
         """
         # calculate phenotype scores
         delta_name, delta = runPhenoScore(
-            self.adata, cond1=low_bin, cond2=high_bin, n_reps=self.n_reps,
+            self.adata, cond_ref=low_bin, cond_test=high_bin, n_reps=self.n_reps,
             transformation=self.fc_transformation, test=self.test, score_level=score_level,
             **kwargs
         )
diff --git a/screenpro/phenoscore/__init__.py b/screenpro/phenoscore/__init__.py
index 581d3e8..780a899 100644
--- a/screenpro/phenoscore/__init__.py
+++ b/screenpro/phenoscore/__init__.py
@@ -19,7 +19,7 @@
 from .phenostat import multipleTestsCorrection
 
 
-def generatePseudoGeneAnnData(adata, num_pseudogenes='auto', pseudogene_size='auto', ctrl_label='negative_control'):
+def _generatePseudoGeneAnnData(adata, num_pseudogenes='auto', pseudogene_size='auto', ctrl_label='negative_control'):
     """Generate pseudogenes from negative control elements in the library.
 
     Args:
@@ -68,15 +68,15 @@ def generatePseudoGeneAnnData(adata, num_pseudogenes='auto', pseudogene_size='au
     return out
 
 
-def runPhenoScore(adata, cond1, cond2, transformation, score_level, test,
-                  growth_rate=1, n_reps=2, keep_top_n = None,num_pseudogenes='auto', pseudogene_size='auto',
+def runPhenoScore(adata, cond_ref, cond_test, transformation, score_level, test,
+                  growth_rate=1, n_reps='auto', keep_top_n = None,num_pseudogenes='auto', pseudogene_size='auto',
                   count_layer=None, ctrl_label='negative_control'):
-    """Calculate phenotype score and p-values when comparing `cond2` vs `cond1`.
+    """Calculate phenotype score and p-values when comparing `cond_test` vs `cond_ref`.
 
     Args:
         adata (AnnData): AnnData object
-        cond1 (str): condition 1
-        cond2 (str): condition 2
+        cond_ref (str): condition 1
+        cond_test (str): condition 2
         transformation (str): transformation to use for calculating score
         test (str): test to use for calculating p-value ('MW': Mann-Whitney U rank; 'ttest' : t-test)
         score_level (str): score level
@@ -93,8 +93,15 @@ def runPhenoScore(adata, cond1, cond2, transformation, score_level, test,
         pd.DataFrame: result dataframe
     """
     # format result name
-    result_name = f'{cond2}_vs_{cond1}'
-    print(f'\t{cond2} vs {cond1}')
+    result_name = f'{cond_test}_vs_{cond_ref}'
+    print(f'\t{cond_test} vs {cond_ref}')
+
+    # set n_reps if not provided
+    if n_reps == 'auto':
+        n_reps = min(
+            adata.obs.query(f'condition=="{cond_ref}"').shape[0], 
+            adata.obs.query(f'condition=="{cond_test}"').shape[0]
+        )
 
     # check if count_layer exists
     if count_layer is None:
@@ -113,16 +120,16 @@ def runPhenoScore(adata, cond1, cond2, transformation, score_level, test,
     # calc phenotype score and p-value
     if score_level in ['compare_reps']:
         # prep counts for phenoScore calculation
-        df_cond1 = adata[adata.obs.query(f'condition=="{cond1}"').index[:n_reps],].to_df(count_layer).T
-        df_cond2 = adata[adata.obs.query(f'condition=="{cond2}"').index[:n_reps],].to_df(count_layer).T
+        df_cond_ref = adata[adata.obs.query(f'condition=="{cond_ref}"').index[:n_reps],].to_df(count_layer).T
+        df_cond_test = adata[adata.obs.query(f'condition=="{cond_test}"').index[:n_reps],].to_df(count_layer).T
 
         # convert to numpy arrays
-        x = df_cond1.to_numpy()
-        y = df_cond2.to_numpy()
+        x = df_cond_ref.to_numpy()
+        y = df_cond_test.to_numpy()
 
         # get control values
-        x_ctrl = df_cond1[adata.var.targetType.eq(ctrl_label)].to_numpy()
-        y_ctrl = df_cond2[adata.var.targetType.eq(ctrl_label)].to_numpy()
+        x_ctrl = df_cond_ref[adata.var.targetType.eq(ctrl_label)].to_numpy()
+        y_ctrl = df_cond_test[adata.var.targetType.eq(ctrl_label)].to_numpy()
 
         # calculate growth score and p_value
         scores, p_values = matrixTest(
@@ -151,20 +158,20 @@ def runPhenoScore(adata, cond1, cond2, transformation, score_level, test,
         adata0 = adata.copy()
 
         # prep counts for phenoScore calculation
-        df_cond1 = adata0[adata0.obs.query(f'condition=="{cond1}"').index].to_df().T
-        df_cond2 = adata0[adata0.obs.query(f'condition=="{cond2}"').index].to_df().T
+        df_cond_ref = adata0[adata0.obs.query(f'condition=="{cond_ref}"').index].to_df().T
+        df_cond_test = adata0[adata0.obs.query(f'condition=="{cond_test}"').index].to_df().T
         # get control values
-        x_ctrl = df_cond1[adata0.var.targetType.eq(ctrl_label)].to_numpy()
-        y_ctrl = df_cond2[adata0.var.targetType.eq(ctrl_label)].to_numpy()
-        del df_cond1, df_cond2
+        x_ctrl = df_cond_ref[adata0.var.targetType.eq(ctrl_label)].to_numpy()
+        y_ctrl = df_cond_test[adata0.var.targetType.eq(ctrl_label)].to_numpy()
+        del df_cond_ref, df_cond_test
         
-        adata_pseudo = generatePseudoGeneAnnData(adata0, num_pseudogenes=num_pseudogenes, pseudogene_size=pseudogene_size, ctrl_label=ctrl_label)
+        adata_pseudo = _generatePseudoGeneAnnData(adata0, num_pseudogenes=num_pseudogenes, pseudogene_size=pseudogene_size, ctrl_label=ctrl_label)
         adata = ad.concat([adata0[:,~adata0.var.targetType.eq(ctrl_label)], adata_pseudo], axis=1)
         adata.obs = adata0.obs.copy()
 
         # prep counts for phenoScore calculation
-        df_cond1 = adata[adata.obs.query(f'condition=="{cond1}"').index].to_df().T
-        df_cond2 = adata[adata.obs.query(f'condition=="{cond2}"').index].to_df().T
+        df_cond_ref = adata[adata.obs.query(f'condition=="{cond_ref}"').index].to_df().T
+        df_cond_test = adata[adata.obs.query(f'condition=="{cond_test}"').index].to_df().T
         
         targets = []
         scores = []
@@ -173,8 +180,8 @@ def runPhenoScore(adata, cond1, cond2, transformation, score_level, test,
         # group by target genes or pseudogenes to aggregate counts for score calculation
         for target_name, target_group in adata.var.groupby('target'):
             # convert to numpy arrays
-            x = df_cond1.loc[target_group.index,:]
-            y = df_cond2.loc[target_group.index,:]
+            x = df_cond_ref.loc[target_group.index,:]
+            y = df_cond_test.loc[target_group.index,:]
             # Sort and find top n guide per target, see #18
             if keep_top_n:
                 x = x.sort_values(x.columns.to_list(), ascending=False)

From 5938c7e749ed7549b1082a0f743eb0873a80f215 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 02:06:10 -0700
Subject: [PATCH 12/34] mend

---
 screenpro/phenoscore/__init__.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/screenpro/phenoscore/__init__.py b/screenpro/phenoscore/__init__.py
index 780a899..74c0bb2 100644
--- a/screenpro/phenoscore/__init__.py
+++ b/screenpro/phenoscore/__init__.py
@@ -75,8 +75,8 @@ def runPhenoScore(adata, cond_ref, cond_test, transformation, score_level, test,
 
     Args:
         adata (AnnData): AnnData object
-        cond_ref (str): condition 1
-        cond_test (str): condition 2
+        cond_ref (str): condition reference
+        cond_test (str): condition test
         transformation (str): transformation to use for calculating score
         test (str): test to use for calculating p-value ('MW': Mann-Whitney U rank; 'ttest' : t-test)
         score_level (str): score level

From c901e987504f8dfb035c1a4d899a257cc1243fa8 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 02:14:09 -0700
Subject: [PATCH 13/34] fix a bug use `pd.Series()`

---
 screenpro/phenoscore/__init__.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/screenpro/phenoscore/__init__.py b/screenpro/phenoscore/__init__.py
index 74c0bb2..684b77d 100644
--- a/screenpro/phenoscore/__init__.py
+++ b/screenpro/phenoscore/__init__.py
@@ -206,8 +206,8 @@ def runPhenoScore(adata, cond_ref, cond_test, transformation, score_level, test,
             targets.append(target_name)
         
         # get mean scores and p-values across replicates
-        scores = [np.mean(s) for s in scores]
-        p_values = [np.mean(p) for p in p_values]
+        scores = pd.Series([np.mean(s) for s in scores])
+        p_values = pd.Series([np.mean(p) for p in p_values])
 
         # get adjusted p-values
         adj_p_values = multipleTestsCorrection(p_values)

From 129eb08c97d0b1ebffc4c5b274f41221d2139870 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 03:07:38 -0700
Subject: [PATCH 14/34] mend

---
 screenpro/phenoscore/__init__.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/screenpro/phenoscore/__init__.py b/screenpro/phenoscore/__init__.py
index 684b77d..3857f52 100644
--- a/screenpro/phenoscore/__init__.py
+++ b/screenpro/phenoscore/__init__.py
@@ -205,10 +205,6 @@ def runPhenoScore(adata, cond_ref, cond_test, transformation, score_level, test,
             p_values.append(target_p_values)
             targets.append(target_name)
         
-        # get mean scores and p-values across replicates
-        scores = pd.Series([np.mean(s) for s in scores])
-        p_values = pd.Series([np.mean(p) for p in p_values])
-
         # get adjusted p-values
         adj_p_values = multipleTestsCorrection(p_values)
         

From 993d375319f476e09754986af8fd35b089853918 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 03:10:20 -0700
Subject: [PATCH 15/34] temp commit

---
 screenpro/phenoscore/__init__.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/screenpro/phenoscore/__init__.py b/screenpro/phenoscore/__init__.py
index 3857f52..ccd1d4b 100644
--- a/screenpro/phenoscore/__init__.py
+++ b/screenpro/phenoscore/__init__.py
@@ -205,14 +205,14 @@ def runPhenoScore(adata, cond_ref, cond_test, transformation, score_level, test,
             p_values.append(target_p_values)
             targets.append(target_name)
         
-        # get adjusted p-values
-        adj_p_values = multipleTestsCorrection(p_values)
+        # # get adjusted p-values
+        # adj_p_values = multipleTestsCorrection(p_values)
         
         # combine results into a dataframe
         result = pd.concat([
             pd.Series(scores, index=targets, name='score'),
             pd.Series(p_values, index=targets, name=f'{test} pvalue'),
-            pd.Series(adj_p_values, index=targets, name='BH adj_pvalue'),
+            # pd.Series(adj_p_values, index=targets, name='BH adj_pvalue'),
         ], axis=1)
     
     else:

From ad9be71d635ce88c89bac63a03a96dcf8b08101b Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 03:16:33 -0700
Subject: [PATCH 16/34] fix a bug set axis as None for test level == 'all'
 https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_rel.html

---
 screenpro/phenoscore/__init__.py  | 6 +++---
 screenpro/phenoscore/phenostat.py | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/screenpro/phenoscore/__init__.py b/screenpro/phenoscore/__init__.py
index ccd1d4b..3857f52 100644
--- a/screenpro/phenoscore/__init__.py
+++ b/screenpro/phenoscore/__init__.py
@@ -205,14 +205,14 @@ def runPhenoScore(adata, cond_ref, cond_test, transformation, score_level, test,
             p_values.append(target_p_values)
             targets.append(target_name)
         
-        # # get adjusted p-values
-        # adj_p_values = multipleTestsCorrection(p_values)
+        # get adjusted p-values
+        adj_p_values = multipleTestsCorrection(p_values)
         
         # combine results into a dataframe
         result = pd.concat([
             pd.Series(scores, index=targets, name='score'),
             pd.Series(p_values, index=targets, name=f'{test} pvalue'),
-            # pd.Series(adj_p_values, index=targets, name='BH adj_pvalue'),
+            pd.Series(adj_p_values, index=targets, name='BH adj_pvalue'),
         ], axis=1)
     
     else:
diff --git a/screenpro/phenoscore/phenostat.py b/screenpro/phenoscore/phenostat.py
index ef270d7..58aec8e 100644
--- a/screenpro/phenoscore/phenostat.py
+++ b/screenpro/phenoscore/phenostat.py
@@ -37,7 +37,7 @@ def matrixStat(x, y, test, level):
             p_value = ttest_rel(y, x, axis=0)[1]
         elif level == 'all':
             # average across all values
-            p_value = ttest_rel(y, x)[1]
+            p_value = ttest_rel(y, x, axis=None)[1]
         else:
             raise ValueError(f'Level "{level}" not recognized')
         return p_value

From dad417b833024dffd48a1936276caf8288691c47 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 03:24:32 -0700
Subject: [PATCH 17/34] temp commit

---
 screenpro/phenoscore/__init__.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/screenpro/phenoscore/__init__.py b/screenpro/phenoscore/__init__.py
index 3857f52..0fbc2f1 100644
--- a/screenpro/phenoscore/__init__.py
+++ b/screenpro/phenoscore/__init__.py
@@ -205,8 +205,8 @@ def runPhenoScore(adata, cond_ref, cond_test, transformation, score_level, test,
             p_values.append(target_p_values)
             targets.append(target_name)
         
-        # get adjusted p-values
-        adj_p_values = multipleTestsCorrection(p_values)
+        # # get adjusted p-values
+        # adj_p_values = multipleTestsCorrection(p_values)
         
         # combine results into a dataframe
         result = pd.concat([

From b979d7de1583a67f53acefc751c6d72356ca9b40 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 03:25:54 -0700
Subject: [PATCH 18/34] mend

---
 screenpro/phenoscore/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/screenpro/phenoscore/__init__.py b/screenpro/phenoscore/__init__.py
index 0fbc2f1..ccd1d4b 100644
--- a/screenpro/phenoscore/__init__.py
+++ b/screenpro/phenoscore/__init__.py
@@ -212,7 +212,7 @@ def runPhenoScore(adata, cond_ref, cond_test, transformation, score_level, test,
         result = pd.concat([
             pd.Series(scores, index=targets, name='score'),
             pd.Series(p_values, index=targets, name=f'{test} pvalue'),
-            pd.Series(adj_p_values, index=targets, name='BH adj_pvalue'),
+            # pd.Series(adj_p_values, index=targets, name='BH adj_pvalue'),
         ], axis=1)
     
     else:

From cd3c3efd44a059d341bc894e3684173eb283d7e8 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 03:31:39 -0700
Subject: [PATCH 19/34] fix a bug

---
 screenpro/assays.py | 44 +++++++++++++++++++++++---------------------
 1 file changed, 23 insertions(+), 21 deletions(-)

diff --git a/screenpro/assays.py b/screenpro/assays.py
index 5e86a60..672f454 100644
--- a/screenpro/assays.py
+++ b/screenpro/assays.py
@@ -165,6 +165,7 @@ def calculateDrugScreen(self, t0, untreated, treated, score_level, db_rate_col='
             db_untreated = 1
             db_treated = 1
             db_treated_vs_untreated = 1
+            growth_factor_table = None
         
         else:
             growth_factor_table = self._calculateGrowthFactor(
@@ -207,27 +208,28 @@ def calculateDrugScreen(self, t0, untreated, treated, score_level, db_rate_col='
         self._add_phenotype_results(f'tau:{tau_name}')
         self._add_phenotype_results(f'rho:{rho_name}')
 
-        # get replicate level phenotype scores
-        pdata_df = pd.concat([
-            runPhenoScoreForReplicate(
-                self.adata, x_label = x_label, y_label = y_label, score = score_label,
-                transformation=self.fc_transformation, 
-                growth_factor_table=growth_factor_table,
-                **kwargs
-            ).add_prefix(f'{score_label}_')
-
-            for x_label, y_label, score_label in [
-                ('T0', untreated, 'gamma'),
-                ('T0', treated, 'tau'),
-                (untreated, treated, 'rho')
-            ]
-        ],axis=1).T
-        # add .pdata
-        self.pdata = ad.AnnData(
-            X = pdata_df,
-            obs = growth_factor_table.loc[pdata_df.index,:],
-            var=self.adata.var
-        )
+        if growth_factor_table:
+            # get replicate level phenotype scores
+            pdata_df = pd.concat([
+                runPhenoScoreForReplicate(
+                    self.adata, x_label = x_label, y_label = y_label, score = score_label,
+                    transformation=self.fc_transformation, 
+                    growth_factor_table=growth_factor_table,
+                    **kwargs
+                ).add_prefix(f'{score_label}_')
+
+                for x_label, y_label, score_label in [
+                    ('T0', untreated, 'gamma'),
+                    ('T0', treated, 'tau'),
+                    (untreated, treated, 'rho')
+                ]
+            ],axis=1).T
+            # add .pdata
+            self.pdata = ad.AnnData(
+                X = pdata_df,
+                obs = growth_factor_table.loc[pdata_df.index,:],
+                var=self.adata.var
+            )
         
     def calculateFlowBasedScreen(self, low_bin, high_bin, score_level, run_name=None, **kwargs):
         """

From cd9c9890abf690b2568430b63199a9ace06f312e Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 03:33:05 -0700
Subject: [PATCH 20/34] use `np.array`

---
 screenpro/phenoscore/__init__.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/screenpro/phenoscore/__init__.py b/screenpro/phenoscore/__init__.py
index ccd1d4b..7694695 100644
--- a/screenpro/phenoscore/__init__.py
+++ b/screenpro/phenoscore/__init__.py
@@ -205,14 +205,17 @@ def runPhenoScore(adata, cond_ref, cond_test, transformation, score_level, test,
             p_values.append(target_p_values)
             targets.append(target_name)
         
-        # # get adjusted p-values
-        # adj_p_values = multipleTestsCorrection(p_values)
+        scores = np.array(scores)
+        p_values = np.array(p_values)
+
+        # get adjusted p-values
+        adj_p_values = multipleTestsCorrection(p_values)
         
         # combine results into a dataframe
         result = pd.concat([
             pd.Series(scores, index=targets, name='score'),
             pd.Series(p_values, index=targets, name=f'{test} pvalue'),
-            # pd.Series(adj_p_values, index=targets, name='BH adj_pvalue'),
+            pd.Series(adj_p_values, index=targets, name='BH adj_pvalue'),
         ], axis=1)
     
     else:

From ef8660195b2e3f4a51db5617480946ad26555c18 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 03:38:11 -0700
Subject: [PATCH 21/34] mend

---
 screenpro/phenoscore/__init__.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/screenpro/phenoscore/__init__.py b/screenpro/phenoscore/__init__.py
index 7694695..1527408 100644
--- a/screenpro/phenoscore/__init__.py
+++ b/screenpro/phenoscore/__init__.py
@@ -204,12 +204,9 @@ def runPhenoScore(adata, cond_ref, cond_test, transformation, score_level, test,
             scores.append(target_scores)
             p_values.append(target_p_values)
             targets.append(target_name)
-        
-        scores = np.array(scores)
-        p_values = np.array(p_values)
 
         # get adjusted p-values
-        adj_p_values = multipleTestsCorrection(p_values)
+        adj_p_values = multipleTestsCorrection(np.array(p_values))
         
         # combine results into a dataframe
         result = pd.concat([

From 0de43adad017738781af8614964d359453f1a33b Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 03:49:29 -0700
Subject: [PATCH 22/34] fix a bug

---
 screenpro/phenoscore/delta.py | 12 +++---------
 1 file changed, 3 insertions(+), 9 deletions(-)

diff --git a/screenpro/phenoscore/delta.py b/screenpro/phenoscore/delta.py
index 75366df..dcaab97 100644
--- a/screenpro/phenoscore/delta.py
+++ b/screenpro/phenoscore/delta.py
@@ -20,15 +20,13 @@ def calculateDelta(x, y, transformation, level):
         np.array: array of log ratio values
     """
     # check if transformation is implemented
-    if transformation not in ['log2', 'log2(x+1)', 'log10', 'log1p']:
+    if transformation not in ['log2', 'log10', 'log1p']:
         raise ValueError(f'transformation "{transformation}" not recognized')
     
     if level == 'all':
         # average across all values
         if transformation == 'log2':
-            return np.log2(y) - np.log2(x)
-        elif transformation == 'log2(x+1)':
-            return np.mean(np.log2(y+1) - np.log2(x+1))
+            return np.mean(np.log2(y) - np.log2(x))
         elif transformation == 'log10':
             return np.mean(np.log10(y) - np.log10(x))
         elif transformation == 'log1p':
@@ -36,9 +34,7 @@ def calculateDelta(x, y, transformation, level):
     elif level == 'row':
         # average across rows
         if transformation == 'log2':
-            return np.log2(y) - np.log2(x)
-        elif transformation == 'log2(x+1)':
-            return np.mean(np.log2(y+1) - np.log2(x+1), axis=0)
+            return np.mean(np.log2(y) - np.log2(x), axis=0)
         elif transformation == 'log10':
             return np.mean(np.log10(y) - np.log10(x), axis=0)
         elif transformation == 'log1p':
@@ -47,8 +43,6 @@ def calculateDelta(x, y, transformation, level):
         # average across columns
         if transformation == 'log2':
             return np.mean(np.log2(y) - np.log2(x), axis=1)
-        elif transformation == 'log2(x+1)':
-            return np.mean(np.log2(y+1) - np.log2(x+1), axis=1)
         elif transformation == 'log10':
             return np.mean(np.log10(y) - np.log10(x), axis=1)
         elif transformation == 'log1p':

From 1483cf70f116293b8a597b9f4620be10518108c2 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 03:51:53 -0700
Subject: [PATCH 23/34] update docs file

---
 docs/source/phenotype.md | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/docs/source/phenotype.md b/docs/source/phenotype.md
index 9d54b76..64633cd 100644
--- a/docs/source/phenotype.md
+++ b/docs/source/phenotype.md
@@ -64,6 +64,12 @@ module](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_r
 > This is a test for the null hypothesis that two related or repeated
 > samples have identical average (expected) values).
 
+#
+
+```{math}
+\text{combined score} = \left( \dfrac{T_{\text{phenotype score}}}{\sigma{\text{(negative controls)}}} \right) \times -\log_{10}(\text{pvalue})$
+```
+
 ___
 
 ```{eval-rst}
@@ -71,19 +77,19 @@ ___
    :members:
    :show-inheritance:
 
-.. automodule:: screenpro.phenotype.phenostat
+.. automodule:: screenpro.phenoscore.phenostat
    :members:
    :show-inheritance:
 
-.. automodule:: screenpro.phenotype.delta
+.. automodule:: screenpro.phenoscore.delta
     :members:
     :show-inheritance:
 
-.. automodule:: screenpro.phenotype.deseq
+.. automodule:: screenpro.phenoscore.deseq
     :members:
     :show-inheritance:
 
-.. automodule:: screenpro.phenotype.annotate
+.. automodule:: screenpro.phenoscore.annotate
     :members:
     :show-inheritance:
 

From e9f03ab9d9698e39d80e93fd7cced8f3b14aa34a Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 03:55:31 -0700
Subject: [PATCH 24/34] mv tests folder

---
 {screenpro/tests => tests}/__init__.py         | 0
 {screenpro/tests => tests}/test_fastq2count.py | 0
 {screenpro/tests => tests}/test_phenoscore.py  | 0
 3 files changed, 0 insertions(+), 0 deletions(-)
 rename {screenpro/tests => tests}/__init__.py (100%)
 rename {screenpro/tests => tests}/test_fastq2count.py (100%)
 rename {screenpro/tests => tests}/test_phenoscore.py (100%)

diff --git a/screenpro/tests/__init__.py b/tests/__init__.py
similarity index 100%
rename from screenpro/tests/__init__.py
rename to tests/__init__.py
diff --git a/screenpro/tests/test_fastq2count.py b/tests/test_fastq2count.py
similarity index 100%
rename from screenpro/tests/test_fastq2count.py
rename to tests/test_fastq2count.py
diff --git a/screenpro/tests/test_phenoscore.py b/tests/test_phenoscore.py
similarity index 100%
rename from screenpro/tests/test_phenoscore.py
rename to tests/test_phenoscore.py

From 53194171d9375305847a71ff0adef3b9d13803a3 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 04:00:55 -0700
Subject: [PATCH 25/34] add `target` column

---
 screenpro/phenoscore/__init__.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/screenpro/phenoscore/__init__.py b/screenpro/phenoscore/__init__.py
index 1527408..e4ac013 100644
--- a/screenpro/phenoscore/__init__.py
+++ b/screenpro/phenoscore/__init__.py
@@ -112,7 +112,7 @@ def runPhenoScore(adata, cond_ref, cond_test, transformation, score_level, test,
         adata.X = adata.layers[count_layer].copy()
     
     # evaluate library table to get targets and riase error if not present
-    required_columns = ['target', 'sequence']
+    required_columns = ['target'] #, 'sequence']
     missing_columns = list(set(required_columns) - set(adata.var.columns))
     if len(missing_columns) > 0:
         raise ValueError(f"Missing required columns in library table: {missing_columns}")
@@ -210,10 +210,15 @@ def runPhenoScore(adata, cond_ref, cond_test, transformation, score_level, test,
         
         # combine results into a dataframe
         result = pd.concat([
+            pd.Series(targets, index=targets, name='target'),
             pd.Series(scores, index=targets, name='score'),
             pd.Series(p_values, index=targets, name=f'{test} pvalue'),
             pd.Series(adj_p_values, index=targets, name='BH adj_pvalue'),
         ], axis=1)
+
+        # rename pseudo genes in target column to `ctrl_label`
+        result['target'] = result['target'].apply(lambda x: ctrl_label if 'pseudo' in x else x)
+
     
     else:
         raise ValueError(f'score_level "{score_level}" not recognized. Currently, "compare_reps" and "compare_guides" are supported.')

From f5af200ce40cd1d54b930b5f779d02f9d6817603 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 04:04:53 -0700
Subject: [PATCH 26/34] bump version 0.4.6

---
 screenpro/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/screenpro/__init__.py b/screenpro/__init__.py
index f51c26b..6d155ab 100644
--- a/screenpro/__init__.py
+++ b/screenpro/__init__.py
@@ -31,6 +31,6 @@
 from .dashboard import DrugScreenDashboard
 
 
-__version__ = "0.4.5"
+__version__ = "0.4.6"
 __author__ = "Abe Arab"
 __email__ = 'abea@arcinstitute.org' # "abarbiology@gmail.com"

From d8d70cb60420543cd842754c136b7a920ec2a770 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 04:13:26 -0700
Subject: [PATCH 27/34] mend

---
 tests/test_phenoscore.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tests/test_phenoscore.py b/tests/test_phenoscore.py
index 8738d2c..297ab6d 100644
--- a/tests/test_phenoscore.py
+++ b/tests/test_phenoscore.py
@@ -45,9 +45,9 @@ def test_runPhenoScore():
     # run function
     result_name, result = runPhenoScore(
         adata=adat,
-        cond1='A',
-        cond2='B',
-        transformation='log2(x+1)',
+        cond_ref='A',
+        cond_test='B',
+        transformation='log2',
         test='ttest',
         score_level='compare_reps',
         growth_rate=1,

From 52753cef49c0f29e9df15d3219d867952c004424 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 04:49:11 -0700
Subject: [PATCH 28/34] change version ranges

---
 environment.yml | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/environment.yml b/environment.yml
index 5ac5774..e72d61b 100644
--- a/environment.yml
+++ b/environment.yml
@@ -4,7 +4,7 @@ channels:
   - conda-forge
   - bioconda
 dependencies:
-  - python=3.9
+  - python=>3.9,<3.12
   - scanpy
   - python-igraph
   - leidenalg
@@ -14,7 +14,6 @@ dependencies:
   - scipy
   - matplotlib<3.7
   - seaborn
-  - pyarrow
   - bokeh
   - ipykernel
   - mscorefonts

From f66471f0df7d9a82320a035eac702a1d719ae9ff Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 05:13:02 -0700
Subject: [PATCH 29/34] mend

---
 environment.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/environment.yml b/environment.yml
index e72d61b..70aa7d7 100644
--- a/environment.yml
+++ b/environment.yml
@@ -4,7 +4,7 @@ channels:
   - conda-forge
   - bioconda
 dependencies:
-  - python=>3.9,<3.12
+  - python>=3.9,<=3.12
   - scanpy
   - python-igraph
   - leidenalg

From 68cc8bada7688132c3c1b14bd6e011e021b67fc8 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 05:23:39 -0700
Subject: [PATCH 30/34] add more python versions

---
 .github/workflows/python-package.yml | 2 +-
 .github/workflows/python-publish.yml | 2 +-
 setup.py                             | 3 +++
 3 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml
index 169ecba..9ae180c 100644
--- a/.github/workflows/python-package.yml
+++ b/.github/workflows/python-package.yml
@@ -16,7 +16,7 @@ jobs:
       fail-fast: false
       matrix:
         os-version: ["ubuntu-latest"]
-        python-version: ["3.9"] # ["3.8", "3.9", "3.10"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
 
     steps:
     - uses: actions/checkout@v3
diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml
index 1c23099..c0ff647 100644
--- a/.github/workflows/python-publish.yml
+++ b/.github/workflows/python-publish.yml
@@ -15,7 +15,7 @@ jobs:
       fail-fast: false
       matrix:
         os-version: ["ubuntu-latest"]
-        python-version: ["3.9"] # ["3.8", "3.9", "3.10"]
+        python-version: ["3.11"] # ["3.8", "3.9", "3.10"]
 
     steps:
     - uses: actions/checkout@v3
diff --git a/setup.py b/setup.py
index 21d4cfb..973102f 100644
--- a/setup.py
+++ b/setup.py
@@ -29,5 +29,8 @@
         'License :: OSI Approved :: MIT License',
         'Programming Language :: Python :: 3',
         'Programming Language :: Python :: 3.9',
+        'Programming Language :: Python :: 3.10',
+        'Programming Language :: Python :: 3.11',
+        
     ]
 )

From 410112aadb68c831b15d5e4efe85c42894d5315b Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 05:25:41 -0700
Subject: [PATCH 31/34] set version range try to fix failed test similar to
 https://github.com/pola-rs/polars/issues/8546

---
 environment.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/environment.yml b/environment.yml
index 70aa7d7..6face60 100644
--- a/environment.yml
+++ b/environment.yml
@@ -21,6 +21,7 @@ dependencies:
   - pip
   - pip:
       - polars
+      - pyarrow<17.0
       - biobear
       - numba
       - pydeseq2

From ea30738ba8c3b15f4cf28eeb92e3f3767c15ad40 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 05:27:22 -0700
Subject: [PATCH 32/34] mend

---
 docs/source/phenotype.md | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/docs/source/phenotype.md b/docs/source/phenotype.md
index 64633cd..09e60f0 100644
--- a/docs/source/phenotype.md
+++ b/docs/source/phenotype.md
@@ -64,10 +64,10 @@ module](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_r
 > This is a test for the null hypothesis that two related or repeated
 > samples have identical average (expected) values).
 
-#
+## Combined score calculation
 
 ```{math}
-\text{combined score} = \left( \dfrac{T_{\text{phenotype score}}}{\sigma{\text{(negative controls)}}} \right) \times -\log_{10}(\text{pvalue})$
+\text{combined score} = \left( \dfrac{T_{\text{phenotype score}}}{\sigma{\text{(negative controls)}}} \right) \times -\log_{10}(\text{pvalue})
 ```
 
 ___
@@ -77,6 +77,12 @@ ___
    :members:
    :show-inheritance:
 
+```
+
+## Other related modules and functions
+
+```{eval-rst}
+
 .. automodule:: screenpro.phenoscore.phenostat
    :members:
    :show-inheritance:

From 6f18a01a98a50b2f95b246715f58709f9509b23a Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 15:09:05 -0700
Subject: [PATCH 33/34] mend

---
 .github/workflows/python-package.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml
index 9ae180c..565bafa 100644
--- a/.github/workflows/python-package.yml
+++ b/.github/workflows/python-package.yml
@@ -16,7 +16,7 @@ jobs:
       fail-fast: false
       matrix:
         os-version: ["ubuntu-latest"]
-        python-version: ["3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.9", "3.10", "3.11"] #, "3.12"]
 
     steps:
     - uses: actions/checkout@v3

From ba01f000489090bd85ae8c58bbc90ccf0d4fc231 Mon Sep 17 00:00:00 2001
From: abearab <abea@arcinstitute.org>
Date: Thu, 25 Jul 2024 15:11:12 -0700
Subject: [PATCH 34/34] set version ranges for `biobear`

---
 environment.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/environment.yml b/environment.yml
index 6face60..bd9689a 100644
--- a/environment.yml
+++ b/environment.yml
@@ -22,7 +22,7 @@ dependencies:
   - pip:
       - polars
       - pyarrow<17.0
-      - biobear
+      - biobear>=0.20.3,<0.21
       - numba
       - pydeseq2
       - simple_colors