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bump version from 0.3.3 -> 0.3.4
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update plotting code formats
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@abearab
abearab authored May 31, 2024
2 parents 96cc1cf + 8085b48 commit ea43cd0
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Showing 2 changed files with 250 additions and 112 deletions.
2 changes: 1 addition & 1 deletion screenpro/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,6 @@
from .ngs import Counter
from .assays import PooledScreens, GImaps

__version__ = "0.3.3"
__version__ = "0.3.4"
__author__ = "Abe Arab"
__email__ = '[email protected]' # "[email protected]"
360 changes: 249 additions & 111 deletions screenpro/plotting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -63,117 +63,7 @@ def draw_threshold(x, threshold, pseudo_sd):
return threshold * pseudo_sd * (1 if x > 0 else -1) / abs(x)


def prep_data(df_in, threshold, ctrl_label):
df = df_in.copy()

df = ann_score_df(df, threshold=threshold, ctrl_label = ctrl_label)

df['-log10(pvalue)'] = np.log10(df.pvalue) * -1

return df


def plot_volcano(ax, df_in, threshold, up_hit='resistance_hit', down_hit='sensitivity_hit',
ctrl_label = 'no-targeting',
dot_size=1,
xlim_l=-5, xlim_r=5,
ylim=6):
df = prep_data(df_in, threshold, ctrl_label)

# Scatter plot for each category
ax.scatter(df.loc[df['label'] == 'target_non_hit', 'score'],
df.loc[df['label'] == 'target_non_hit', '-log10(pvalue)'],
alpha=0.1, s=dot_size, c='black', label='target_non_hit')
ax.scatter(df.loc[df['label'] == up_hit, 'score'], df.loc[df['label'] == up_hit, '-log10(pvalue)'],
alpha=0.9, s=dot_size, c='#fcae91', label=up_hit)
ax.scatter(df.loc[df['label'] == down_hit, 'score'], df.loc[df['label'] == down_hit, '-log10(pvalue)'],
alpha=0.9, s=dot_size, c='#bdd7e7', label=down_hit)
ax.scatter(df.loc[df['label'] == ctrl_label, 'score'],
df.loc[df['label'] == ctrl_label, '-log10(pvalue)'],
alpha=0.1, s=dot_size, c='gray', label=ctrl_label)

# Set x-axis and y-axis labels
ax.set_xlabel('phenotype score')
ax.set_ylabel('-log10(p-value)')

# Set x-axis limits
ax.set_xlim(xlim_l, xlim_r)

# Set y-axis limits
ax.set_ylim(0.1, ylim)

# Add legend
ax.legend()


def label_as_black(ax, df_in, label, threshold, size=2, size_txt="auto",
ctrl_label = 'no-targeting',
t_x=.5, t_y=-0.1):
df = prep_data(df_in, threshold, ctrl_label)

target_data = df[df['target'] == label]

# Scatter plot for labeled data
ax.scatter(target_data['score'], target_data['-log10(pvalue)'],
s=size, linewidth=0.5, edgecolors='black', facecolors='black', label='target')

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['score'].iloc[i] + t_x, target_data['-log10(pvalue)'].iloc[i] + t_y),
color='black', size=size_txt)


def label_sensitivity_hit(ax, df_in, label, threshold, size=2, size_txt="auto",
ctrl_label = 'no-targeting',
t_x=.5, t_y=-0.1):
df = prep_data(df_in, threshold, ctrl_label)

target_data = df[df['target'] == label]

# Scatter plot for labeled data
ax.scatter(target_data['score'], target_data['-log10(pvalue)'],
s=size, linewidth=0.5, edgecolors='black', facecolors='#3182bd', label='target')

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['score'].iloc[i] + t_x, target_data['-log10(pvalue)'].iloc[i] + t_y),
color='black', size=size_txt)


def label_resistance_hit(ax, df_in, label, threshold, size=2, size_txt="auto",
ctrl_label = 'no-targeting',
t_x=.5, t_y=-0.1):
df = prep_data(df_in, threshold, ctrl_label)

target_data = df[df['target'] == label]

# Scatter plot for labeled data
ax.scatter(target_data['score'], target_data['-log10(pvalue)'],
s=size, linewidth=0.5, edgecolors='black', facecolors='#de2d26', label='target')

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['score'].iloc[i] + t_x, target_data['-log10(pvalue)'].iloc[i] + t_y),
color='black', size=size_txt)

## Scatter plot of replicates

def plotReplicateScatter(ax, adat_in, x, y, title, min_val=None, max_val=None, log_transform=True):
adat = adat_in[[x, y], :].copy()
Expand Down Expand Up @@ -208,3 +98,251 @@ def plotReplicateScatter(ax, adat_in, x, y, title, min_val=None, max_val=None, l
ax.get_legend().remove()

ax.grid(False)


## 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):

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'
)

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):

gamma = self.screen.getPhenotypeScores(
run_name=self.run_name,
score_name=self.gamma_score_name,
ctrl_label=self.ctrl_label,
threshold=self.threshold,
)
gamma['-log10(pvalue)'] = np.log10(gamma.pvalue) * -1

tau = self.screen.getPhenotypeScores(
run_name=self.run_name,
score_name=self.tau_score_name,
ctrl_label=self.ctrl_label,
threshold=self.threshold
)
tau['-log10(pvalue)'] = np.log10(tau.pvalue) * -1

rho = self.screen.getPhenotypeScores(
run_name=self.run_name,
score_name=self.rho_score_name,
ctrl_label=self.ctrl_label,
threshold=self.threshold
)
rho['-log10(pvalue)'] = np.log10(rho.pvalue) * -1

return gamma, tau, rho

def _volcano(
self, ax, df, up_hit, down_hit,
xlabel='phenotype score',
ylabel='-log10(pvalue)',
dot_size=1,
xlims=(-5, 5),
ylims=(0.1,6)
):

# Scatter plot for each category
ax.scatter( df.loc[df['label'] == 'target_non_hit', 'score'],
df.loc[df['label'] == 'target_non_hit', '-log10(pvalue)'],
alpha=0.1, s=dot_size, c='black', label='target_non_hit')

ax.scatter( df.loc[df['label'] == up_hit, 'score'],
df.loc[df['label'] == up_hit, '-log10(pvalue)'],
alpha=0.9, s=dot_size, c='#fcae91', label=up_hit)

ax.scatter( df.loc[df['label'] == down_hit, 'score'],
df.loc[df['label'] == down_hit, '-log10(pvalue)'],
alpha=0.9, s=dot_size, c='#bdd7e7', label=down_hit)

ax.scatter( df.loc[df['label'] == self.ctrl_label, 'score'],
df.loc[df['label'] == self.ctrl_label, '-log10(pvalue)'],
alpha=0.1, s=dot_size, c='gray', label=self.ctrl_label)

# 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,
xlabel='auto',
ylabel='-log10(pvalue)',
xlims=(-5, 5),
ylims=(0.1, 6)
):
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',
xlabel=xlabel, ylabel=ylabel,
dot_size=dot_size, xlims=xlims, ylims=ylims)

def drawVolcanoGamma(
self, ax,
gamma_df=None,
dot_size=1,
xlabel='auto',
ylabel='-log10(pvalue)',
xlims=(-5, 5),
ylims=(0.1, 6)
):
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',
xlabel=xlabel, ylabel=ylabel,
dot_size=dot_size, xlims=xlims, ylims=ylims)

def drawVolcanoTau(
self, ax,
tau_df=None,
dot_size=1,
xlabel='auto',
ylabel='-log10(pvalue)',
xlims=(-5, 5),
ylims=(0.1, 6)
):
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',
xlabel=xlabel, ylabel=ylabel,
dot_size=dot_size, xlims=xlims, ylims=ylims)

def drawRhoGammaScatter(
self, ax,
dot_size=1,
xlabel='auto',
ylabel='auto',
xlims=(-5, 5),
ylims=(-5, 5)
):

gamma_df, _, rho_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'],
gamma_df.loc[rho_df['label'] == 'target_non_hit', 'score'],
alpha=0.1, s=dot_size, c='black', label='target_non_hit')

ax.scatter( rho_df.loc[rho_df['label'] == up_hit, 'score'],
gamma_df.loc[rho_df['label'] == up_hit, 'score'],
alpha=0.9, s=dot_size, c='#fcae91', label=up_hit)

ax.scatter( rho_df.loc[rho_df['label'] == down_hit, 'score'],
gamma_df.loc[rho_df['label'] == down_hit, 'score'],
alpha=0.9, s=dot_size, c='#bdd7e7', label=down_hit)

ax.scatter( rho_df.loc[rho_df['label'] == self.ctrl_label, 'score'],
gamma_df.loc[rho_df['label'] == self.ctrl_label, 'score'],
alpha=0.1, s=dot_size, c='gray', label=self.ctrl_label)

# 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):
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)

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):
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)

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):
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)

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