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plots.py
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plots.py
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import pandas as pd
import dash_bio as dashbio
import plotly.express as px
import plotly.graph_objects as go
def blank_fig():
fig = go.Figure(go.Scatter(x=[], y=[]))
fig.update_layout(template=None)
fig.update_xaxes(showgrid=False, showticklabels=False, zeroline=False)
fig.update_yaxes(showgrid=False, showticklabels=False, zeroline=False)
return fig
def methylation_heatmap(data):
methylation_data = pd.DataFrame(
columns=['Gene id', 'Patient id', 'Methylation'],
data=data,
)
methylation_data["Methylation"] = pd.to_numeric(methylation_data["Methylation"])
methylation_data = methylation_data.pivot('Patient id', 'Gene id', 'Methylation')
columns = list(methylation_data.columns.values)
rows = list(methylation_data.index)
cluster = 'all' if len(columns) > 1 else 'rows'
fig = dashbio.Clustergram(
data=methylation_data.loc[rows].values,
column_labels=columns,
row_labels=rows,
hidden_labels='row',
center_values=False,
cluster=cluster,
color_map=[
[0.0, 'white'],
[1.0, 'blue']
]
)
fig.for_each_trace(
lambda t: t.update(hovertemplate="Gene id: %{x}<br>Patient id: %{y}<br>Methylation: %{z}<extra></extra>")
if isinstance(t, go.Heatmap)
else t
)
return fig
def dysregulation_heatmap(data):
dysregulation_data = pd.DataFrame(
columns=['Regulation', 'Patient id', 'z-value'],
data=data,
)
dysregulation_data["z-value"] = pd.to_numeric(dysregulation_data["z-value"]).abs()
dysregulation_data = dysregulation_data.pivot('Patient id', 'Regulation', 'z-value')
columns = list(dysregulation_data.columns.values)
rows = list(dysregulation_data.index)
dysregulation_data = dysregulation_data.fillna(0)
cluster = 'all' if len(columns) > 1 else 'rows'
hidden_labels = []
if len(columns) > 20:
hidden_labels.append('col')
if len(rows) > 20:
hidden_labels.append('row')
fig = dashbio.Clustergram(
data=dysregulation_data.loc[rows].values,
column_labels=columns,
row_labels=rows,
hidden_labels=hidden_labels,
center_values=False,
cluster=cluster,
color_map=[
[0.0, 'white'],
[1.0, 'red']
],
)
for t in fig.data:
if isinstance(t, go.Heatmap):
z = [[ij if ij > 0 else '' for ij in i] for i in t['z']]
fig.for_each_trace(
lambda t: t.update(customdata=z)
if isinstance(t, go.Heatmap)
else t
)
fig.for_each_trace(
lambda t: t.update(hovertemplate="Regulation: %{x}<br>Patient id: %{y}<br>Z-score: %{customdata}<extra></extra>")
if isinstance(t, go.Heatmap)
else t
)
return fig
def mutation_bar(elements):
node_set = set()
for element in elements:
if 'mutation' in element['data']:
color = 'Source/Target'
if 'center' in element['classes']:
color = 'Query gene'
node_set.add((element['data']['id'], element['data']['mutation'], color))
ids, mutations, colors = map(list, zip(*node_set))
df = pd.DataFrame({'Gene id': ids, 'Fraction of patients with mutation': mutations, 'Type': colors, })
fig = px.bar(df, x='Gene id', y='Fraction of patients with mutation',
color='Type', color_discrete_map={'Query gene': 'red', 'Source/Target': 'grey'},
category_orders={"Type": ["Query gene", "Source/Target"]},
template="simple_white")
fig.update_layout(
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1
),
xaxis={'categoryorder': 'total descending'},
)
fig.update_xaxes(type='category', tickmode='array', ticktext=df['Gene id'].tolist())
return fig