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added function to easily plot signatures from exposures signatures an… #3

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added function to easily plot signatures from exposures signatures an… #3

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29 changes: 28 additions & 1 deletion src/mutation_signatures_visualization.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
# Load required module
import matplotlib
matplotlib.use('Agg')
import sys, os, numpy as np, seaborn as sns, matplotlib.pyplot as plt
import sys, os, numpy as np, seaborn as sns, matplotlib.pyplot as plt, pandas as pd
from matplotlib.offsetbox import AnchoredText
import matplotlib.gridspec as gridspec
sns.set_style('whitegrid')
Expand Down Expand Up @@ -47,6 +47,33 @@
################################################################################
# PLOTS
################################################################################

# counts_df is samples-by-categories
# exposures_df samples-by-signatures
# signatures_df is signatures-by-categories
def plot_signatures(counts_df, signature_df, exposure_df, output_file):
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Add palette and ylabel as optional arguments

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More specifically, try to make the function call match the sbs_signature_plot (since you are eventually calling tht)

#contribs = pd.DataFrame(index=signature_df.columns, data=0)
output = []
# for each signature
for sig in signature_df.index:
contrib = 0.
# we want to iterate through each sample
for sample in exposure_df.index:
# and calculate the number of mutations attribution to signature i in sample j
contrib += exposure_df.loc[sample, sig]*counts_df.loc[sample].sum() # calculate the contribution
output.append(signature_df.loc[sig]*contrib)
#contribs[sig].append(phi[i]*contrib)
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Not sure we need to keep commented out code around

df = pd.concat(output, axis=1)
df = df.transpose()
# Plot the counts and signatures
# plt_df = pd.DataFrame(data=[X.sum(axis=0)] + contribs,
# index=['Counts'] + sig_names,
# columns=sbs96_df.columns)
sbs_signature_plot(df, palette=BROAD, ylabel='Count')
# Save to file
plt.tight_layout()
plt.savefig(output_file)

def sbs_signature_plot(data, fig=None, sharex=False, sharey='row',
xlabel='Trinucleotide sequence motifs',
ylabel='Probability', row_labels=True,
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