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refactor(tcga_analysis): comments
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@jyaacoub
jyaacoub committed Jul 17, 2024
1 parent a0064c5 commit 3517ea7
Showing 1 changed file with 68 additions and 68 deletions.
136 changes: 68 additions & 68 deletions tcga_analysis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -51,7 +51,8 @@ def load_TCGA(tcga_maf= "../data/tcga/mc3/mc3.v0.2.8.PUBLIC.maf", tcga_code_tabl

def plot_tcga_heat_map(prots_df=None, tcga_df=None, merged_df=None, top=10, title_prot_subset="all proteins",
title_postfix='', axis=None, show=True, tf_idf_score=False):
"""Returns merged dataframe of prots and tcga maf file on "gene" column"""
""" THIS IS THE OLD HEAT MAP, USE THE OTHER FUNCTION (`normalized_heatmap`).
Returns merged dataframe of prots and tcga maf file on "gene" column"""
if isinstance(prots_df, str):
prots_df = add_gene_name(pd.read_csv(prots_df))

Expand Down Expand Up @@ -103,72 +104,10 @@ def plot_tcga_heat_map(prots_df=None, tcga_df=None, merged_df=None, top=10, titl

return merged_df

def plot_combined_heatmap(df_tcga=None):
if not df_tcga:
df_tcga = load_TCGA()
df_tcga['case'] = df_tcga['Tumor_Sample_Barcode'].str[:12]
# df_tcga_uni['uniprot'] = df_tcga_uni['SWISSPROT'].str.split('_').str[0]
# df_tcga_uni['uniprot2'] = df_tcga_uni['TREMBL'].str.split(',').str[0].str.split('_').str[0]

cases = [True,False]
test_df = pd.read_csv('../downloads/test_prots_gene_names.csv').rename({'gene_name':'gene'}, axis=1)
csvs = {
'all proteins': "../downloads/all_prots.csv",
'test proteins with BindingDB': test_df,
'test proteins': test_df[test_df.db != 'BindingDB'],
}

_, axes = plt.subplots(len(csvs),len(cases), figsize=(12*len(cases),8*len(csvs)))

for i, drop_duplicates in enumerate(cases):
df_tcga_uni = df_tcga.drop_duplicates(subset='Tumor_Sample_Barcode') if drop_duplicates else df_tcga

for j, k in enumerate(csvs.keys()):
merged_df = plot_tcga_heat_map(csvs[k], df_tcga_uni, merged_df=None,
top=20,
title_prot_subset=k,
title_postfix=' (unique cases)' if drop_duplicates else '',
axis=axes[j][i], show=False)

plt.tight_layout()

def box_plot(df_tcga, normalize=False, limit=20):
groups = df_tcga.groupby(['Study Abbreviation', 'Tumor_Sample_Barcode']).size().reset_index(name='counts')
# Group by 'Study Abbreviation' to get the size of each group
group_sizes = groups.groupby('Study Abbreviation').groups

# Convert the dictionary to a DataFrame suitable for seaborn
df = pd.DataFrame([(k, v) for k, lst in group_sizes.items() for v in lst], columns=['Study Abbreviation', 'Group Size'])
df.sort_values(by='Group Size', ascending=False, inplace=True)

# limiting to top x cancers:
cancer_size = df.groupby('Study Abbreviation').sum().sort_values(by="Group Size", ascending=False).iloc[:limit]
cancer_size.rename({"Group Size": "Total"}, axis=1, inplace=True)
top_x_cancers = cancer_size.index

df = df[df['Study Abbreviation'].isin(top_x_cancers)]

if normalize:
# Merge the original dataframe with the aggregated counts dataframe
df = df.merge(cancer_size, on='Study Abbreviation')

# Normalize the 'Group Size' by dividing by the 'Aggregated Count'
df['Normalized Group Size'] = df['Group Size'] / df['Total']


# Plotting the boxplot with seaborn
plt.figure(figsize=(12, 8))
sns.boxplot(x='Study Abbreviation', y=f'{"Normalized " if normalize else ""}Group Size',
data=df)
plt.xlabel('Cancer type')
plt.ylabel(f'Mutation counts{" (normalized by cancer size)" if normalize else ""}')
plt.title(f'Boxplot of {"normalized " if normalize else ""}patient mutation count by cancer type')
plt.xticks(rotation=45)
plt.show()

def normalized_heatmap(merged_df, top=100, tfidf=False, normalize=True, normalize_by='avg',
biomart_csv='/cluster/home/t122995uhn/projects/downloads/mart_export.tsv',
variant=None, axis=None, merged_df_name='ALL TCGA'):
"""Normalize_by can take on the following values: [avg, max, None], if "None" then we normalize by the gene counts"""
df_gene_len = pd.read_csv(biomart_csv, sep='\t')
df_gene_len.dropna(inplace=True)

Expand Down Expand Up @@ -250,11 +189,74 @@ def normalized_heatmap(merged_df, top=100, tfidf=False, normalize=True, normaliz
axis.set_xlabel('Gene Name')
plt.colorbar(heatmap)

def plot_combined_heatmap(df_tcga=None):
"""Plotting heatmaps filtered for different test sets"""
if not df_tcga:
df_tcga = load_TCGA()
df_tcga['case'] = df_tcga['Tumor_Sample_Barcode'].str[:12]
# df_tcga_uni['uniprot'] = df_tcga_uni['SWISSPROT'].str.split('_').str[0]
# df_tcga_uni['uniprot2'] = df_tcga_uni['TREMBL'].str.split(',').str[0].str.split('_').str[0]

cases = [True,False]
test_df = pd.read_csv('../downloads/test_prots_gene_names.csv').rename({'gene_name':'gene'}, axis=1)
csvs = {
'all proteins': "../downloads/all_prots.csv",
'test proteins with BindingDB': test_df,
'test proteins': test_df[test_df.db != 'BindingDB'],
}

_, axes = plt.subplots(len(csvs),len(cases), figsize=(12*len(cases),8*len(csvs)))

for i, drop_duplicates in enumerate(cases):
df_tcga_uni = df_tcga.drop_duplicates(subset='Tumor_Sample_Barcode') if drop_duplicates else df_tcga

for j, k in enumerate(csvs.keys()):
merged_df = plot_tcga_heat_map(csvs[k], df_tcga_uni, merged_df=None,
top=20,
title_prot_subset=k,
title_postfix=' (unique cases)' if drop_duplicates else '',
axis=axes[j][i], show=False)

plt.tight_layout()

def box_plot(df_tcga, normalize=False, limit=20):
groups = df_tcga.groupby(['Study Abbreviation', 'Tumor_Sample_Barcode']).size().reset_index(name='counts')
# Group by 'Study Abbreviation' to get the size of each group
group_sizes = groups.groupby('Study Abbreviation').groups

# Convert the dictionary to a DataFrame suitable for seaborn
df = pd.DataFrame([(k, v) for k, lst in group_sizes.items() for v in lst], columns=['Study Abbreviation', 'Group Size'])
df.sort_values(by='Group Size', ascending=False, inplace=True)

# limiting to top x cancers:
cancer_size = df.groupby('Study Abbreviation').sum().sort_values(by="Group Size", ascending=False).iloc[:limit]
cancer_size.rename({"Group Size": "Total"}, axis=1, inplace=True)
top_x_cancers = cancer_size.index

df = df[df['Study Abbreviation'].isin(top_x_cancers)]

if normalize:
# Merge the original dataframe with the aggregated counts dataframe
df = df.merge(cancer_size, on='Study Abbreviation')

# Normalize the 'Group Size' by dividing by the 'Aggregated Count'
df['Normalized Group Size'] = df['Group Size'] / df['Total']


# Plotting the boxplot with seaborn
plt.figure(figsize=(12, 8))
sns.boxplot(x='Study Abbreviation', y=f'{"Normalized " if normalize else ""}Group Size',
data=df)
plt.xlabel('Cancer type')
plt.ylabel(f'Mutation counts{" (normalized by cancer size)" if normalize else ""}')
plt.title(f'Boxplot of {"normalized " if normalize else ""}patient mutation count by cancer type')
plt.xticks(rotation=45)
plt.show()


# %%
df_tcga = load_TCGA()


# %%
variants = [None, 'Missense_Mutation', 'Silent', "3'UTR", "Nonsense_Mutation"]
test_df = pd.read_csv('../downloads/test_prots_gene_names.csv').rename({'gene_name':'gene'}, axis=1)
Expand All @@ -274,6 +276,4 @@ def normalized_heatmap(merged_df, top=100, tfidf=False, normalize=True, normaliz
for i, v in enumerate(variants):
normalized_heatmap(merged_df, normalize_by='avg', variant=v, axis=axes[i][j], merged_df_name=k)

plt.tight_layout()

# %%
plt.tight_layout()

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