feat(tcga): analysis script #95 #111

tcga_analysis.py

@@ -0,0 +1,99 @@
+#%% 1.Gather data for davis,kiba and pdbbind datasets
+import os
+import pandas as pd
+import matplotlib.pyplot as plt
+from src.analysis.utils import combine_dataset_pids
+from src import config as cfg
+df_prots = pd.read_csv('../data/all_prots_bindingdb.csv')
+
+
+#%% 2. Load TCGA data
+df_tcga = pd.read_csv('../data/TCGA_ALL.maf', sep='\t')
+
+#%% 3. Pre filtering
+df_tcga = df_tcga[df_tcga['Variant_Classification'] == 'Missense_Mutation']
+df_tcga['seq_len'] = pd.to_numeric(df_tcga['Protein_position'].str.split('/').str[1])
+df_tcga = df_tcga[df_tcga['seq_len'] < 5000]
+df_tcga['seq_len'].plot.hist(bins=100, title="sequence length histogram capped at 5K")
+plt.show()
+df_tcga = df_tcga[df_tcga['seq_len'] < 1200]
+df_tcga['seq_len'].plot.hist(bins=100, title="sequence length after capped at 1.2K")
+
+#%% 4. Merging df_prots with TCGA
+df_tcga['uniprot'] = df_tcga['SWISSPROT'].str.split('.').str[0]
+
+dfm = df_tcga.merge(df_prots[df_prots.db != 'davis'], 
+                    left_on='uniprot', right_on='prot_id', how='inner')
+
+# for davis we have to merge on HUGO_SYMBOLS
+dfm_davis = df_tcga.merge(df_prots[df_prots.db == 'davis'], 
+                          left_on='Hugo_Symbol', right_on='prot_id', how='inner')
+
+dfm = pd.concat([dfm,dfm_davis], axis=0)
+
+del dfm_davis # to save mem
+#%%
+tcga_tss = pd.read_csv('../data/tcga_code_tables/tissueSourceSite.tsv', sep='\t')
+tcga_bcr = pd.read_csv('../data/tcga_code_tables/bcrBatchCode.tsv', sep='\t')
+tcga_codes = tcga_tss.merge(tcga_bcr.drop_duplicates(subset='Study Name'), on='Study Name', how='left')
+tcga_codes = tcga_codes[['TSS Code', 'Study Abbreviation']]
+
+#%%
+dfm['TSS Code'] = dfm['Tumor_Sample_Barcode'].str.split('-').str[1] # get second id to match with TSS code for cancer type
+dfm = dfm.merge(tcga_codes, on='TSS Code', how='left')
+
+# %% 5. Post filtering step
+# 5.1. Filter for only those sequences with matching sequence length (to get rid of nonmatched isoforms)
+# seq_len_x is from tcga, seq_len_y is from our dataset 
+tmp = len(dfm)
+# allow for some error due to missing amino acids from pdb file in PDBbind dataset
+#   - assumption here is that isoforms will differ by more than 50 amino acids
+dfm = dfm[(dfm.seq_len_y <= dfm.seq_len_x) & (dfm.seq_len_x<= dfm.seq_len_y+50)]
+print(f"Filter #1 (seq_len)     : {tmp:5d} - {tmp-len(dfm):5d} = {len(dfm):5d}")
+
+# 5.2. Filter out those that dont have the same reference seq according to the "Protein_position" and "Amino_acids" col
+
+# Extract mutation location and reference amino acid from 'Protein_position' and 'Amino_acids' columns
+dfm['mt_loc'] = pd.to_numeric(dfm['Protein_position'].str.split('/').str[0])
+dfm = dfm[dfm['mt_loc'] < dfm['seq_len_y']]
+dfm[['ref_AA', 'mt_AA']] = dfm['Amino_acids'].str.split('/', expand=True)
+
+dfm['db_AA'] = dfm.apply(lambda row: row['prot_seq'][row['mt_loc']-1], axis=1)
+
+# Filter #2: Match proteins with the same reference amino acid at the mutation location
+tmp = len(dfm)
+dfm = dfm[dfm['db_AA'] == dfm['ref_AA']]
+print(f"Filter #2 (ref_AA match): {tmp:5d} - {tmp-len(dfm):5d} = {len(dfm):5d}")
+print('\n',dfm.db.value_counts())
+
+# %% final seq len distribution
+
+n_bins = 25
+lengths = dfm.seq_len_x
+fig, ax = plt.subplots(1, 1, figsize=(10, 5))
+
+# Plot histogram
+n, bins, patches = ax.hist(lengths, bins=n_bins, color='blue', alpha=0.7)
+ax.set_title('TCGA final filtering for db matches')
+
+# Add counts to each bin
+for count, x, patch in zip(n, bins, patches):
+    ax.text(x + 0.5, count, str(int(count)), ha='center', va='bottom')
+
+ax.set_xlabel('Sequence Length')
+ax.set_ylabel('Frequency')
+
+plt.tight_layout()
+plt.show()
+
+# %% Getting updated sequences
+def apply_mut(row):
+    ref_seq = list(row['prot_seq'])
+    ref_seq[row['mt_loc']-1] = row['mt_AA']
+    return ''.join(ref_seq)
+
+dfm['mt_seq'] = dfm.apply(apply_mut, axis=1)
+
+
+# %%
+dfm.to_csv("/cluster/home/t122995uhn/projects/data/tcga/tcga_maf_davis_pdbbind.csv")