Merge branch 'development' of https://github.com/jyaacoub/MutDTA into…

… development

docs/requirements.txt

@@ -1,32 +1,31 @@
-numpy==1.23.5
-pandas==1.5.3
-tqdm==4.65.0
-rdkit==2023.3.1
-scipy==1.10.1
+numpy
+pandas
+tqdm
+rdkit
+scipy
 
 # for generating figures:
-matplotlib==3.7.1
-seaborn==0.11.2
-statannotations==0.6.0
+matplotlib
+seaborn
+statannotations
 
-lifelines==0.27.7 # used for concordance index calc
-#biopython # used for cmap
+lifelines
 
 # model building
-torch==2.0.1
-torch-geometric==2.3.1
-transformers==4.31.0 # huggingface needed for esm
+torch
+torch-geometric
+transformers
 
 # optional:
-torchsummary==1.5.1
-tabulate==0.9.0 # for torch_geometric.nn.summary
-ipykernel==6.23.1
-plotly==5.14.1
-requests==2.31.0
-#ray[tune]
+torchsummary
+tabulate
+ipykernel
+plotly
+requests
+ray[tune]
 
-submitit==1.4.5
-ProDy==2.4.1
+submitit
+ProDy
 
 # for chemgpt
-selfies==1.0.4
+selfies

docs/requirements_versions.txt

@@ -0,0 +1,32 @@
+numpy==1.23.5
+pandas==1.5.3
+tqdm==4.65.0
+rdkit==2023.3.1
+scipy==1.10.1
+
+# for generating figures:
+matplotlib==3.7.1
+seaborn==0.11.2
+statannotations==0.6.0
+
+lifelines==0.27.7 # used for concordance index calc
+#biopython # used for cmap
+
+# model building
+torch==2.0.1
+torch-geometric==2.3.1
+transformers==4.31.0 # huggingface needed for esm
+
+# optional:
+torchsummary==1.5.1
+tabulate==0.9.0 # for torch_geometric.nn.summary
+ipykernel==6.23.1
+plotly==5.14.1
+requests==2.31.0
+#ray[tune]
+
+submitit==1.4.5
+ProDy==2.4.1
+
+# for chemgpt
+selfies==1.0.4

playground.py

@@ -1,16 +1,9 @@
 # %%
-import pandas as pd
-
-df = pd.read_csv('/cluster/home/t122995uhn/projects/data/DavisKibaDataset/davis/nomsa_binary_original_binary/train/XY.csv', index_col=0)
-dft = pd.read_csv('/cluster/home/t122995uhn/projects/data/DavisKibaDataset/davis/nomsa_binary_original_binary/test/XY.csv', index_col=0)
-dfv = pd.read_csv('/cluster/home/t122995uhn/projects/data/DavisKibaDataset/davis/nomsa_binary_original_binary/val/XY.csv', index_col=0)
-
-trainp = df['prot_id'].drop_duplicates()
-testp = dft['prot_id'].drop_duplicates()
-valp = dfv['prot_id'].drop_duplicates()
-
-overlap_train_test = trainp[trainp.isin(testp)]
-overlap_train_val = trainp[trainp.isin(valp)]
-overlap_test_val = testp[testp.isin(valp)]
-
-# %%
+from src.data_processing.init_dataset import create_datasets
+
+create_datasets(
+    data_opt=['davis'],
+    feat_opt=['foldseek'],
+    edge_opt=['binary']
+)
+# %%

results/model_media/model_stats.csv

@@ -55,3 +55,5 @@ DGM_davis4D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.8289131436553395,0.72401732620
 EDIM_davis2D_nomsaF_binaryE_48B_0.0001LR_0.4D_2000E,0.8554472810106364,0.7147960047167372,0.6480432041534627,0.3808700198664558,0.3378217575814264,0.6171466761366019
 EDIM_davis3D_nomsaF_binaryE_48B_0.0001LR_0.4D_2000E,0.8432469960431263,0.7538742839566113,0.6228109253699023,0.3432475435749076,0.3027376194169844,0.5858733170019843
 EDIM_davis4D_nomsaF_binaryE_48B_0.0001LR_0.4D_2000E,0.8554137020365195,0.7430289570015053,0.6449800902544639,0.3552904408465783,0.2941160608088106,0.5960624471031356
+EDIM_davis0D_nomsaF_binaryE_48B_0.0001LR_0.4D_2000E,0.8421343521980615,0.7442023086590929,0.6221393745119952,0.3652477400883954,0.3289030617467355,0.6043572950568193
+EDIM_davis1D_nomsaF_binaryE_48B_0.0001LR_0.4D_2000E,0.8421023614186662,0.7223580257923967,0.6225670625924141,0.3758262699500786,0.3181417466188423,0.6130467110670105

results/model_media/model_stats_val.csv

@@ -33,3 +33,5 @@ DGM_davis4D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.8257259216950036,0.73150924245
 EDIM_davis3D_nomsaF_binaryE_48B_0.0001LR_0.4D_2000E,0.8366344407519283,0.7604833093809331,0.5981203556939766,0.3109887031416362,0.2938859237605942,0.5576636110969015
 EDIM_davis2D_nomsaF_binaryE_48B_0.0001LR_0.4D_2000E,0.8371103489279001,0.7312114712420225,0.5906166464692194,0.3223543817280097,0.3226479165694292,0.5677626103645869
 EDIM_davis4D_nomsaF_binaryE_48B_0.0001LR_0.4D_2000E,0.8333774894628959,0.759476307205861,0.6095430076677542,0.3768294961878906,0.31885009590497027,0.6138643956020666
+EDIM_davis0D_nomsaF_binaryE_48B_0.0001LR_0.4D_2000E,0.8289380911435942,0.7355891133257101,0.6157672298130831,0.4139988652707585,0.3623009506861369,0.6434274359014842
+EDIM_davis1D_nomsaF_binaryE_48B_0.0001LR_0.4D_2000E,0.8526864235317578,0.7780004172307035,0.6560782026672488,0.37383884772108467,0.3317426473954145,0.6114236237839398

src/data_analysis/figures.py

@@ -156,34 +156,6 @@ def fig2_pro_feat(df, verbose=False, sel_col='cindex', exclude=[], show=True, ad
     # reset stylesheet back to defaults
     mpl.rcParams.update(mpl.rcParamsDefault)
 
-# similar to above but only for one dataset to show significance between ESM and other features
-# this will be plotted as a jitter plot with error bars
-def fig4_pro_feat_violin(df, sel_dataset='davis', verbose=False, sel_col='cindex', exclude=[], 
-                         show=True, add_labels=True, add_stats=False):
-    # Extract relevant data
-    filtered_df = df[(df['edge'] == 'binary') & (~df['overlap']) & (df['lig_feat'].isna())]
-
-    # show all with fold info
-    filtered_df = filtered_df[(filtered_df['data'] == sel_dataset) & (filtered_df['fold'] != '')]
-    nomsa = filtered_df[(filtered_df['feat'] == 'nomsa')][sel_col]
-    msa = filtered_df[(filtered_df['feat'] == 'msa')][sel_col]
-    shannon = filtered_df[(filtered_df['feat'] == 'shannon')][sel_col]
-    esm = filtered_df[(filtered_df['feat'] == 'ESM')][sel_col]
-
-    # Might have different length for each feature
-    ax = sns.violinplot(data=[nomsa, msa, shannon, esm])
-    ax.set_xticklabels(['nomsa', 'msa', 'shannon', 'esm'])
-    ax.set_ylabel(sel_col)
-    ax.set_xlabel('Features')
-    ax.set_title(f'Feature {sel_col} for {sel_dataset}')
-
-    # %% Annotation for stats
-    if add_stats:
-        pairs=[('nomsa', 'msa'), ('nomsa', 'shannon'), ('msa', 'shannon')]
-        annotator = Annotator(ax, pairs, data=filtered_df, x='feat', y=sel_col, verbose=verbose)
-        annotator.configure(test='Mann-Whitney', text_format='star', loc='outside')
-        annotator.apply_and_annotate()
-
 # Figure 3 - Edge type cindex difference
 # Edges -> binary, simple, anm, af2
 def fig3_edge_feat(df, verbose=False, sel_col='cindex', exclude=[], show=True, add_labels=True):
@@ -275,6 +247,70 @@ def fig3_edge_feat(df, verbose=False, sel_col='cindex', exclude=[], show=True, a
     # reset stylesheet back to defaults
     mpl.rcParams.update(mpl.rcParamsDefault)    
 
+# Figure 4: violin plot with error bars for Cross-validation results to show significance among pro feats
+def fig4_pro_feat_violin(df, sel_dataset='davis', verbose=False, sel_col='cindex', exclude=[], 
+                         show=True, add_labels=True, add_stats=False):
+    # Extract relevant data
+    filtered_df = df[(df['edge'] == 'binary') & (~df['overlap']) & (df['lig_feat'].isna())]
+
+    # show all with fold info
+    filtered_df = filtered_df[(filtered_df['data'] == sel_dataset) & (filtered_df['fold'] != '')]
+    nomsa = filtered_df[(filtered_df['feat'] == 'nomsa')][sel_col]
+    msa = filtered_df[(filtered_df['feat'] == 'msa')][sel_col]
+    shannon = filtered_df[(filtered_df['feat'] == 'shannon')][sel_col]
+    esm = filtered_df[(filtered_df['feat'] == 'ESM')][sel_col]
+
+    # Get values for each node feature
+    ax = sns.violinplot(data=[nomsa, msa, shannon, esm])
+    ax.set_xticklabels(['nomsa', 'msa', 'shannon', 'esm'])
+    ax.set_ylabel(sel_col)
+    ax.set_xlabel('Features')
+    ax.set_title(f'Feature {sel_col} for {sel_dataset}')
+
+    # Annotation for stats
+    if add_stats:
+        pairs=[('nomsa', 'msa'), ('nomsa', 'shannon'), ('msa', 'shannon')]
+        annotator = Annotator(ax, pairs, data=filtered_df, x='feat', y=sel_col, verbose=verbose)
+        annotator.configure(test='Mann-Whitney', text_format='star', loc='inside', 
+                            hide_non_significant=True)
+        annotator.apply_and_annotate()
+
+    if show:
+        plt.show()
+
+# Figure 5: violin plot with error bars for Cross-validation results to show significance among edge feats
+def fig5_edge_feat_violin(df, sel_dataset='davis', verbose=False, sel_col='cindex', exclude=[],
+                            show=True, add_labels=True, add_stats=False):
+    filtered_df = df[(df['feat'] == 'nomsa') & (~df['overlap']) & (df['lig_feat'].isna())]
+    filtered_df = filtered_df[(filtered_df['data'] == sel_dataset) & (filtered_df['fold'] != '')]
+
+    filtered_df.sort_values(by=['edge'], inplace=True)
+
+    # Get values for each edge type
+    binary = filtered_df[filtered_df['edge'] == 'binary'][sel_col]
+    simple = filtered_df[filtered_df['edge'] == 'simple'][sel_col]
+    anm = filtered_df[filtered_df['edge'] == 'anm'][sel_col]
+    af2 = filtered_df[filtered_df['edge'] == 'af2'][sel_col]
+
+    # plot violin plot with annotations
+    ax = sns.violinplot(data=[binary, simple, anm, af2])
+    ax.set_xticklabels(['binary', 'simple', 'anm', 'af2'])
+    ax.set_ylabel(sel_col)
+    ax.set_xlabel('Edge type')
+    ax.set_title(f'Edge type {sel_col} for {sel_dataset}')
+
+    if add_stats:
+        pairs = [('binary', 'simple'), ('binary', 'anm'), ('binary', 'af2'), 
+                ('simple', 'anm'), ('simple', 'af2'), ('anm', 'af2')]
+        annotator = Annotator(ax, pairs, data=filtered_df, x='edge', y=sel_col, verbose=verbose)
+        annotator.configure(test='Mann-Whitney', text_format='star', loc='inside', hide_non_significant=True)
+        annotator.apply_and_annotate()
+
+    if show:
+        plt.show()
+
+
+
 def prepare_df(csv_p:str, old_csv_p='results/model_media/old_model_stats.csv') -> pd.DataFrame:
     df = pd.read_csv(csv_p)
 
@@ -304,6 +340,9 @@ def prepare_df(csv_p:str, old_csv_p='results/model_media/old_model_stats.csv') -
 
     return df
 
+
+
+
 if __name__ == '__main__':
     df = prepare_df('results/model_media/model_stats.csv')
 

src/data_processing/datasets.py

@@ -100,15 +100,10 @@ def __init__(self, save_root:str, data_root:str, aln_dir:str,
         assert feature_opt in self.FEATURE_OPTIONS, \
             f"Invalid feature_opt '{feature_opt}', choose from {self.FEATURE_OPTIONS}"
 
-        self.shannon = False
         self.feature_opt = feature_opt
-        if feature_opt == 'nomsa':
-            self.aln_dir = None # none treats it as np.zeros
-        elif feature_opt == 'msa':
+        self.aln_dir = None # none treats it as np.zeros
+        if feature_opt in ['msa', 'shannon']:
             self.aln_dir =  aln_dir # path to sequence alignments
-        elif feature_opt == 'shannon':
-            self.aln_dir = aln_dir
-            self.shannon = True
 
         assert edge_opt in self.EDGE_OPTIONS, \
             f"Invalid edge_opt '{edge_opt}', choose from {self.EDGE_OPTIONS}"
@@ -150,6 +145,10 @@ def pdb_p(self, code) -> str:
         """path to pdbfile for a particular protein"""
         raise NotImplementedError
 
+    def pddlt_p(self, code) -> str:
+        """path to plddt file for a particular protein"""
+        return None
+
     @abc.abstractmethod
     def cmap_p(self, code) -> str:
         raise NotImplementedError
@@ -332,10 +331,15 @@ def process(self):
             # extra_feat is Lx54 or Lx34 (if shannon=True)
             try:
                 pro_cmap = np.load(self.cmap_p(code))
-                extra_feat, edge_idx = target_to_graph(pro_seq, pro_cmap,
-                                                            threshold=self.cmap_threshold,
-                                                            aln_file=self.aln_p(code),
-                                                            shannon=self.shannon)
+                # updated_seq is for updated foldseek 3di combined seq
+                updated_seq, extra_feat, edge_idx = target_to_graph(target_sequence=pro_seq, 
+                                                                    contact_map=pro_cmap,
+                                                                    threshold=self.cmap_threshold, 
+                                                                    pro_feat=self.feature_opt,
+                                                                    aln_file=self.aln_p(code), 
+                                                                    # for foldseek feats
+                                                                    pdb_fp=self.pdb_p(code),
+                                                                    pddlt_fp=self.pddlt_p(code))
             except Exception as e:
                 raise Exception(f"error on protein graph creation for code {code}") from e
 
@@ -364,7 +368,7 @@ def process(self):
 
             pro = torchg.data.Data(x=torch.Tensor(pro_feat),
                                 edge_index=torch.LongTensor(edge_idx),
-                                pro_seq=pro_seq, # protein sequence for downstream esm model
+                                pro_seq=updated_seq, # Protein sequence for downstream esm model
                                 prot_id=prot_id,
                                 edge_weight=pro_edge_weight)
             processed_prots[prot_id] = pro
@@ -591,13 +595,22 @@ def pdb_p(self, code, safe=True):
         code = re.sub(r'[()]', '_', code)
         # davis and kiba dont have their own structures so this must be made using 
         # af or some other method beforehand.
-        if self.edge_opt not in cfg.STRUCT_EDGE_OPT: return None
+        if (self.edge_opt not in cfg.STRUCT_EDGE_OPT) and \
+            (self.feature_opt not in cfg.STRUCT_PRO_FEAT_OPT): return None
 
         file = glob(os.path.join(self.af_conf_dir, f'highQ/{code}_unrelaxed_rank_001*.pdb'))
         # should only be one file
         assert not safe or len(file) == 1, f'Incorrect pdb pathing, {len(file)}# of structures for {code}.'
         return file[0] if len(file) >= 1 else None
 
+    def pddlt_p(self, code, safe=True):
+        # this contains confidence scores for each predicted residue position in the protein
+        pdb_p = self.pdb_p(code, safe=safe)
+        if pdb_p is None: return None
+        # from O00141_unrelaxed_rank_001_alphafold2_ptm_model_1_seed_000.pdb
+        # to   O00141_scores_rank_001_alphafold2_ptm_model_1_seed_000.json
+        return pdb_p.replace('unrelaxed', 'scores').replace('.pdb', '.json')
+
     def cmap_p(self, code):
         return os.path.join(self.data_root, 'pconsc4', f'{code}.npy')
 
@@ -718,17 +731,21 @@ def pre_process(self):
         no_cmap = [c for c in codes if not os.path.isfile(self.cmap_p(c))]
         print(f'Number of codes without cmap files: {len(no_cmap)} out of {len(codes)}')
 
-        # Checking that structure and af_confs files are present if edgeW is anm or af2
+        # Checking that structure and af_confs files are present if required:
         no_confs = []
-        if self.edge_opt in cfg.STRUCT_EDGE_OPT:
-           no_confs = [c for c in codes if (
-               (self.pdb_p(c, safe=False) is None) or # no highQ structure
-                (len(self.af_conf_files(c)) < 2))]    # not enough af confirmations.
+        if self.edge_opt in cfg.STRUCT_EDGE_OPT or self.feature_opt in cfg.STRUCT_PRO_FEAT_OPT:
+            if self.feature_opt == 'foldseek':
+                # we only need HighQ structures for foldseek
+                no_confs = [c for c in codes if (self.pdb_p(c, safe=False) is None)]
+            else:
+                no_confs = [c for c in codes if (
+                (self.pdb_p(c, safe=False) is None) or # no highQ structure
+                    (len(self.af_conf_files(c)) < 2))]    # only if not for foldseek
 
-           # WARNING: TEMPORARY FIX FOR DAVIS (TESK1 highQ structure is mismatched...)
-           no_confs.append('TESK1')
+            # WARNING: TEMPORARY FIX FOR DAVIS (TESK1 highQ structure is mismatched...)
+            no_confs.append('TESK1')
 
-           print(f'Number of codes missing af2 configurations: {len(no_confs)} / {len(codes)}')
+            print(f'Number of codes missing af2 configurations: {len(no_confs)} / {len(codes)}')
 
         invalid_codes = set(no_aln + no_cmap + no_confs)
         # filtering out invalid codes: