Changelog.md includes all changes realted to v1.3.1

CHANGELOG.md

@@ -5,6 +5,15 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/).
 
+## [1.3.1] - 2026-04-27
+
+### New `auto_pysmccnet` param: `rename`
+- Added a `rename` boolean flag to the pysmccnet pipeline.
+    - When set to `False`, the pipeline preserves the original dataset column names instead of automatically appending datatype prefixes (column names must be strictly unique across omics)
+
+### Datasets Module:
+- Fixed a user-reported error ahead of the Multi Omics NETwork Analysis Workshop (MONET) workshop at the University of Colorado Anschutz Medical Campus.
+
 ## [1.3.0] - 2026-04-01
 
 ### Network Module (`bioneuralnet.network`)

README.md

@@ -8,7 +8,7 @@
 [![Documentation](https://img.shields.io/badge/docs-read%20the%20docs-blue.svg)](https://bioneuralnet.readthedocs.io/en/latest/)
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.17503083.svg)](https://doi.org/10.5281/zenodo.17503083)
 
-## Welcome to BioNeuralNet 1.3.0
+## Welcome to BioNeuralNet 1.3.1
 
 ![BioNeuralNet Logo](assets/logo_update.png)
 

bioneuralnet/__init__.py

@@ -13,7 +13,7 @@
 
 """
 
-__version__ = "1.3.0"
+__version__ = "1.3.1"
 
 # submodules to enable direct imports such as `from bioneuralnet import utils`
 from . import utils

bioneuralnet/datasets/monet/phenotype.csv

@@ -1,4 +1,5 @@
-0
+"phenotype"
+0
 1
 0
 0

bioneuralnet/network/pysmccnet/pipeline.py

@@ -18,7 +18,7 @@
 from ...utils.logger import get_logger
 logger = get_logger(__name__)
 
-def auto_pysmccnet(X: List[Union[pd.DataFrame, np.ndarray]], Y: Union[pd.DataFrame, np.ndarray], AdjustedCovar: Optional[pd.DataFrame] = None, preprocess: bool = False, Kfold: int = 5, subSampNum: int = 100, DataType: Optional[List[str]] = None, BetweenShrinkage: float = 2.0, ScalingPen: List[float] = [0.1, 0.1], saving_dir: str = os.getcwd(), tuneLength: int = 5, tuneRangeCCA: List[float] = [0.1, 0.5], tuneRangePLS: List[float] = [0.5, 0.9], EvalMethod: str = 'accuracy', ncomp_pls: int = 3, seed: int = 123, CutHeight: float = 1 - 0.1**10, min_size: int = 10, max_size: int = 100, summarization: str = "NetSHy", precomputed_fold_data: Optional[dict] = None, device: Optional[torch.device] = "cpu", dtype: torch.dtype = torch.float64) -> dict:
+def auto_pysmccnet(X: List[Union[pd.DataFrame, np.ndarray]], Y: Union[pd.DataFrame, np.ndarray], AdjustedCovar: Optional[pd.DataFrame] = None, preprocess: bool = False, Kfold: int = 5, subSampNum: int = 100, DataType: Optional[List[str]] = None, BetweenShrinkage: float = 2.0, ScalingPen: List[float] = [0.1, 0.1], saving_dir: str = os.getcwd(), tuneLength: int = 5, tuneRangeCCA: List[float] = [0.1, 0.5], tuneRangePLS: List[float] = [0.5, 0.9], EvalMethod: str = 'accuracy', ncomp_pls: int = 3, seed: int = 123, CutHeight: float = 1 - 0.1**10, min_size: int = 10, max_size: int = 100, summarization: str = "NetSHy", precomputed_fold_data: Optional[dict] = None, device: Optional[torch.device] = "cpu", dtype: torch.dtype = torch.float64, rename: bool = True) -> dict:
     """Automated SmCCNet workflow with GPU acceleration.
 
     Runs the complete SmCCNet pipeline supporting both CCA (continuous phenotype) and PLS (binary phenotype) modes. The workflow includes optional preprocessing, cross-validation for penalty tuning, subsampling for stability selection, and final network construction.
@@ -48,6 +48,7 @@ def auto_pysmccnet(X: List[Union[pd.DataFrame, np.ndarray]], Y: Union[pd.DataFra
         precomputed_fold_data (dict | None): Precomputed CV folds to bypass internal fold generation.
         device (torch.device | cpu): PyTorch device; if None, automatically selects GPU if available.
         dtype (torch.dtype): PyTorch data type for computations.
+        rename (bool): If True, prefix datatype to column names; if False, use original column names.
 
     Returns:
 
@@ -73,20 +74,38 @@ def auto_pysmccnet(X: List[Union[pd.DataFrame, np.ndarray]], Y: Union[pd.DataFra
     if DataType is None:
         DataType = [f"Omics{i+1}" for i in range(len(X))]
 
+    rename_mapping = {}
+
+    # validating uniqueness if rename is False
+    if not rename:
+        all_original_columns = []
+        for data_obj in X:
+            if hasattr(data_obj, 'columns'):
+                all_original_columns.extend(list(data_obj.columns))
+            else:
+                all_original_columns.extend([f"Feat{j+1}" for j in range(data_obj.shape[1])])
+
+        if len(all_original_columns) != len(set(all_original_columns)):
+            raise ValueError("Overlapping column names detected across omics. Set 'rename=True' or rename your columns manually.")
+
     feature_labels = []
 
     for i, data_obj in enumerate(X):
         prefix = DataType[i]
 
         if hasattr(data_obj, 'columns'):
-            # DataFrame: use real column names with DataType prefix
-            labels = [f"{prefix}_{col}" for col in data_obj.columns]
+            og_cols = data_obj.columns
+            labels = [f"{prefix}_{col}" for col in og_cols]
         else:
-            # Numpy array: fallback to generic
-            labels = [f"{prefix}_Feat{j+1}" for j in range(data_obj.shape[1])]
+            og_cols = [f"Feat{j+1}" for j in range(data_obj.shape[1])]
+            labels = [f"{prefix}_{col}" for col in og_cols]
 
         feature_labels.extend(labels)
 
+        if not rename:
+            for prefixed_col, og_col in zip(labels, og_cols):
+                rename_mapping[prefixed_col] = og_col
+
     # preprocessing
     if preprocess:
         print("\n--------------------------------------------------")
@@ -465,8 +484,10 @@ def auto_pysmccnet(X: List[Union[pd.DataFrame, np.ndarray]], Y: Union[pd.DataFra
         for i in range(len(X)):
             feature_labels.extend([f"{DataType[i]}_Feat{j+1}" for j in range(X[i].shape[1])])
 
-    Abar = get_abar(Ws_final, feature_label=feature_labels)
+    if rename_mapping:
+        feature_labels = [rename_mapping.get(label, label) for label in feature_labels]
 
+    Abar = get_abar(Ws_final, feature_label=feature_labels)
 
     if not os.path.exists(saving_dir):
         os.makedirs(saving_dir)

docs/source/conf.py

@@ -12,7 +12,7 @@
 try:
     release = metadata.version("bioneuralnet")
 except metadata.PackageNotFoundError:
-    release = "1.3.0"
+    release = "1.3.1"
 
 project = "BioNeuralNet"
 version = release

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = bioneuralnet
-version = 1.3.0
+version = 1.3.1
 author = Vicente Ramos
 author_email = vicente.ramos@ucdenver.edu
 description = A Graph Neural Network based Multi-Omics Network Data Analysis Tool