MetaPaCS (an Ensemble stacking-Based Model for Identifying Pancreatic Cancer Subtypes), is an accurate and cost-effective model for Pancreatic Cancer subtype prediction based on RNA-seq Expression data only. Leveraging multiple different machine learning techniques, MetaPaCS is able to identify accurately and efficiently predict 4 different Pcancreatic Cancer subtypes, which may provide insights into the characteristics of these subtypes that can significantly aid clinical decision-making processes.
- Download MetaPaCS and LIMMAdata.csv from the github
- Open the MetaPaCS code in jupyter notebook
- Specify the current directory, put LIMMAdata.csv or your input data in this directory
```import os os.chdir() #change to your directory import pandas as pd !pip install torch torchvision torchaudio ```
- Put LIMMAdata.csv (test data) or your own input data (must be structured as below) in this directory
```le = LabelEncoder()
x = pd.read_csv('') #your file here
data = "" #give the data a name ```
- Specify the target directory for the output in the evaluation area of the code
- Enter in the name of your input in block 4, run the code in the order of the block
- Use the other blocks of part 4 to measure performance of all possible unique combinations of 2 or 4 base classifiers
- Change the configuration of the code to work for the combination blocks by removing the hashtags from the path below to open the alternate output path
```#filepath = '//' #for combination testing, enter the directory path for saving the results
folderpath = "//" #enter the directory path for saving the results
#folderpath = "/" #for combination testing
foldername = f"{run}/"
filepath = f"{folderpath}{foldername}"
subdir = f"{testing}/{stacked}/{model_name}/"
filename = f"{data}({randomstate}),metrics.csv"
filename2 = f"predictions.csv"
full_path = os.path.join(filepath, subdir, filename) #evaluation
full_path2 = os.path.join(filepath, subdir, filename2) #predictions ```
The prediction results and evaluations will be stored and exported to a folder named stackeval (10 classifiers LOO) in your specified directory. Evaluations are saved for all base and meta-learning classifiers.
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If you find any bugs or problems, or you have any comments on S, please don't hesitate to contact via email [email protected] or Issues.
Mengtao Sun, Nick Peterson, Shibiao Wan, Xinchao Wu
MetaPaCS: A novel meta-learning model for pancreatic cancer subtype prediction Nick Peterson, Mengtao Sun, Xinchao Wu, Jieqiong Wang, Shibiao Wan bioRxiv TBD; doi: TBD
GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.