Installation

The following modules are needed to run scGO.

Required modules

module	version
python	3.8.13
torch	1.9.1
scanpy	1.9.3
scikit-learn	1.3.2
scipy	1.10.1

Conda is recommended for package management, you can create a new conda environment and then install the packages. Here’s an example of how you can do it. Create a new conda environment:

conda create -n scGO python=3.8.13

Activate the newly created environment:

conda activate scGO

Install the required modules:

pip install torch==1.9.1
pip install scanpy==1.9.3
pip install scikit-learn==1.3.2
pip install scipy==1.10.1

The entire installation will take about 1-5 minutes. After installing all the essential packages, reset the environment’s state by deactivating and reactivating the environment:

conda deactivate
conda activate scGO

We have also provided a yaml file in the repository so you can install the dependencies through the configuration file:

conda env create -f scGO.yaml

The source code and data processing scripts are available on GitHub. You can download them by using the git clone command:

git clone https://github.com/yulab2021/scGO.git

TandemMod offers three modes: de novo training, transfer learning, and prediction. Researchers can train from scratch, fine-tune pre-trained models, or apply existing models for predictions. It provides a user-friendly solution for studying RNA modifications.

In the provided repository, the pretrained models are located under the ./models directory, and the data processing scripts and the main script are located under the ./scripts directory:

scGO
├── data
│   ├── goa_human.gaf.zip
│   └── TF_annotation_hg38.demo.tsv
├── demo
│   ├── baron_data.csv
│   ├── baron_data_filtered.csv
│   ├── baron_data_filtered.predicted.csv
│   ├── baron_meta_data.csv
│   ├── baron_meta_data_senescence_score.csv
│   ├── baron_meta_data_senescence_score.predicted.csv
│   ├── feature
│   ├── gene_TF_dict
│   ├── gene_to_TF_transform_matrix
│   ├── goa_human.gaf.zip
│   ├── GO_mask
│   ├── GO_TF_mask
│   ├── test_data.csv
│   ├── TF_annotation_hg38.demo.tsv
│   ├── TF_gene_dict
│   └── TF_mask
├── models
│   ├── scGO.demo.pkl
│   └── scGO.senescence_score.demo.pkl
├── README.md
├── results_reproduce
├── scGO.yml
└── scripts
    ├── data_processing.py
    ├── __init__.py
    ├── models.py
    ├── __pycache__
    │   ├── models.cpython-38.pyc
    │   └── utils.cpython-38.pyc
    ├── rds_to_csv.r
    ├── scGO.py
    ├── test.csv
    ├── test.ipynb
    └── utils.py