🙋 Please let us know if you find out a mistake or have any suggestions!
🌟 If you find this resource helpful, please consider to star this repository and cite our research:
Sicheng Feng, Siyu Li, Luonan Chen, Shengquan Chen. Unveiling potential threats: backdoor attacks in single-cell pretrained models. 2024.
Use python 3.9 from Anaconda
- torch==2.1.2
- anndata==0.10.7
- datasets==2.19.1
- einops==0.8.0
- matplotlib==3.9.0
- numba==0.59.1
- numpy==1.26.3
- pandas==2.2.2
- scanpy==1.10.1
- scgpt==0.2.1
- scikit-learn==1.5.0
- scipy==1.13.0
- torchtext==0.16.2
To install all dependencies:
conda create -n scBackdoor python=3.9
conda activate scBackdoor
pip install -r requirements.txtYou can download the example datasets from [scGPT] and [GeneFormer] , then place the downloaded contents under Yourpath4Dataset to reproduce the experiments.
You can download the pretrained models from [scGPT] (whole-human), [scBERT] and [GeneFormer], then place the downloaded contents under Yourpath4PretrainedModels to reproduce the experiments.
- Download datasets and pretrained models, then place them under
rightpathand adjust the path-params in the scripts. - Then you can try to reproduce the experiments with the provided scripts. For example, you can evaluate on Human Pancreas datasets by:
nohup ./run.sh & # for scGPT_ExpThe commands to run the experiments are as follows:
nohup ./run.sh & # for scGPT_Exp
nohup ./run.sh & # for scBERT_Exp
python geneformer_scBackdoor.py # for GeneFormer_ExpThe poison-related code is in the poison_utils.py or poison_trigger.py. You can find them in each experiment's folder.
The folder tree is as follows:
├── LICENSE
├── README.md -- introduction about the project
├── figures -- use for show up
│ └── fig1.png
├── requirements.txt -- requirements for installation
│── scGPT_Exp
│ ├── test -- the attack pipeline
│ │ ├── run.sh
│ │ └── scBackdoor.py
│ └── utils -- the scGPT items
│ ├── detect_tools.py
│ ├── poison_trigger.py
│ ├── preprocess.py
│ ├── print_tools.py
│ └── tools.py
├── GeneFormer_Exp
│ ├── geneformer -- the GeneFormer items
│ │ ├── __init__.py
│ │ ├── classifier.py
│ │ ├── classifier_utils.py
│ │ ├── collator_for_classification.py
│ │ ├── emb_extractor.py
│ │ ├── evaluation_utils.py
│ │ ├── gene_median_dictionary.pkl
│ │ ├── gene_name_id_dict.pkl
│ │ ├── in_silico_perturber.py
│ │ ├── in_silico_perturber_stats.py
│ │ ├── perturber_utils.py
│ │ ├── poison_utils.py
│ │ ├── pretrainer.py
│ │ ├── token_dictionary.pkl
│ │ └── tokenizer.py
│ └── geneformer_scBackdoor.py -- the attack pipeline
└── scBERT_Exp
├── attn_sum_save.py
├── finetune.py
├── lr_baseline_crossorgan.py
├── performer_pytorch -- the scBERT items
│ ├── __init__.py
│ ├── performer_pytorch.py
│ └── reversible.py
├── poison_utils.py
├── predict.py
├── preprocess.py
├── pretrain.py
├── run.sh -- the attack pipeline
└── utils.py
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scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI, Nature Methods 2024. [GitHub Repo]
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Transfer learning enables predictions in network biology, Nature 2023. [Huggingface Repo]
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scBERT as a Large-scale Pretrained Deep Language Model for Cell Type Annotation of Single-cell RNA-seq Data, Nature Machine Intelligence 2022. [GitHub Repo]
We sincerely thank the authors of the following open-source projects: