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Missing dependencies and instructions in README #27

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11 changes: 6 additions & 5 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -27,17 +27,18 @@ https://drive.weixin.qq.com/s?k=AJEAIQdfAAoUxhXE7r
The test single-cell transcriptomics data file should be pre-processed by first revising gene symbols according to [NCBI Gene database](https://www.ncbi.nlm.nih.gov/gene) updated on Jan. 10, 2020, wherein unmatched genes and duplicated genes will be removed. Then the data should be normalized with the `sc.pp.normalize_total` and `sc.pp.log1p` method in `scanpy` (Python package), detailed in `preprocess.py`.

You can download this repo and run the demo task on your computing machine within about 4 hours.

It expects the gene2vec embedding `gene2vec_16906.npy` in a `data` folder parallel to the `scBERT` repository (e.g., `../data/gene2vec_16906.npy` if your current working directory is ``scBERT`).

- Fine-tune using pre-trained models
```
python -m torch.distributed.launch --data_path "fine-tune_data_path" --model_path "pretrained_model_path" finetune.py
python -m torch.distributed.launch finetune.py --data_path "fine-tune_data_path" --model_path "pretrained_model_path"
#The cell type information is stored in 'label' and 'label_dict' files.
```


- Predict using fine-tuned models
```
python --data_path "test_data_path" --model_path "finetuned_model_path" predict.py
python predict.py --data_path "test_data_path" --model_path "finetuned_model_path"
#The cell type information will be loaded frome 'label' and 'label_dict' files.
```

Expand All @@ -46,7 +47,7 @@ python --data_path "test_data_path" --model_path "finetuned_model_path" predict.

The detection of novel cell type can be done by thresholding the predicted probabilities. (Default threshold=0.5)
```
python --data_path "test_data_path" --model_path "finetuned_model_path" --novel_type True --unassign_thres "custom_threshold" predict.py
python predict.py --data_path "test_data_path" --model_path "finetuned_model_path" --novel_type True --unassign_thres "custom_threshold
```

- Expected output
Expand Down Expand Up @@ -91,4 +92,4 @@ The copyright holder for this project is Tencent AI Lab.
All rights reserved.

# Citation
Yang, F., Wang, W., Wang, F. et al. scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data. Nat Mach Intell (2022). https://doi.org/10.1038/s42256-022-00534-z
Yang, F., Wang, W., Wang, F. et al. scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data. Nat Mach Intell (2022). https://doi.org/10.1038/s42256-022-00534-z
4 changes: 3 additions & 1 deletion requirements.txt
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Expand Up @@ -4,5 +4,7 @@ transformers==4.6.1
scanpy==1.7.2
scikit-learn==0.24.2
scipy==1.5.4
numpy==1.19.2
numpy==1.20
pandas==1.1.5
einops==0.6.0
matplotlib<3.7