scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism
It's prefered to create a new environment for scPRAM
conda create -n scPRAM python==3.8
conda activate scPRAM
scPRAM is available on PyPI, and could be installed using
# CUDA 11.6
pip install scpram --extra-index-url https://download.pytorch.org/whl/cu116
# CUDA 11.3
pip install scpram --extra-index-url https://download.pytorch.org/whl/cu113
# CUDA 10.2
pip install scpram --extra-index-url https://download.pytorch.org/whl/cu102
# CPU only
pip install scpram --extra-index-url https://download.pytorch.org/whl/cpu
Installation via Github is also provided
git clone https://github.com/jiang-q19/scPRAM
cd scPRAM
# CUDA 11.6
pip install scpram.whl --extra-index-url https://download.pytorch.org/whl/cu116
# CUDA 11.3
pip install scpram.whl --extra-index-url https://download.pytorch.org/whl/cu113
# CUDA 10.2
pip install scpram.whl --extra-index-url https://download.pytorch.org/whl/cu102
# CPU only
pip install scpram.whl --extra-index-url https://download.pytorch.org/whl/cpu
This process will take approximately 5 to 10 minutes, depending on the user's computer device and internet connectivition.
Out-of-sample prediction across cell types is demonstrated here. scPRAM can be easily used through three steps: data preprocessing, model training, prediction and evaluation. See the tutorial for a pipeline demonstration using the PBMC data set as an example.
If your perturbation dataset has already undergone quality control and preprocessing with Scanpy, please disregard this step. Otherwise, you can perform preprocessing using the following code. The specific parameters can be adjusted according to the actual situation of your dataset.
from scpram.data_process import adata_process
adata = adata_process(adata, min_genes=200, min_cells=10, n_top_genes=6000)Before starting the training, you need to determine the specific values in the key_dic based on your dataset. After holding out the perturbed data for the target type, the remaining data will be used as the training set.
from scpram import models
model = models.SCPRAM(input_dim=adata.n_vars, device='cuda:0')
model = model.to(model.device)
# key_dic varies with the adata
key_dic = {'condition_key': 'condition',
'cell_type_key': 'cell_type',
'ctrl_key': 'control',
'stim_key': 'stimulated',
'pred_key': 'predict',
}
cell_to_pred = 'CD4T'
# The training set does not contain the type of data to be predicted after the perturbation
train = adata[~((adata.obs[key_dic['cell_type_key']] == cell_to_pred) &
(adata.obs[key_dic['condition_key']] == key_dic['stim_key']))]
model.train_SCPRAM(train, epochs=100)After completing the training, out-of-sample predictions can be made, and the output results represent the predicted perturbation response. We combine the predicted response, actual response, and data before perturbation for performance evaluation.
from scpram import evaluate
pred = model.predict(train_adata=train,
cell_to_pred=cell_to_pred,
key_dic=key_dic,
ratio=0.005) # The ratio need to vary with the size of dataset
ground_truth = adata[(adata.obs[key_dic['cell_type_key']] == cell_to_pred)]
eval_adata = ground_truth.concatenate(pred)
evaluate.evaluate_adata(eval_adata=eval_adata,
cell_type=cell_to_pred,
key_dic=key_dic
)
