Welcome to CANAL’s documentation!

Continually adapting pre-trained language model to universal annotation of single-cell RNA-seq data

PyTorch implementation of CANAL, a universal cell-type annotation tool that continuously fine-tunes a pretrained language model trained on a large amount of unlabeled scRNA-seq data, as new well-labeled data emerges.

The source code of CANAL is available at https://github.com/aster-ww/CANAL-torch/tree/main/CANAL

Dependencies

CANAL requires the following:

• python (3.7 recommended)

• torch (1.10.1)

• anndata (0.8.0)

• scanpy (1.9.1)

• pandas (1.3.5)

• scikit-learn (1.0.2)

• local-attention (1.6.0)

• einops (0.6.0)

• numpy (1.21.6)

• h5py (3.6.0)

• cuda toolkit and nvidia cudnn if using GPU.

To apply the CANAL model:

• Prepare the preprocessed scRNA-seq data: gene_align and normalize in the preprocess module are required to obtain AnnData objects for network inputs.

• Run CANAL at the initial stage: use CANAL_model.train in the model module by setting current_stage=1. The model is initialized by the pre-trained model checkpoint on the Panglao dataset

• Run CANAL at the incremental stage: use CANAL_model.train in the model module by setting current_stage≥1. The model is initialized by the model trained at previous stage

• Predict cell types of the test data: use CANAL_model.predict in the model module to obtain the predicted cell types of the test data

• Evaluate model performance: If true cell types of the test data is available, use CANAL_model.evaluation in the model module to evaluate the performance of current fine-tuned model

There are four examples in the Tutorial to run CANAL:

• Tutorial 1: Preprocess the raw scRNA-seq datasets

• Tutorial 2: Run CANAL with data stream from various batches

• Tutorial 3: Run CANAL with data stream from different tissues

• Tutorial 4: Apply CANAL on test data with novel cells

Hyper-parameters

• lambda: default 0.1, the strength of representation distillation loss

• L: default 1000, the size of example bank

Data Availability

Link	Description
https://drive.google.com/drive/folders/1BMf-N-k-3aCEY7CJvUcK9nZZ2UD7p3C0?usp=sharing	Datasets of the pancreas experiemnts
https://drive.google.com/drive/folders/1CaBySV_EFAPPrlpSevEewFds5cjJxC_T?usp=sharing	Datasets of the cross-tissue experiemnts
https://drive.google.com/drive/folders/1OGMWxR7qTWd_p21d57EyNWv5X48BNN0M?usp=sharing	Datasets of the human immune experiemnts

The detailed information and download URL of pre-trained model checkpoint, gene2vec embedding and the Panglao dataset used for pre-training can be seen at: https://github.com/TencentAILabHealthcare/scBERT

If you have any questions, please contact: wanhui1997@pku.edu.cn

Contents:

• Welcome to CANAL’s documentation!
• API documentation
  • preprocess
  • model
• Tutorial
  • Tutorial 1: prepare the preprocessed scRNA-seq data
  • Tutorial 2: run CANAL with data streams from various batches
  • Tutorial 3: run CANAL on cross-tissue experiments
  • Tutorial 4: detect novel cells in the test data via CANAL