transfer/code/scGPT/data/cellxgene/README.md

Build Training Cell Corpus from Cellxgene Census

• This documentation describes the procedure for building the pre-training cell corpus from the cellxgene census.
• Please note that this script is designed to run on a cluster with the SLURM workload manager for parallelization.
• You may need to modify the scripts to run on your own system.
• Internet access is required for querying the cellxgene census dataset.
• The scripts referred to in this document are located in the /data/cellxgene directory.

General Workflow for Cell Corpus Construction

• The general workflow is:

  0. (Optional) Configure the query list and query conditions.
  1. Build the cell index files based on query
  2. Download the dataset in h5ad chunks
  3. Transform the h5ad into scb (single-cell bank for high-performance IO)

(Optional) Configure the Query List and Query Conditions

• If you wish to customize your pre-training dataset, you may modify the data_config.py file and query_list.txt file.
• In the data_config.py file,
  • MAJOR_TISSUE_LIST refers to the general organ system defined in the cellxgene census; it defines the resolution we used to store the cells.
  • VERSION refers to the version of the cellxgene census; we used the version 2023-05-08 for our experiments. You may change it to the latest/LTS version. Check out the cellxgene census release plan for more information.
  • As we only use normal cells for pre-training, we filter the dataset by the DISEASE column in the cellxgene census.
  • For the pan-cancer model, we filter the dataset by the DISEASE column in the cellxgene census. The filtered cancer list is defined in the cancer_list.txt file. You may modify it according to your own needs.

Build the Cell Index Files Based on Query

• We first query cells from the cellxgene census and filter the cells according to our needs.
  • INDEX_PATH is the path to the cell index file (to be generated), cell index is the SOMA id (unique index in cellxgene census) for each cell in the cellxgene census.
  • QUERY_PATH is the path to the query file; each line in the query file is a general organ system defined in the cellxgene census.
• Replace them in the following command and run it to generate the cell index file:

INDEX_PATH="path/to/index"
QUERY_PATH="path/to/query"

./build_soma_idx.sh $INDEX_PATH $QUERY_PATH

Download the Dataset in Chunks

• We download the dataset in chunks; each chunk contains a maximum of 200000 cells, and the chunk size can be modified by changing the MAX_PARTITION_SIZE in the download_partition.sh file.
• Before running the script, you need to modify the DATA_PATH, QUERY_PATH and INDEX_PATH in the array_download_partition.sh file.
  • Keep the INDEX_PATH and QUERY_PATH consistent with the previous step.
  • DATA_PATH is the path to the directory to store the downloaded dataset. The resulting dataset will be stored in the h5ad format.
• Submit it to download the dataset (each compute node will need internet access):

sbatch array_download_partition.sh

Build the scb Files

• We preprocess the dataset and then transform the h5ad into scb (single-cell bank for high-performance I/O).
• Before running the script, you need to modify the DATA_PATH, OUTPUT_PATH, QUERY_PATH, and VOCAB_PATH in the array_build_scb.sh file.
  • Keep the DATA_PATH and QUERY_PATH consistent with the previous step.
  • OUTPUT_PATH is the path to store the scb files.
  • VOCAB_PATH is the path to the vocabulary file, which is used to map the gene id to token id.
• Then simply submit the job to the cluster by:

sbatch array_build_scb.sh