/ChatCell

ChatCell: Facilitating Single-Cell Analysis with Natural Language

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ChatCell: Facilitating Single-Cell Analysis with Natural Language

💻 Demo • 🤗 Dataset • ⌚️ QuickStart • 🛠️ Usage • 🚀 Evaluation • 🧬 Single-cell Analysis Tasks • 📝 Cite

The project ChatCell aims to facilitate single-cell analysis with natural language, which derives from the Cell2Sentence technique to obtain cell language tokens and utilizes cell vocabulary adaptation for T5-based pre-training. Have a try with the demo at GPTStore App!

✨ Acknowledgements

Special thanks to the authors of Cell2Sentence: Teaching Large Language Models the Language of Biology and Representing cells as sentences enables natural-language processing for single-cell transcriptomics for their inspiring work.

The workflow_data/src folder and transform.py in this project are grounded in their research. Grateful for their valuable contributions to the field.

🆕 News

  • [Feb 2024] Our ChatCell app is now live on GPTStore, give it a try📱!
  • [Feb 2024] We released the model weights based on T5 in small, base, and large configurations on Huggingface 🤗.
  • [Feb 2024] We released the instructions of ChatCell on Huggingface 🤗.

📌 Table of Contents

⌚️ Quickstart

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("zjunlp/chatcell-small")
model = AutoModelForSeq2SeqLM.from_pretrained("zjunlp/chatcell-small")
input_text="Distinguish between resistant and sensitive cancer cells in response to Cisplatin, using the data from the 100 most expressed genes in descending order MYL12B FTL MYL12A HIST1H4C RPL23 GSTP1 RPS3 ENO1 RPLP1 TXN ANXA2 PPP1CB B2M RPLP0 HSPA8 H2AFZ TPI1 ANXA1 RPL7 GAPDH CHP1 LDHA RPL3 S100A11 PRDX1 CALM2 CAPZA1 SLC25A5 RPS27 YWHAZ GNB2L1 PTBP3 RPS6 MOB1A S100A2 ACTG1 BROX SAT1 RPL35A CA2 PSMB4 RPL8 TBL1XR1 RPS18 HNRNPH1 RPL27 RPS14 RPS11 ANP32E RPL19 C6ORF62 RPL9 EEF1A1 RPL5 COLGALT1 NPM1 CCT6A RQCD1 CACUL1 RPL4 HSP90AA1 MALAT1 ALDOA PSMA4 SEC61G RPL38 PSMB5 FABP5 HSP90AB1 RPL35 CHCHD2 EIF3E COX4I1 RPL21 PAFAH1B2 PTMA TMED4 PSMB3 H3F3B AGO1 DYNLL1 ATP5A1 LDHB COX7B ACTB RPS27A PSME2 ELMSAN1 NDUFA1 HMGB2 PSMB6 TMSB10 SET RPL12 RPL37A RPS13 EIF1 ATP5G1 RPS3A TOB1."

# Encode the input text and generate a response with specified generation parameters
input_ids = tokenizer(input_text,return_tensors="pt").input_ids
output_ids = model.generate(input_ids, max_length=512, num_return_sequences=1, no_repeat_ngram_size=2, top_k=50, top_p=0.95, do_sample=True)

# Decode and print the generated output text
output_text = tokenizer.decode(output_ids[0],skip_special_tokens=True)
print(output_text)

🛠️ Usage

📚 Step1: Prepare the data

❗️Note: You can download the original data from the raw_data directory. Alternatively, you can directly download the pre-processed data we provide on huggingface to skip Step 1 of the process.

Change to the evaluation directory with the command: cd workflow_data.

1. For tasks such as random cell sentence generation, pseudo-cell generation, and cell type annotation, we utilize cells from the SHARE-seq mouse skin dataset.

  • Follow these steps to use the transform.py script (This file was initially developed by the Cell2Sentence team, thanks for their great work!🤗) to translate scENA-seq data into cell sentence:

    • Define data_filepath to specify the path to your downloaded SHARE-seq mouse skin dataset .h5ad file.
    • Define output_dir to specify the directory where the generated cell sentences will be saved.
    • Define eval_output_dir to specify where figures and evaluation metrics will be stored.
    • Run the transformation process by executing the following command in your terminal: python transform.py.

  • Then covert cell sentences to instructions with mouse_to_json.py:

    • Set input_path to the output_dir specified in transform.py.
    • Define train_json_file_path, val_json_file_path, and test_json_file_path to specify the paths where you want to save your train, validation, and test datasets in JSON format, respectively.
    • Run the following command in your terminal to start the conversion process: python mouse_to_json.py.

2. For the drug sensitivity prediction task, we select GSE149383 and GSE117872 datasets.

  • For GSE149383: Open GSE149383_to_json.py, define expression_data_path and cell_info_path to the location of your downloaded erl_total_data_2K.csv and erl_total_2K_meta.csv file.
  • For GSE117872: Open GSE117872_to_json.py, define expression_data_path and cell_info_path to the location of your downloaded GSE117872_good_Data_TPM.txt and GSE117872_good_Data_cellinfo.txt file.
  • Update output_json_path with the desired location for the JSON output files.
  • Execute the conversion script:
    • Run python GSE149383_to_json.py for the GSE149383 dataset.
    • Run python GSE117872_to_json.py for the GSE117872 dataset.
  • Open split.py, define input_path to the same locations as output_json_path used above. Specify the locations for train_json_file_path, val_json_file_path, and test_json_file_path where you want the split datasets to be saved.
  • Run the script with python split.py to split the dataset into training, validation, and test sets.

3. After preparing instructions for each specific task, follow the steps below to merge the datasets using the merge.py script.

  • Ensure that the paths for train_json_file_path, val_json_file_path, and test_json_file_path are correctly set to point to the JSON files you previously generated for each dataset, such as GSE117872, GSE149383, and mouse.
  • Run python merge.py to start the merging process. This will combine the specified training, validation, and testing datasets into a unified format, ready for further analysis or model training.

📜 Step2 : Vocabulary Adaptation

To adapt the tokenizer vocabulary with new terms from cell biology, follow these steps using the vocabulary_adaptation.py script.

  • Ensure you have the following parameters configured in the script before running it:

    • tokenizer_last: The path to the directory containing the pre-existing tokenizer.

    • tokenizer_now: The destination path where the updated tokenizer will be saved.

    • GSE117872_json_file_path: This should be set to the output_json_path variable from the GSE117872_to_json.py script

    • GSE149383_json_file_path: Similarly, this should match the output_json_path variable in the GSE149383_to_json.py script.

    • cell_sentences_hf_path: This path should correspond to the cell_sentences_hf directory, which is specified as the output_dir variable within the transform.py script

  • Once all parameters are configured, execute the script to update the tokenizer's vocabulary with new cell biology terms. Run the following command in your terminal: python vocabulary_adaptation.py.

🛠️ Step3: Train and generate

1. Training

  • Open the finetune.py script. Update the script with the paths for your training and validation JSON files (train_json_path and valid_json_path), the tokenizer location (tokenizer_path), the base model directory (model_path), and the directory where you want to save the fine-tuned model (output_dir).
  • Execute the fine-tuning process by running the following command in your terminal: python finetune.py

2. Generation

  • Single-Instance Inference:
    • To run inference on a single instance, set the necessary parameters in inference_one.py.
    • Execute the script with: python inference_one.py.
  • Web Interface Inference:
    • For interactive web interface inference using Gradio, configure inference_web.py with the required parameters.
    • Launch the web demo by running: python inference_web.py.
  • Batch Inference:
    • For inference on a batch of instances, adjust the parameters in inference_batch.py as needed.
    • Start the batch inference process with: python inference_batch.py.

⌨️ Step4: Translating sentences into gene expressions

For the pseudo-cell generation task, we also translate sentences into gene expressions, including data extraction and transformation stages.

  • Data Extraction:

    • Open extract_gene_generation.py. Set up the necessary parameters for generating cells based on cell type. This step is intended for training datasets larger than 500 samples, covering 16 cell types.
    • Run the following command in your terminal to start the data extraction process: python extract_gene_generation.py.

  • Transformation Process:

    • After generating the necessary files, proceed by configuring sentence_to_expression.py with the appropriate parameters for the translation process.
    • Execute the transformation script with the command: python sentence_to_expression.py.

🚀 Evaluation

To evaluate the performance of various tasks, follow these steps:

  • Change to the evaluation directory with the command: cd evaluation.

  • Random Cell Generation Task:

    • Open Performance_of_random_cell_generation.py.
    • Specify the json_path to the JSON file with the generated data.
    • Specify the global_path to the global gene vocabulary file, usually located in the cell_sentences subdirectory within output_dir specified by the transform.py script, and is named vocab_human.txt.
    • Run the command: python Performance_of_random_cell_generation.py.

  • Pseudo-cell Generation Task:

    • Depending on the format of your data, open python Performance_of_pseudo-cell_generation_lev.py, or python Performance_of_pseudo-cell_generation_expr.py.
    • Specify the my_data_path to the file with the generated pseudo-cell data.
    • Specify the ground_truth_data_path to the file with the ground truth data.
    • Specify the k to the K-value for KNN analysis.
    • Depending on the format of your data, run: python Performance_of_pseudo-cell_generation_lev.py, or python Performance_of_pseudo-cell_generation_expr.py.

  • Cell Type Annotation and Drug Sensitivity Prediction Tasks:

    • Open python performance_of_classification.py.
    • Specify the my_data_path to the JSON file containing the generated data for the task.
    • Run the command: python performance_of_classification.py.

🧬 Single-cell Analysis Tasks

ChatCell can handle the following single-cell tasks:

  • Random Cell Sentence Generation. Random cell sentence generation challenges the model to create cell sentences devoid of predefined biological conditions or constraints. This task aims to evaluate the model's ability to generate valid and contextually appropriate cell sentences, potentially simulating natural variations in cellular behavior.

  • Pseudo-cell Generation. Pseudo-cell generation focuses on generating gene sequences tailored to specific cell type labels. This task is vital for unraveling gene expression and regulation across different cell types, offering insights for medical research and disease studies, particularly in the context of diseased cell types.

  • Cell Type Annotation. For cell type annotation, the model is tasked with precisely classifying cells into their respective types based on gene expression patterns encapsulated in cell sentences. This task is fundamental for understanding cellular functions and interactions within tissues and organs, playing a crucial role in developmental biology and regenerative medicine.

  • Drug Sensitivity Prediction. The drug sensitivity prediction task aims to predict the response of different cells to various drugs. It is pivotal in designing effective, personalized treatment plans and contributes significantly to drug development, especially in optimizing drug efficacy and safety.

📝 Cite

@article{fang2024chatcell,
  title={ChatCell: Facilitating Single-Cell Analysis with Natural Language},
  author={Fang, Yin and Liu, Kangwei and Zhang, Ningyu and Deng, Xinle and Yang, Penghui and Chen, Zhuo and Tang, Xiangru and Gerstein, Mark and Fan, Xiaohui and Chen, Huajun},
  year={2024},
}

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