We have previously developed huARdb currently in developing which collects single-cells immune profiling datasets including linked transcriptome and full-length TCR informations. However, one of the main obstacles in using single-cell immune profiling datasets for disease immunotherapy is the absence of a convenient reference atlas to access this information. Despite the growing potential of TCR engineering in this area, there is a significant challenge in identifying functional TCRs due to the lack of efficient computational tools to integrate the data. Nonetheless, these datasets offer a valuable resource to identify such TCRs and further advance TCR-T technology.
We aims to build the most comprehensive atlas containing matched transcriptome and full-length V(D)J sequence of T and B cells in Cancer, Autoimmune diseases, and Infections.
- In 2023, we make the first release of an integrated datasets containing more than 1,000,000 hcT cells with full length TCR sequence.
- In 2024, we will release the second version of the atlas with more than 20,000,000 hcT cells with full length TCR sequence, which is available at Zenodo.
To robustly identify potential disease associated TCRα/β pairs considering both TCR sequence similarity and transcriptome features from million-level paired TCRα/β repertoire, we developed a deep-learning based framework named TCR-DeepInsight.
The documentation for TCR-DeepInsight is available at ReadTheDocs.
Hardware requirement for TCR-DeepInsight includes
- RAM: >16Gb for larger dataset
- VRAM of CUDA-enabled GPU: >8Gb
pip install tcr-deep-insightYou can create a running environment using conda (Anaconda or Miniconda)
conda create -n tcr-deep-insight -f environment.yml
conda activate tcr-deep-insight
git clone [email protected]:WanluLiuLab/TCR-DeepInsight.git
cd TCR-DeepInsightPlease see the PyTorch official website for installing GPU-enabled version of PyTorch.
# Testing if CUDA is available
import torch
print(torch.__version__)
print(torch.cuda.is_available())TCR-DeepInsight was tested on PyTorch 1.13.1 and CUDA 11.7
In IPython, simply import the package to get started:
import tcr_deep_insight as tdi For detailed usage, Please see the Documentation for TCR-DeepInsight.
This project is licensed under the BSD 3-Clause License.