Bulk2Space is a two-step spatial deconvolution method based on deep learning frameworks, which converts bulk transcriptomes into spatially resolved single-cell expression profiles.

For Bulk2Space, the python version need is over 3.8. If you have installed Python3.6 or Python3.7, consider installing Anaconda, and then you can create a new environment.

conda create -n bulk2space python=3.8
conda activate bulk2space

The version of pytorch should be suitable to the CUDA version of your machine. You can find the appropriate version on the PyTorch website. Here is an example with CUDA11.6:

pip install torch --extra-index-url https://download.pytorch.org/whl/cu116

cd bulk2space-main
pip install -r requirements.txt

python setup.py build
python setup.py install

To use Bulk2Space we require five formatted .csv files as input (i.e. read in by pandas). We have included two test datasets in the tutorial/data/example_data folder of this repository as examples to show how to use Bulk2Space.

If you choose the spot-based data (10x Genomics, ST, or Slide-seq, etc) as spatial reference, please refer to:

• Demonstration of Bulk2Space on demo1 dataset

If you choose the image-based data (MERFISH, SeqFISH, or STARmap, etc) as spatial reference, please refer to:

• Demonstration of Bulk2Space on demo2 dataset

For more details about the format of input and the description of parameters, see the Tutorial Handbook.

Additional step-by-step tutorials now available! Preprocessed datasets used can be downloaded from Google Drive (PDAC) and Google Drive (hypothalamus).

• Integrating spatial gene expression and histomorphology in pancreatic ductal adenocarcinoma (PDAC)

• Spatially resolved analysis of mouse hypothalamus by Bulk2Space

Should you have any questions, please feel free to contact the co-first authors of the manuscript, Dr. Jie Liao ([email protected]), Mr. Jingyang Qian ([email protected]), Miss Yin Fang ([email protected]), Mr. Zhuo Chen ([email protected]), or Mr. Xiang Zhuang ([email protected]).

Liao, J., Qian, J., Fang, Y. et al. De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution. Nat Commun 13, 6498 (2022). https://doi.org/10.1038/s41467-022-34271-z