SpaTrio is a computational tool based on optimal transport that can align single-cell multi-omics data in space while preserving the spatial topology of the tissue section and local geometry of modality

This toolkit is written in both R and Python programming languages. The core optimal transport algorithm is implemented in Python, while the initial data preparation and downstream multimodal analysis are written in R.

# We recommend using Anaconda, and then you can create a new environment.
# Create and activate Python environment
conda create -n spatrio python=3.8
conda activate spatrio

# Install requirements
cd SpaTrio-main
pip install -r requirements.txt

# Install spatrio
python setup.py build
python setup.py install

install.packages("doParallel")
BiocManager::install("ConsensusClusterPlus")

# Install SpaTrio package from local file
install.packages("SpaTrio_1.0.0.tar.gz", repos = NULL, type = "source")

To use SpaTrio we require formatted .csv files as input (i.e. read in by pandas).

• multi_rna.csv/spatial_rna.csv (The gene expression matrix of cells/spots)

• multi_meta.csv/spatial_meta.csv (The meta information matrix of cells/spots)

• emb.csv (The low-dimensional embedding matrix of cells)

• pos.csv (The spatial location matrix of spots)

At the same time, we also support additional specifications of the number of cells in each spot.

• expected_num.csv (The number of cells contained in each spot)

In some examples of simulated data, the number of cell types in the spot is given (ref_counts.csv). These data will be converted to expected_num for use.

• ref_counts.csv (The number of celltypes contained in each spot)

We have included two test datasets (demo1 & demo2) in the tutorial/data/ of this repository as examples to show how to use SpaTrio to align cells to space.

Simulated data in the stripe pattern:

• Demonstration of SpaTrio on demo1 dataset

Simulated data in the ring pattern:

• Demonstration of SpaTrio on demo2 dataset

More importantly, we support directly calling the core functions written in Python from the R language to facilitate downstream analysis.

DBiT-seq mouse embryo datasets (Google Drive):

• Demonstration of SpaTrio on DBiT-seq mouse embryo datasets

10x Visium+ADT mouse liver datasets (Google Drive):

• Demonstration of SpaTrio on mouse liver datasets

We have applied SpaTrio on different tissues of multiple species, here we give step-by-step tutorials for all application scenarios. And preprocessed datasets used can be downloaded from Google Drive.

• Using SpaTrio to reconstruct and analyze single-cell multi-modal data of mouse cerebral cortex

• Using SpaTrio to reconstruct and analyze single-cell multi-modal data of human steatosis liver

• Using SpaTrio to reconstruct and analyze single-cell multi-modal data of human breast cancer

Should you have any questions, please feel free to contact the author of the manuscript, Mr. Penghui Yang ([email protected]).

Penghui Yang, et al. Revealing spatial multimodal heterogeneity in tissues with SpaTrio, Cell Genomics, 2023, https://doi.org/10.1016/j.xgen.2023.100446.