STdGCN: spatial transcriptomic cell-type deconvolution using graph convolutional networks

Spatial Transcriptomics deconvolution using Graph Convolutional Networks (STdGCN) is a graph-based deep learning framework that leverages cell type profiles learned from single-cell data to deconvolve the cell type mixtures of spatial transcriptomics data. The manuscript of this software is now available on biorXiv (https://www.biorxiv.org/content/10.1101/2023.03.10.532112v1) [1].

Requirements

torch == 1.11.0
numpy == 1.21.6
pandas == 1.3.5
scanpy == 1.9.1
matplotlib == 3.5.1
scipy == 1.7.3
tqdm == 4.64.0
sklearn == 1.0.2
scanorama == 1.7.2
random
pickle
time
math
copy

The example dataset

The included spatial transcriptomic (ST) dataset is from Zhu et al. [2], which includes a seqFISH+ slice from the sub-ventricular zone (SVZ) of a mouse somatosensory (SS) region. The resolution of this dataset is single cell-level. We resampled the cells into multiple square pixel areas. Cells within each square pixel area were merged into a synthetic spot. We chose the 200 × 200 square pixel area for resampling. Spots with cells less than two were discarded. The raw single-cell ST data was also used as the single cell reference. All example data files are stored in “./data”

Run STdGCN

A complete guide for running cell type deconvolution using STdGCN can be found in “Toturial.ipynb” and “Toturial.py”, including the detailed introductions and annotations of using STdGCN.

Input files

• sc_data.tsv: The expression matrix of the single cell reference data with cells as rows and genes as columns.
• sc_label.tsv: The cell-type annotation of sincle cell data. The table should have two columns: The cell barcode/name and the cell-type annotation information.
• ST_data.tsv: The expression matrix of the spatial transcriptomics data with spots as rows and genes as columns.
• coordinates.csv: The coordinates of the spatial transcriptomics data. The table should have three columns: Spot barcode/name, X axis (column name 'x'), and Y axis (column name 'y').
• marker_genes.tsv [optional]: The gene list used to run STdGCN. Each row is a gene and no table header are permitted.
• ST_ground_truth.tsv [optional]: The ground truth of ST data. The data should be transformed into the cell type proportions.

Output files

• pseudo_ST.pkl: The pseudo-spots information.
• marker_genes.tsv: Selected cell type marker genes for training.
• model_parameters: The saved deep learning parameters for STdGCN.
• Loss_function.jpg: The curves to display the loss changes of training, validating, and test (if ST_ground_truth.tsv is provided) datasets.
• predict_result.csv: The predicted cell type proportions for the ST data.
• results.h5ad: The predicted cell type proportions for the ST data.
• predict_results_pie_plot.jpg: The pie plot visualization of the predicted ST data.
• [cell type name].jpg: The scatter plots show the predicted proportions of each cell type in the ST map.

References

[1] Li, Y, Luo, Y. STdGCN: accurate cell-type deconvolution using graph convolutional networks in spatial transcriptomic data. bioRxiv, 2023.2003.2010.532112 (2023).
[2] Zhu Q, Shah S, Dries R, Cai L, Yuan GC. Identification of spatially associated subpopulations by combining scrnaseq and sequential fluorescence in situ hybridization data. Nat Biotechnol 2018.