1Job name(optional)
2Spatial Expression MatrixExample
H5ad format file containing gene expression matrix, spatial location information and tissue image information.
3Spatial position information
Example
File containing barcode name and spatial location.
If the spatial information is contained in the obs object of the h5ad file, enter the property name of the information in the form of "spot pixel row coordinates &spot pixel column coordinates", for example "x&y"
4Cell annotation or clustering information
Example
If the cell annotation information is included in the h5ad file, enter the property name for this information, for example "celltype"
5Species
6Ligand receptor interaction library
stLearn

Introduction

stLearn is designed to comprehensively analyse Spatial Transcriptomics (ST) data to investigate complex biological processes within an undissociated tissue. ST is emerging as the “next generation” of single-cell RNA sequencing because it adds spatial and morphological context to the transcriptional profile of cells in an intact tissue section. However, existing ST analysis methods typically use the captured spatial and/or morphological data as a visualisation tool rather than as informative features for model development. We have developed an analysis method that exploits all three data types: Spatial distance, tissue Morphology, and gene Expression measurements (SME) from ST data. This combinatorial approach allows us to more accurately model underlying tissue biology, and allows researchers to address key questions in three major research areas: cell type identification, cell trajectory reconstruction, and the study of cell-cell interactions within an undissociated tissue sample.

Run demo

When running the demo data, use the sample file "Rice_ZH11_ST_p9_part_min.h5ad" for the spatial representation matrix; The spatial location information is "x&y"; Cell comment or clustering information is "celltype"; Species information: Oryza_sativa_L.japonica_cv.Zhonghua_11; The ligand receptor interaction library was ZhonghuaDB.

Result

The resulting files may include:

1. cluster_plot.pdf: The cell type information generated using Seurat;

2. Ligand_Receptor_result.h5ad: The Ligand-Receptor Analysis result saved as h5ad file;

3. Ranking_of_LRs_500.pdf: Visualise the overall ranking of LRs by top-500 significant spots;

4. Ranking_of_LRs_50.pdf: Visualise the overall ranking of LRs by top-50 significant spots;

5. all_spots_Binary_LR_coexpression.pdf: Binary LR coexpression plot for all spots;

6. significant_spots_Binary_LR_coexpression .pdf: Binary LR coexpression plot for significant spots;

7. all_spots_Continuous_LR_coexpression.pdf: Continuous LR coexpression for all spots;

8. significant_spots_Continuous_LR_ coexpression.pdf: Continuous LR coexpression for significant spots;

9. cell_type_rank.pdf: Diagnostic plot to check interaction and cell type frequency correlation;

10. LR_CCI_Map.pdf: Visualise individual celltype-celltype interactions across multiple LR pairs;

11. lrs_LR_CCI_Map.pdf: Visualise individual celltype-celltype interactions across significant LR pairs;

12. cell_type_interact.pdf: Spatial cell type interactions plot with simple black arrows;

13. mean_LR_cell_type_interact.pdf: Spatial cell type interactions spot by the mean LR expression in the spots connected by arrow