The sample data for stMLnet

The input of stMLnet includes demo dataset and prior database. please refer to the following paper for more details:

Cheng J, Yan L, Nie Q, Sun X*. Modeling and inference of spatial intercellular communications and multilayer signaling regulations using stMLnet. bioRxiv. 2022.

Files and datails:

• ex_databases: demo database `ex_databases`, which includes three sublists: `LigRec.DB`, `RecTF.DB`, and `TFTG.DB`. Each sublist consists of three columns: `source`, `target`, and `score`. In the aforementioned sublists, the `source` column represents signalling molecules such as ligands, receptors, and transcription factors, while the `target` column represents signalling molecules such as receptors, transcription factors, and target genes. The `score` column in `LigRec.DB` and `TFTG.DB` indicates the frequency of interactions occurring in the collected database, while the `score` column in `RecTF.DB` represents the predicted probabilities of interactions occurring in the collected database.
• ex_inputs: The demo data is derived from the breast cancer dataset of 10X Visiumd, which can be found at [here](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Breast_Cancer_Block_A_Section_1). We analyzed this spatial transcriptomic data using Seurat (Version 4.0.2) and performed deconvolution using the RCTD method. The demo data `ex_inputs`, which includes:

      `exprMat`: Normalized expression matrix.
      `annoMat`: Cell type annotation matrix.
      `locaMat`: Spatial location information matrix.
      `ligs_of_inter`: Potential ligand lists for different cell types.
      `recs_of_inter`: Potential receptor lists for different cell types.
      `tgs_of_inter`: Potential target gene lists for different cell types.