CITE: Xu, J., Guo, P., Hao, S. et al. A spatiotemporal atlas of mouse liver homeostasis and regeneration. Nat Genet (2024). https://doi.org/10.1038/s41588-024-01709-7
Database: https://db.cngb.org/stomics/lista/
Step 1: Run 00.Stereo_seq_Matrix2SeuratObject-pipeline.R to create Seurat objects. You can run SAW analysis pipeline (https://github.com/STOmics/SAW) to create matrix or download the processed matrix from our database: https://db.cngb.org/stomics/lista/download/
Step 2: Run 00.cut_zonation_layer_and_pathway_module_score.R to split spots into 9 zonation layers. The pathway module score can be add simutaneously.
Step 3: Run 00.Ligand_receptor_interaction_zonation_analysis.R to calculate interaction score of ligand & receptor pairs. The ligand receptor pairs used in our study was provided in the mouse_lr_pair.txt file. You can investigate all of them or a part.
Step 4: Run 00.Find_zonation_pathway_phyper.test.R to detect pathways enriched with zonation genes.
Step 5: Run 00.run_RCTD.R to calculate cell type projection score of scRNAseq on Stereo-seq data. You can find a detailed tutorial from RCTD offtial website: https://github.com/dmcable/spacexr
Step 6: Run 00.run_scenic.py to calculate gene regulatory network. You can find a detailed tutorial from SCENIC offtial website: https://pyscenic.readthedocs.io/en/latest/
Step 7: Run 00.run_hotspot.py to calculate gene coexpression modules base on their expression pattern. You can find a detailed tutorial from Hotspot offtial website: https://yoseflab.github.io/Hotspot/
Step 8: Run 01.TBL1XR1.bulkRNAseq.preprocessing.sh to get raw bulk RNAseq matrix of TBL1XR1 purturbation data.
Step 9: Run 01.TBL1XR1.bulkRNAseq.DEseq.r to get differential expressed genes of TBL1XR1 purturbation data.
Step 10: Run 02.ATAC_chipseq_preprocessing.sh to get chromatin modification regions of ATAC or Chip-seq data.
Step 11: Run 02.ATAC_chipseq_Motif_scan.r to get putative binding motifs of specific chromatin modification regions.
Step 12: Run 03.scRNAseq_clustering_scanpy.py to cluster scRNAseq datasets.