This folder contains the spatial transcriptomics data + code. This code was generated by members of the Smillie Lab @ MGH and Harvard Medical School. github.tar.gz: spatial analysis code and data anndata.h5ad: anndata object (scanpy) V*tar.gz: raw spatial transcriptomics files The github.tar.gz folder contains everything you need to reproduce the spatial transcriptomics figures. It is structured as follows: 1.BayesPrism: code for running BayesPrism on spatial data 2.SparCC: code for running SparCC on spatial data 3.Lasso: code for running lasso regression on spatial data 4.Analysis: code for reproducing all figures in the paper 4.Analysis/1.analysis.r: script to reproduce all figures in the paper *** code: code library containing all necessary functions load_data.r: code to load the single-cell and spatial datasets sco.rds: single-cell analysis object (10X Chromium) formatted as an R list vis.rds: spatial analysis object (10X Visium) formatted as an R list All scripts are numbered. You need to run everything in order. For convenience, we include the output files for 1.BayesPrism, 2.SparCC, and 3.Lasso, allowing you to skip straight to the analysis code in 4.Analysis. To reproduce all figures in the paper, you need to do the following: Edit your PROJECT_FOLDER in the header of load_data.r Install the packages listed at the top of load_data.r Go to the 4.Analysis directory, start an interactive R session, and type:> source('1.analysis.r') This will load the beginning of the 1.analysis.r script (until the stop() statement on line 68). You can run the code in two different ways: You can step through the code line by line in your interactive R session (starting at line 68) Alternatively, remove the stop() statement from the script, then run the code start to finish If you encounter any errors, try to debug them using a combination of Google+ChatGPT. If you still have trouble, please contact the Smillie Lab. Note: the single-cell and spatial code are also available on GitHub. However, the spatial analysis requires large files that cannot be hosted on GitHub. Therefore, it is better to download the code + files from Zenodo. The GitHub link is provided below: https://github.com/LJ-Kong/fibrosis_scRNA_stRNA