DOI: 10.5522/04/27044423.v1
title: Spatial transcriptomic files for "An in vivo and in vitro spatiotemporal atlas of human midbrain development"

description: Files required to reproduce analysis from "An in vivo and in vitro spatiotemporal atlas of human midbrain development".<br>Contains:Three docker images: <code>commot_docker.tar</code>; <code>cell2location_docker.tar</code>; <code>midbrain_tissues_organoids_st_docker.tar</code>. Instructions on how to load these are provided below. <code>cell2location_docker.tar</code> is only used for pre-processing. <code>commot_docker.tar</code> and <code>midbrain_tissues_organoids_st_docker.tar</code> are used for the main analysis.<code>mlo_all_st.tar.gz</code> archive containing processed data and the code to reproduce the figures from the paper.<code>cell2location_results_*</code> (one for each sample): cell type deconvolution results used in the main analysis. <code>mlo_resolution075_Annot.rds</code>: annotated single-cell reference, used for cell2location deconvolution.Dockerfiles used to generate each docker image.<br>How to reproduce resultsThe <code>mlo_all_st.tar.gz</code> file can be downloaded and extracted and used to re-run the pipeline that generated the spatial transcriptomic figures. Once the necessary docker images have been downloaded and installed (see below), the pipeline can be run with:<code>tar -xzvf </code><code>mlo_all_st.tar.gz</code><code># From root of extracted directory (i.e. containing the `code` directory)</code><code>docker run \</code><code> --rm \</code><code> --volume /var/run/docker.sock:/var/run/docker.sock \</code><code> --volume "$(pwd):/tmp/in_mnt" \</code><code> --workdir /tmp/in_mnt/code \</code><code> --env HOST_MOUNT_PATH="$(pwd)" \</code><code> --oom-score-adj -1000 \</code><code> midbrain_tissues_organoids_docker \</code><code> R -e 'rmarkdown::render("mlo_analysis_code.Rmd")'</code><br><br>The above code should exactly reproduce the analysis in the publication. If it does not, please let us know.Note that <code>mlo_all_st.tar.gz </code>already contains generated figures and data. These are included so that the target is obvious to the user: re-running the pipeline will replace these files with regenerated versions.<br>Docker images<br>We used Docker to ensure reproducibility. The three images used to run the analysis can be loaded into docker with:<code>docker load &lt; IMAGE_NAME</code>The <code>cell2location</code> and <code>COMMOT</code> images were generated using the installation instructions of the packages they contain. The `midbrain_tissues_organoids_st_docker` is more complex since it contains all other software necessary to run the rest of the analysis. It is based on the docker image generated by the `Seurat` developers, with additional R packages installed. The versions of these R packages are stored in `renv.lock`, generated by the `renv` package. The Dockerfiles for each image are in this repository.<br>Running <code>Cell2Location</code><br>Before the main analysis pipeline, we run <code>cell2location</code> to estimate the cell type proportions on each spot of the Visium slides. We do so with<br><code>sh run_entire_c2l_pipeline.sh</code><br>You probably don't want to re-run this step since it is time-consuming.<br>