Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses

Raw data and code to reproduce figures in the manuscript "Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses" # README ## Introduction This README provides essential information about the codebase for the manuscript titled "Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses." The code in this repository is self-contained and is expected to run smoothly given the appropriate versions of the required libraries/packages. ## Directory structure and execution details ### R code - Main Figures 2A-2D, 3A-3C, and 4A-4E, as well as supplemental figures S2A-S2H, S3A-S3E, S4L, and S5A-S5I, were generated using R. Execute the `R_figs_master.r` script located in the `r_code` directory. - All figures will be saved within the `r_code/code_generated_figures` directory. - Note: Exact UMAP representations might vary across different hardware and operating systems, likely due to an issue with the UWOT package ([Reference Issue](https://github.com/satijalab/seurat/issues/5514)). If figures appear outside their designated plot ranges, set "FixAxes" to 'FALSE' in the `single_cell_variables.r` script. ### MATLAB code - Main figures 1B, 1D-1F, and 6A-6H, as well as supplemental figures S1A-S1J and S6A-S6I, were generated using MATLAB (version 9.11.0.1809720 (R2021b) Update 1). Execute the `get_the_figs_matlab.m` script located in the `matlab_code` directory. - All figures will be saved within the `matlab_code/code_generated_figures` directory. - Required: [fca_readfcs, version 2020.06.22](https://ch.mathworks.com/matlabcentral/fileexchange/9608-fca_readfcs). ### Python code - Figures 5B-5F panels were generated using Python (version 3.6.8). Run the `fig_5_analysis_code.py` script located in the `python_code` directory. - All figures will be saved within the `python_code/code_generated_figures` directory. - The preprocessed images located in `python_code/data_repository/Adamts2_processed`, `python_code/data_repository/Agmat_processed`, and `python_code/data_repository/Baz1a_processed` were generated using the ImageJ macro `python_code/cropped_to_processed_macro.ijm` from the raw images in `python_code/data_repository/Adamts2_cropped`, `python_code/data_repository/Agmat_cropped`, and `python_code/data_repository/Baz1a_cropped`. ## Supplementary code (for reference only as raw data is not included) ### Mapping code and genome construction code - Initial processing of Single-cell RNA-sequencing was performed with Cell Ranger, coordinated by the Python script:   `python_code/mapping_and_genome_construction/single_cell_mapping_pipeline.py`. Some components of this script are deprecated and were primarily used to pass .fastq files to Cell Ranger and organize the outputs. - A custom genome was constructed to account for the expression of CaMPARI2 in the single-cell RNA-sequencing dataset:   `python_code/mapping_and_genome_construction/campari2_genome_construction.py`. - Processing of Bulk RNA-sequencing, either single or paired-end, was executed through Python:   `python_code/mapping_and_genome_construction/bulk_single_end_mapping.py` and `python_code/mapping_and_genome_construction/bulk_paired_end_mapping.py`. - A custom genome was constructed to account for the expression of various artificial promoter viruses:   `python_code/mapping_and_genome_construction/bulk_seq_genome_construction.py`.