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IMPORTANT_FILES.md

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Descriptions of important files:

  • sub_registration.sh or sub_registration_terastitcher.sh: * .sh file to be used to submit to a slurm scheduler * this can change depending on scheduler+cluster but generally batch structure requires 2 variables to pass to run_tracing.py: * stepid = controlling which 'step' to run * jobid = controlling which the jobid (iteration) of each step * Steps: * 0: set up dictionary and save; requires a single job (jobid=0) * 1: process (stitch, resize) zplns, ensure that 1000 > zplns/slurmfactor. typically submit 80 jobs for LBVT (jobid=0-80). * 2: resample and combine; typically submit 3 jobs (requires 1 job/channel; jobid=0-3) * 3: registration via elastix

  • sub_main_tracing.sh: * .sh file to be used to submit to a slurm scheduler * this can change depending on scheduler+cluster but generally batch structure requires 2 variables to pass to run_tracing.py AND cell_detect.py: * stepid = controlling which 'step' to run * jobid = controlling which the jobid (iteration) of each step * Steps: * 0: set up dictionary and save; requires a single job (jobid=0) * 1: process (stitch, resize) zplns, ensure that 1000 > zplns/slurmfactor. typically submit 80 jobs for LBVT (jobid=0-80). * 2: resample and combine; typically submit 3 jobs (requires 1 job/channel; jobid=0-3) * 3: registration via elastix * cnn_preprocess.sh (will add to this)

  • run_tracing.py: * .py file to be used to manage the parallelization to a SLURM cluster * inputdictionary and params need to be changed for each brain * the function directorydeterminer in tools/utils REQUIRES MODIFICATION for both your local machine and cluster. This function handles different paths to the same file server. * generally the process is using a local machine, run step 0 (be sure that files are saved *BEFORE( running this step) to generate a folder where data will be stored * then using the cluster's headnode (in the new folder's lightsheet directory generated from the previous step) submit the batch job: sbatch sub_registration.sh

  • cell_detect.py: * .py file to be used to manage the parallelization of CNN preprocessing and postprocessing to a SLURM cluster * params need to be changed per cohort. * see the tutorial for more info.

  • tools: convert 3D STP stack to 2D representation based on colouring

    • imageprocessing: * preprocessing.py: functions use to preprocess, stitch, 2d cell detect, and save light sheet images
    • analysis: * allen_structure_json_to_pandas.py: simple function used to generate atlas list of structures in coordinate space * other functions useful when comparing multiple brains that have been processed using the pipeline

  • supp_files:

    • gridlines.tif, image used to generate registration visualization
    • allen_id_table.xlsx, list of structures from Allen Brain Atlas used to determine anatomical correspondence of xyz location.

  • parameterfolder:

    • folder consisting of elastix parameter files with prefixes Order<#>_ to specify application order