This package supports the patchseq autotrace pipeline to generate automated neuron morphological reconstructions.
conda create -n autotrace_env python=3.9
conda activate autotrace_env
git clone https://github.com/AllenInstitute/patchseq_autotrace.git
cd patchseq_autotrace
pip install -r requirements.txt
pip install .
In order to download image stacks from the internal LIMS system, you must set certain OS environment variables to connect properly. These include
LIMS_HOST
LIMS_DBNAME
LIMS_USER
LIMS_PASSWORD
To set these either open a terminal and run the following commands or add the commands to your .bashrc file.
export LIMS_HOST=yourdbhostname
Contact the technology team if you need to get credential details to access LIMS.
After installation the following console scripts will be available to run from the command line of your environment. To see detailed instructions on each script type the name of the SCRIPT_NAME --help
script to submit a batch of cells to run on the allen hpc. Creates a DAG workflow composed of the following scripts
script to prepare an already existing image stack, or obtain from LIMS and prepare an image stack for segmentation
script to segment the resulting images from auto-pre-proc
script to post-process and skeletonize the segmented image stack generated by auto-segmentation
script to convert the skeletonized image generated in auto-post-proc to an swc file
script is run at the end of the pipeline (after auto-skeleton-to-swc), or if any of the intermediate steps fail to remove large files from disk.
This code is an important part of the internal Allen Institute code base and we are actively using and maintaining it. Issues are encouraged, but because this tool is so central to our mission pull requests might not be accepted if they conflict with our existing plans.
