SPIMquant is a Snakebids app for quantitative analysis of SPIM (lightsheet) brain data. It performs automated nonlinear template registration and quantification of pathology from SPIM microscopy datasets.
Features include:
- Deformable registration to a template
- Atlas-based quantification of pathology
- Coarse-grained and fine-grained parallelization using Snakemake and Dask
- Support for reading BIDS datasets directly from cloud-based object storage
- Support for simple and scalable cloud-based processing with Coiled
- Processing lightsheet microscopy data is computationally-demanding, and you will need sufficient (and ideally fast and local) disk space. The more cores you have access to, the faster the code will run, but you will also need sufficient memory (e.g. 2-4 GB per core) as well.
- A Linux machine with Singularity or Apptainer installed, and a recent version of Python (>= 3.10). The workflow will download any containers it requires to run non-python dependencies (c3d, greedy, ANTS).
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Install the package directly from github:
pip install -e git+https://github.com/khanlab/spimquant#egg=spimquant
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Or if you are going to make changes to the code, you should clone the repository then install it:
git clone https://github.com/khanlab/SPIMquant.git pip install ./SPIMquant
SPIMquant is a BIDS App, so you need a BIDS dataset containing SPIM (or lightsheet microscopy) data to use it. The SPIMprep workflow is the recommended tool to produce a BIDS dataset from your raw or minimally-preprocessed microscopy data.
- Perform a dry run:
spimquant /path/to/bids/dir /path/to/output/dir participant -np --use-apptainer
- Run the app using all cores:
spimquant /path/to/bids/dir /path/to/output/dir participant --cores all --use-apptainer
We welcome contributions! Please refer to the contributing guidelines for more details on how to contribute.
This project is licensed under the MIT License. See the LICENSE file for more details.