SPIMprep is a Snakemake+SnakeBIDS workflow for pre-processing single plane illumination microscopy (SPIM, aka lightsheet microscopy). It takes TIF images (tiled or prestitched) and outputs a validated BIDS Microscopy dataset, with a multi-channel multi-scale OME-Zarr file for each scan, along with downsampled nifti images.

Supported inputs:

SPIMprep supports a range of inputs, with the type of acquisition specified by including the short-hand name as a substring in the acquisition tag:

• blaze: Raw Ultramicroscope Blaze OME TIFF files, either as 2D or 3D TIFF files
• prestitched: Prestitched images, as a stack of 2D TIF files (e.g. from LifeCanvas)
• imaris: Prestitched into a single Imaris (.ims) file

Installation

SPIMprep requires the Pixi package manager. After installing Pixi, clone the repository and install dependencies:

git clone https://github.com/khanlab/spimprep SPIMprep
cd SPIMprep
pixi install
pixi shell

Usage

Run the workflow using the run.py script:

./run.py --input-path /path/to/data/ --subject M4A1Te3 --stains Abeta PI Lectin --output-bids-dir /path/to/output --work-dir /tmp --cores all --use-conda

The --input-path should be a folder with a sample’s tif files, or a tar file containing the tif files. The acquisition value must contain one of blaze, imaris or prestitched to define which workflow will be used.

Full Documentation: here

This package allows you to build BIDS Apps using Snakemake. It offers:

* Flexible data grabbing with PyBIDS, configurable solely by config file entries
* Helper function for creating BIDS paths inside Snakemake workflows/rules
* Command-line invocation of snakemake workflows with BIDS App compliance
* Configurable argument parsing specified using the Snakemake workflow config
* Execution either as command-line BIDS apps or via snakemake executable

Full Documentation: here

Relevant papers:

• Mölder F, Jablonski KP, Letcher B et al. Sustainable data analysis with Snakemake [version 2; peer review: 2 approved]. F1000Research. 2021. doi: 10.12688/f1000research.29032.2

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

• 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

Hardware Requirements

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.

Software Requirements

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).

Installation

Install the package directly from GitHub:

pip install -e git+https://github.com/khanlab/spimquant#egg=spimquant

Or if you are going to make changes to the code, clone the repository then install it:

git clone https://github.com/khanlab/SPIMquant.git
pip install ./SPIMquant

Usage

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-conda

Run the app using all cores:

spimquant /path/to/bids/dir /path/to/output/dir participant --cores all --use-conda

If your input BIDS dataset stores data in zarr zipstores (e.g. SPIM files ending in *_SPIM.ome.zarr.zip), then you should use the following option:

--filter-spim extension='ome.zarr.zip'

Full Documentation: here

ZarrNii is a Python library for working with OME-Zarr and NIfTI formats. ZarrNii bridges the gap between these two popular formats, enabling seamless data transformation, metadata preservation, and efficient processing. The motivating application is for whole brain lightsheet microscopy and ultra-high field MRI, but it can generally be used for any 3T+channel datasets.

Key Features

• Seamless Format Conversion: Easily convert between OME-Zarr and NIfTI while preserving spatial metadata
• Transformations: Apply common operations like affine transformations, downsampling, and upsampling
• Multiscale Support: Work with multiscale OME-Zarr pyramids
• Metadata Handling: Access and modify OME-Zarr metadata like axes and transformations
• Lazy Loading: Leverage Dask arrays for efficient processing of large datasets

Installation

Install zarrnii using pip:

pip install zarrnii

Or install the latest development version from GitHub:

pip install git+https://github.com/khanlab/zarrnii.git

Quick Start

from zarrnii import ZarrNii

# Load an OME-Zarr dataset
znimg = ZarrNii.from_ome_zarr("path/to/zarr_dataset.ome.zarr")

# Perform a transformation (e.g., downsample)
downsampled_znimg = znimg.downsample(level=2)

# Save as NIfTI
downsampled_znimg.to_nifti("output_dataset.nii")

Use Cases

ZarrNii allows you to:

• Read and write OME-Zarr and NIfTI datasets
• Perform transformations like cropping, downsampling, and interpolation
• Preserve and manipulate metadata from OME-Zarr (e.g., axes, coordinate transformations, OME annotations)

Full Documentation: here