mermake 0.0.65

Code to process merfish data

Introduction

MERMAKE processes MERFISH and smFISH imaging data by detecting local intensity maxima (puncta) in 3D image stacks. In multiplexed experiments (MERFISH), these puncta are decoded using a user-supplied codebook, while in smFISH mode, the puncta are simply called and reported as-is.

To install MERMAKE,

python3 -m pip install mermake

❗The newest version of mermake (>= 0.0.61) does drift but requires the nightly release of cupy available via:

pip install --pre cupy-cuda12x==14.0.0a1 -f https://pip.cupy.dev/pre

⚠️ GPU Requirements & CUDA Toolkit

MERMAKE relies on CuPy for GPU-accelerated image processing. To run MERMAKE successfully, you must have:

1. An NVIDIA GPU with CUDA support
2. The CUDA Toolkit installed (Refer to the official CuPy installation guide)

MERMAKE Usage

To run MERMAKE, you'll need to provide a configuration TOML file with a few key settings describing your experiment.

mermake my_settings.toml

If mermake is run without providing a toml file, it will warn about the usage and print out the toml file format. Most users only need to edit the [paths] section.

TOML Section	Variable Name	Description
[paths]	codebook	Path to the CSV codebook for decoding barcodes (for MERFISH data only)
[paths]	psf_file	Path to the PSF file used for deconvolution (e.g., .npy or .pkl)
[paths]	flat_field_tag	Prefix path for flat field correction files (e.g., "Scope3_")
[paths]	hyb_range	Range of hybridization rounds to process (e.g., 'H1_*_set1:H1_*_set3')
[paths]	hyb_folders	List of folders containing raw imaging data
[paths]	output_folder	Path to the folder where MERMAKE should save results

All other sections ([hybs], [dapi], etc.) are preconfigured for most use cases and usually do not need to be changed. Though to use multi-psfs you will want to set the tilesize to the size of the samping grid (ie 300).

---

Example Config (config.toml)

[paths]
codebook = "codebook.csv"
psf_file = "psfs/psf_scope3.npy"
flat_field_tag = "flat_field/Scope3_"
hyb_range = "H1_*_set1:H16_*_set3"
hyb_folders = ["experiment_folder"]
output_folder = "output"

#---------------------------------------------------------------------------------------#
#---------------------------------------------------------------------------------------#
#           you probably dont have to change any of the settings below                  #
#---------------------------------------------------------------------------------------#
#---------------------------------------------------------------------------------------#

hyb_save =  '{fov}--{tag}--col{icol}__Xhfits.npz'
dapi_save = '{fov}--{tag}--dapiFeatures.npz'
drift_save = 'drift_{fov}--_set{iset}.pkl'
regex = '''([A-z]+)(\d+)_([^_]+)_set(\d+)(.*)''' #use triple quotes to avoid double escape

[hybs]
tile_size = 500
overlap = 89
beta = 0.0001
threshold = 3600
blur_radius = 30
delta = 1
delta_fit = 3
sigmaZ = 1
sigmaXY = 1.5

[dapi]
tile_size = 500
overlap = 89
beta = 0.01
threshold = 3.0
blur_radius = 50
delta = 5
delta_fit = 5
sigmaZ = 1
sigmaXY = 1.5