https://github.com/bencardoen/smlmtools.jl
Computational tools for single molecule localization / superresolution microscopy (point clouds).
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Computational tools for single molecule localization / superresolution microscopy (point clouds).
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README.md
SmlmTools
A set of tools for processing point cloud based superresolution/single molecule localization microscopy, including but not limited to - point cloud to image conversion - fiducial detection/tracking - cross-channel alignment - temporal drift correction
While microscopes often have built-in or software solutions to do this for you, they may not be perfect or you may no longer have access to the microscope. You can use this software when: - you have a point cloud dataset - no access to microscope or raw data - you're not happy with the current correction - correction is in 2D, not 3D - ...
Table of Contents
3.Usage
Installation
bash
git clone https://github.com/bencardoen/SmlmTools.jl.git
cd SmlmTools.jl
julia --project=.
or adding as a package
bash
julia -e 'using Pkg; Pkg.add(url="https://github.com/bencardoen/SmlmTools.jl.git")'
Algorithm
You can review the documentation of the code, but in short this is in plain English what will happen: - Load the dataset (3D point clouds) for both channels - Find up to k fiducials (default 2) per channel - Use the fact that fiducials in SMLM continuously emit, so find peaks in the spatial density distribution (those would show up as 'bright points') - Pair the fiducials across channels - If the distance between the closest pair is > than a threshold (default 400nm), increase nr of fiducials - If distance < threshold, pick this pair (brightest nearest pair) - For each channel - Look at the mean location of the fiducial over time - Use this offset over time to correct all points in this channel - The trajectory will be plotted for you so you can inspect it - Once corrected across time - Find the linear translation (offset) between the time-corrected fiducials, and use this align the channels
To allow you to inspect each stage, plots are saved with before/after data and for example the temporal trajectory in XYZ of the beads.
First, the bead (in point cloud) is detected and plotted. Note that it is not circular due to temporal drift, and they are not aligned across channels.
Then we plot the corrected bead
And the trajectory the beads take over time
Usage
2-channel alignment
Let F and S be the file names of the GSD bin files of either channel:
bash
julia --project=. scripts/align.jl --f [F] --s [S] --outdir [mydirectory] --colocalize --align --segment
Adding --colocalize runs colocalization metrics.
Point cloud to image
If you have a 3D point cloud in coords3d (Nx3), project image using nmperpx.
julia
image, smoothed, density, beadmask = project_image(coords3d, nm_per_px; mx=nothing, remove_bead=false, log_scale=true, nm=10)
Using on cluster
- Log in
#### Get resources
This example runs on Compute Canada
bash # Change to local scratch directory cd scratch # Copy the singularity image cp /project/rrg-hamarneh/data/nabi-robert-ivan/software/smlmtools/SmlmTools.sif . # Set executable chmod u+x SmlmToools.sif # Request resources to run from SLURM salloc --mem=32GB --account=rrg-hamarneh --cpus-per-task=4 --time=3:00:00#### Runbash # Make sure Singularity has rw access export SINGULARITY_BINDPATH="/scratch/$USER,$SLURM_TMPDIR" # Execute ./SmlmTools.sif -f testdata/1C8PTRF_3_1_Cav_647.bin -s testdata/1C8PTRF_3_1_PTRF_568.bin -t GSD -p 10 -a -n 10 --outdir . -cThis will produce colocalization for 7 metrics, 2D image projections and 3D alignment.
Cite
If you find this useful, consider citing
bibtext
@software{cardoen_ben_2023_7632321,
author = {Cardoen, Ben},
title = {{SmlmTools: A Julia package for computational
methods for single molecule localization /
superresolution microscopy}},
month = feb,
year = 2023,
note = {https://github.com/bencardoen/SmlmTools.jl},
publisher = {Zenodo},
version = {0.1},
doi = {10.5281/zenodo.7632321},
url = {https://doi.org/10.5281/zenodo.7632321}
}
See also
- Colocalization
- DataCurator This package allows you to run the above without any code, as a part of your pre or postprocessing workflows
Troubleshooting
- The code tries to find beads by looking at maximum emission density, because beads blink continuously. However, if you ask it to look for up to 3 beads, and there are no beads, invariably it'll pick bright parts of the cell.
- If beads are beyond the maximum distance (300nm center to center), the code will refuse to continue. You can override it, but be careful.
Owner
- Name: Ben Cardoen
- Login: bencardoen
- Kind: user
- Location: Vancouver
- Company: https://github.com/sfu-mial
- Twitter: BenCardoen
- Repositories: 29
- Profile: https://github.com/bencardoen
PhD Student Computing Science @sfu-mial Simon Fraser University
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