SEMORE

SEmi-automatic MOrphological fingerprinting and segmentation Extraction

A multi-independent-module pipeline for structure segmentation and disection in single molecule localization microscopy (SMLM) data and the extraction of unique morphological fingerprints.

Citing

TBA

Dependencies

• python==3.8
• pandas==1.5.3
• matplotlib==3.7.1
• scipy==1.10.1
• hdbscan==0.8.29
• opencv==4.6.0
• scikit-learn==1.2.2
• umap-learn==0.5.3

Usage

Installation

SEMORE's installation guide utilize conda environment setup, therefore either miniconda or anaconda is required to follow the bellow installation guide.

• Anaconda install guide: here
• Mini conda install guide: here

SEMORE is most easily setup in a new conda environment with dependecies and channels found in dependency.yml - Open Terminal / Commando promt at whished location of SEMORE and run the bash commands below, which creates the environemnt, downloades and installs packages, takes 9m 35s. Run time for regular csv files of <50MB is expected below 10 minutes.

git clone https://github.com/hatzakislab/SEMORE
cd SEMORE
conda env create -f dependency.yml
conda activate SEMORE

SEMORE modules and additional/helpful functions are contained in the Scripts folder. SEMORE modules are imported as:

from Scripts.SEMORE_clustering import find_clust
from Scripts.SEMORE_fingerprint import Morphology_fingerprint

Three test python scripts are provided:

• Data_sim_test.py - test data generation.
• Segmentation_test.py - test the clustering module on simulated data.
• Fingerprint_test.py - test the fingerprint modules on the resulting data from Segmentation_test.py.

Own data

SEMORE_clustering.find_clust accepts 2-D localizations containing a temporal element [x,y,t] while SEMORE_fingerprint.Morphology_fingerprint accepts localizations [x,y] both static and temporal resolved. The output of the fingerprintg can then freely be used for further analysis.

For demostration

For demostration regarding presented data contained in the manuscript, please refer to the _For_puplicaiton folder where you will find the required information and scripts.

Contact

https://www.hatzakislab.com/

Nikos S.Hatzakis, Professor
Department of Chemistry
hatzkais@chem.ku.dk

Jacob Kæstel-hansen, PhD fellow
Department of Chemistry
jkh@chem.ku.dk

Steen W. B. Bender, Master student
Department of Chemistry
csq439@alumni.ku.dk