https://github.com/curtlab/2calm

CALM (2-sample Comparative Analysis of 3D Localisation Microscopy data) is an analysis pipeline, which organizes localisation microscopy data into clusters of different dimensions and calculated the samples’ statistical parameters using various numerical methods.

https://github.com/curtlab/2calm

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analysis clustering machine-learning smlm
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CALM (2-sample Comparative Analysis of 3D Localisation Microscopy data) is an analysis pipeline, which organizes localisation microscopy data into clusters of different dimensions and calculated the samples’ statistical parameters using various numerical methods.

Basic Info
  • Host: GitHub
  • Owner: CURTLab
  • License: mit
  • Language: MATLAB
  • Default Branch: master
  • Homepage:
  • Size: 155 MB
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analysis clustering machine-learning smlm
Created over 6 years ago · Last pushed over 2 years ago
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README.md

2CALM

CALM Version 4.2 (2-sample Comparative Analysis of 3D Localisation Microscopy data) is an analysis pipeline, which organizes localisation microscopy data into clusters of different dimensions and calculated the samples’ statistical parameters using various numerical methods.

CALM Version 4.2 is the prototype software platform that allows a comparative analysis of 3D localisation microscopy data representing e.g. protein distributions in two biological samples. The CALM 4.2 system includes a collection of functions and scripts for handling individual menu items. The system should be unpacked together with the main CALM 4.2 directory and subdirectories (the subdirectories can be freely extended, e.g. for individual experiments).

The system starts in the Matlab (min Version R2018) with the CALM.m command.

In this prototype system, the input data structure is limited to specified matlab format. Data set must be of type structure array named par. The structure must contain the fields: * par.pkmatrix – array of numerical data with row-measurements and columns- data coordinates. * par.pkdesc - contains two cells par.pkdesc.desc, and par.pkdesc.units: * par.pkdesc.desc - gives a description of the pkmatrix-columns line * with at least the following string variables: frame; x; y; z; pa; paz; intensity where pa is the position accuracy of xy localization and paz is position accuracy of localization on z-axis. frame is frame number. * par.pkdesc.units contains a description of the units of measure for each column in string format.

IMPORT DATA FROM EXCEL

In order to enable analysis of data in another format, the system has an interface to Excel that imports and transfers Excel data to the Matlab format described above.
Excel data spreadsheet must be column-oriented and must have at least 3 columns (3D point localizations) with column headers x, y, z. If possible, the following additional columns should contain: * position accuracy (in nm) in xy with header pa * position accuracy (in nm) along the axis z with header paz * intensity of the point with the header intensity * the frame number with the frame header

In the absence of these columns they will be automatically generated with default values typical for this type of nanoscopic samples.

License

Copyright 2018 - 2021 Jaroslaw Jacak, Medical Engineering Dept. Upper Austria University od Applied Sciences, Linz, Austria This code may be freely used and distributed, so long as it maintains this copyright line

Citation

Please cite our paper: Mayr S, Hauser F, Puthukodan S, Axmann M, Göhring J, et al. (2020) Statistical analysis of 3D localisation microscopy images for quantification of membrane protein distributions in a platelet clot model. PLOS Computational Biology 16(6): e1007902. https://doi.org/10.1371/journal.pcbi.1007902

Link to paper: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007902

Owner

  • Login: CURTLab
  • Kind: user
  • Location: Linz, Austria

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