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boeken_tau_simpull

This repository contains the code and data for the Tau SiMPull methods paper, led by Dorothea Böken

https://github.com/dboeken/boeken_tau_simpull

Science Score: 67.0%

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    Found 6 DOI reference(s) in README
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    Links to: zenodo.org
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    Low similarity (15.0%) to scientific vocabulary
Last synced: 7 months ago · JSON representation ·

Repository

This repository contains the code and data for the Tau SiMPull methods paper, led by Dorothea Böken

Basic Info
  • Host: GitHub
  • Owner: dboeken
  • License: mit
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 375 KB
Statistics
  • Stars: 2
  • Watchers: 1
  • Forks: 0
  • Open Issues: 1
  • Releases: 1
Created about 3 years ago · Last pushed about 2 years ago
Metadata Files
Readme License Citation

README.md

DOI

DOI

BOEKEN Tau SiMPull 2023

This repository contains the analysis code associated with the Tau SiMPull project, led by Dorothea Böken. This manuscript has been submitted for publication under the title "Characterisation of tau aggregates in human samples at super-resolution".

This manuscript has been submitted as a preprint via BioRxiv here. A link to the final version will be provided upon publication.

Prerequisites

This analysis assumes a standard installation of Python 3 (=> 3.10.5). For specific package requirements, see the environment.yml file, or create a new conda environment containing all packages by running conda create -f environment.yml.

Raw data

Example images for use with the initial ComDet1 (diffraction-limited) and Picasso2 (super-resolution) steps have been provided, alongside the complete set of preprocessed data as an open-access Zenodo dataset. These datasets can be automatically collected using the raw_data.py script in the preprocessing folder, and will be placed in a new directory titled 'data'.

Workflow

Raw images were originally preprocessed using utility scripts provided by smma, which allow for manual quality control steps coupled to automatic processing using either a python implementation of ComDet1 for diffraction-limited images, or Picasso2 and SKAN3 for super-resolved images. Thresholds were optimised for each experiment by manually inspecting the output for various threshold combinations on positive and negative control images before applying the automatic analysis to the entire image dataset for that experiment.

The output of these preprocessing steps are then directly analysed using scripts available in the analysis folder. Here, each figure has a dedicated (independent) analysis script, which performs various filtering, calculation and statistical operations. The results are then saved to .csv where relevant before being visualised using scripts provided in the plotting folder. Again, each figure has a dedicated (independent) script. Thus, the scripts can be run in any order with the exception of requiring the analysis script for a given figure (and supplementary figure) to be run before plotting.

Acknowledgements

This work relies heavily on the excellent existing functionalities provided by the ComDet1, Picasso2 and SKAN3 packages.

References

  1. E. Katrukha, ekatrukha/ComDet: ComDet 0.5.3 (2020), doi:10.5281/ZENODO.4281064.

  2. J. Schnitzbauer, M. T. Strauss, T. Schlichthaerle, F. Schueder, R. Jungmann, Super-resolution microscopy with DNA-PAINT. Nature Protocols 2017 12:6. 12, 1198–1228 (2017).

  3. J. Nunez-Iglesias, A. J. Blanch, O. Looker, M. W. Dixon, L. Tilley, A new Python library to analyse skeleton images confirms malaria parasite remodelling of the red blood cell membrane skeleton. PeerJ. 2018, e4312 (2018).

Owner

  • Login: dboeken
  • Kind: user

Citation (citation.cff) 

cff-version: 1.1.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Böken
    given-names: Dorothea
    orcid: https://orcid.org/0009-0008-8443-4469
  - family-names: Cox
    given-names: Dezerae
    orcid: https://orcid.org/0000-0002-5345-8360
title: dboeken/Boeken_Tau_SiMPull: First submission
version: 1.0.0
date-released: 2023-06-12

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