https://github.com/arcadia-science/2024-phenotypeomat
Science Score: 23.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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○codemeta.json file
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✓DOI references
Found 2 DOI reference(s) in README -
✓Academic publication links
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○Scientific vocabulary similarity
Low similarity (14.3%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: Arcadia-Science
- License: mit
- Language: Python
- Default Branch: main
- Size: 30.3 KB
Statistics
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
- Releases: 1
Metadata Files
README.md
2024-phenotypeomat
Purpose
This repository contains the analysis code to create the data driven components of the figures in our pub The phenotype-o-mat: A flexible tool for collecting visual phenotypes
These scripts are intended to provide examples of how an experimenter might analyze data collected on the Phenotype-o-mat.
A comprehensive protocol for assembling a Phenotype-o-mat is available here
Installation and Setup
This repository uses conda to manage software environments and installations. You can find operating system-specific instructions for installing miniconda here. After installing conda and mamba, run the following command to create the pipeline run environment.
{bash}
mamba env create -n phenotypeomat-analysis --file envs/dev.yml
conda activate phenotypeomat-analysis
After setting up the conda environment, the analyses can be run as follows:
python3 colony_segment_figure_chr_fl_fig.py [PATH TO SINGLE C. REINHARDTII TIFF] [PATH TO SINGLE C. SMITHII TIFF]
python3 parent_strains_reflectance_fig.py [PATH TO FOLDER CONTAINING IMAGES]
Data
The data analyzed for the pub are available here
Overview
Description of the folder structure
The analysis scripts are located in the data_analysis_scripts folder.
The dev.yml file defines the conda envionrment to run the scripts and is contained in the env folder.
Methods
These two scripts analyze and plot two types of data collected using the phenotype-o-mat: chlorophyll fluorescence data and multi-wavelength reflectance data.
Each script starts by taking a single image (flurescence or transillumination) and identifying colony location and shape. These segmentations are then used to collect the remaining intensity data (fluorescence or reflectance) and plot that data. The details of those plots can be seen in the pub or by running the scripts.
Compute Specifications
The computer used to run these analyses: CPU: i7-1260P RAM: 32GB Operating system: Ubuntu 22.04.4
These aren't complex analyses so any computer should work
Contributing
See how we recognize feedback and contributions to our code.
Owner
- Name: Arcadia Science
- Login: Arcadia-Science
- Kind: organization
- Location: United States of America
- Website: https://www.arcadiascience.com/
- Twitter: ArcadiaScience
- Repositories: 16
- Profile: https://github.com/Arcadia-Science
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Dependencies
- actions/checkout v3 composite
- actions/setup-python v4 composite