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| Format: | Recurso digital |
| Language: | English |
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2026
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| Online Access: | https://doi.org/10.5281/zenodo.18671201 |
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| _version_ | 1866901903396306944 |
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| author | Onishchenko, Evgeny Noor, Elad |
| author_facet | Onishchenko, Evgeny Noor, Elad |
| contents | <div> <h2>Instructions</h2> <p><span>This dataset contains source data and scripts used to generate figure panels and tables describing analysis of lysine and arginine vacuolar transport in budding yeast using dynamic labeling assays.</span></p> <p><strong>important note</strong> In order to precisely recreate the results, we recommend installing the scripts on a computer running Mac OS and python 3.13, which is what we used for the analysis.</p> Download the Jupyter notebook (detailed_model.ipynb), R script (manuscript_plots.R) and the Data folder (data.zip). <p>Extract the Zip file in the same location as the Jupyter notebook and the R script.</p> <p> </p> <p><strong>First</strong>, run the jupyter notebook (one can conveniently use VS code). </p> <p>Beforehand, ensure that the required modules were installed:</p> <div> <div><span>from</span><span> path </span><span>import</span><span> Path</span></div> <div><span>import</span><span> numpy </span><span>as</span><span> np </span><span># common mathematical functions</span></div> <div><span>import</span><span> pandas </span><span>as</span><span> pd </span><span># enables working with dataframes</span></div> <div><span>import</span><span> matplotlib.pyplot </span><span>as</span><span> plt </span><span># a standard plotting package for python</span></div> <div><span>import</span><span> seaborn </span><span>as</span><span> sns </span><span># extra tools for plotting (such as violin plots)</span></div> <div><span>from</span><span> tqdm </span><span>import</span><span> tqdm</span></div> <div><span>import</span><span> itertools</span></div> <div><span>import</span><span> os</span></div> <div><span>from</span><span> symbolic_compartmental_model </span><span>import</span><span> SymbolicCompartmentalModel </span><span>#package to parametrize compartmental models</span></div> </div> <p> </p> <p><strong>Next</strong> run the R script (use R studio).</p> <p> </p> <p>Also, ensure that the required packages were installed:</p> <p>library(stringr)<br>library(reshape2)<br>library(data.table)<br>library(plyr)<br>library(dplyr)<br>library(gplots)<br>library(gridExtra)<br>library(RColorBrewer)<br>library(ggplot2)<br>library(openxlsx)<br>library(tidyr)<br>library(shuffle)<br>library(stringr)</p> <p> </p> <p>Pre-computed outputs of the scripts can be found in the results folder (results.zip).</p> </div> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18671201 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | analysis of lysine and arginine vacuolar transport by dynamic labeling Onishchenko, Evgeny Noor, Elad SILAC dynamic labeling compartmnetal models Yeast Mass spectrometry <div> <h2>Instructions</h2> <p><span>This dataset contains source data and scripts used to generate figure panels and tables describing analysis of lysine and arginine vacuolar transport in budding yeast using dynamic labeling assays.</span></p> <p><strong>important note</strong> In order to precisely recreate the results, we recommend installing the scripts on a computer running Mac OS and python 3.13, which is what we used for the analysis.</p> Download the Jupyter notebook (detailed_model.ipynb), R script (manuscript_plots.R) and the Data folder (data.zip). <p>Extract the Zip file in the same location as the Jupyter notebook and the R script.</p> <p> </p> <p><strong>First</strong>, run the jupyter notebook (one can conveniently use VS code). </p> <p>Beforehand, ensure that the required modules were installed:</p> <div> <div><span>from</span><span> path </span><span>import</span><span> Path</span></div> <div><span>import</span><span> numpy </span><span>as</span><span> np </span><span># common mathematical functions</span></div> <div><span>import</span><span> pandas </span><span>as</span><span> pd </span><span># enables working with dataframes</span></div> <div><span>import</span><span> matplotlib.pyplot </span><span>as</span><span> plt </span><span># a standard plotting package for python</span></div> <div><span>import</span><span> seaborn </span><span>as</span><span> sns </span><span># extra tools for plotting (such as violin plots)</span></div> <div><span>from</span><span> tqdm </span><span>import</span><span> tqdm</span></div> <div><span>import</span><span> itertools</span></div> <div><span>import</span><span> os</span></div> <div><span>from</span><span> symbolic_compartmental_model </span><span>import</span><span> SymbolicCompartmentalModel </span><span>#package to parametrize compartmental models</span></div> </div> <p> </p> <p><strong>Next</strong> run the R script (use R studio).</p> <p> </p> <p>Also, ensure that the required packages were installed:</p> <p>library(stringr)<br>library(reshape2)<br>library(data.table)<br>library(plyr)<br>library(dplyr)<br>library(gplots)<br>library(gridExtra)<br>library(RColorBrewer)<br>library(ggplot2)<br>library(openxlsx)<br>library(tidyr)<br>library(shuffle)<br>library(stringr)</p> <p> </p> <p>Pre-computed outputs of the scripts can be found in the results folder (results.zip).</p> </div> |
| title | analysis of lysine and arginine vacuolar transport by dynamic labeling |
| topic | SILAC dynamic labeling compartmnetal models Yeast Mass spectrometry |
| url | https://doi.org/10.5281/zenodo.18671201 |