Plasma protein-based organ-specific aging and mortality models unveil diseases as accelerated aging of organismal systems
Description
This repository accompanies our manuscript "Plasma protein-based organ-specific aging and mortality models unveil diseases as accelerated aging of organismal systems". The repository contains all code required to reproduce the analyses in our pulication. Please also see https://github.com/ludgergoeminne/organAging/ for the latest comments. If you make use of anything in this repository, we kindly request to make a reference to: Ludger J.E. Goeminne, Anastasiya Vladimirova, Alec Eames, Alexander Tyshkovskiy, M. Austin Argentieri, Kejun Ying, Mahdi Moqri, Vadim N. Gladyshev (2024). Plasma protein-based organ-specific aging and mortality models unveil diseases as accelerated aging of organismal systems, Cell Metabolism, doi: https://doi.org/10.1016/j.cmet.2024.10.005.
Files
Steps to reproduce
All analyses (except for model training, which was done in Python) were done in R, running in RStudio. If you want to reproduce our analysis, you will need an installation of R (https://www.r-project.org) and possibly RStudio (https://www.rstudio.com/products/rstudio/download/). Next, download this project to your computer. If you want to reproduce the full analysis based on individual-level data, you will also need to apply for access to the following datasets: The UK Biobank (Sudlow et al. (2015), apply from https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access) Bild et al. (2002) (apply from https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001416.v3.p1) Dammer et al. (2022) (apply from https://www.synapse.org/#!Synapse:syn30549757/files/) Additionally, the individual-level data from the following datasets can be downloaded freely: Filbin et al. (2021) (download from https://doi.org/10.17632/nf853r8xsj.2) Damsky et al. (2022) (download from https://www.ncbi.xyz/geo/query/acc.cgi?acc=GSE169148) Note that many temporary results (as long as they do not contain individual-level data from the restricted datasets) have been saved in the folder /data/rds and are loaded in the scripts by default to speed up the workflow. However, you can always check the code that was used to generate them. Thanks to the saved files, each file is created in such a way that it can be run independently of the other files. The most important files are numbered so that if you don't want to make use of the saved intermediary results, you can run them in ascending order just like we did to generate the results. Reproducing the figures To reproducing our figures, please look into the file /scripts/Arrange_Figures.R (if you want to reproduce a panel from a main figure) and/or /scripts/Arrange_Supp_Figures.R (if you want to reproduce a panel from a supplementary figure). For each figure object that is being assembled, the names of the scripts where the figure panels come from are given. For example, if one would like to reproduced Figure 4A, you will find the following comment in the file Arrange_Figures.R: ### Arrange, plot, and save Fig. 4 ### # Plot_Hazard_Ratios.R # Plot_Heatmaps_HR_Diseases.R # Plot_Scatter_Leave_Proteins_Out.R This example helps you find that the code to reproduce this hazard ratio plot can be found in the file Plot_Hazard_Ratios.R. The Excel files that from the Data S1 file were assembled with the scripts in file /scripts/Export_Figures_Excel.R. To find the scripts where the data was generated, we again suggest to look into the files /scripts/Arrange_Figures.R and /scripts/Arrange_Supp_Figures.R.
Institutions
Categories
Funding
National Institute on Aging