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MotrpacRatTraining6moData

DOI R package version R-CMD-check Last commit

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Table of Contents

Overview

About this package

This package provides convenient access to the processed data and downstream analysis results presented in the main paper for the first large-scale multi-omic multi-tissue endurance exercise training study conducted in young adult rats by the Molecular Transducers of Physical Activity Consortium (MoTrPAC). Find the preprint on bioRxiv. See the vignette for examples of how to use this package.

While the data in this package can be used by themselves, the MotrpacRatTraining6mo R package relies heavily on this package and provides many functions to help retrieve and explore the data. See examples in the MotrpacRatTraining6mo vignette.

About MoTrPAC

MoTrPAC is a national research consortium designed to discover and perform preliminary characterization of the range of molecular transducers (the "molecular map") that underlie the effects of physical activity in humans. The program's goal is to study the molecular changes that occur during and after exercise and ultimately to advance the understanding of how physical activity improves and preserves health. The six-year program is the largest targeted NIH investment of funds into the mechanisms of how physical activity improves health and prevents disease. See motrpac.org and motrpac-data.org for more details.

Installation

Install this package with devtools:

if (!require("devtools", quietly = TRUE)){
  install.packages("devtools")
}
options(timeout=1e5) # extend the timeout
devtools::install_github("MoTrPAC/MotrpacRatTraining6moData")

The output for a successful installation looks something like this. Note that the *** moving datasets to lazyload DB step takes the longest (~5 minutes):

Downloading GitHub repo MoTrPAC/MotrpacRatTraining6moData@HEAD
✓  checking for file ‘.../MoTrPAC-MotrpacRatTraining6moData-1c6478a/DESCRIPTION’ ...
─  preparing ‘MotrpacRatTraining6moData’:
✓  checking DESCRIPTION meta-information
─  checking for LF line-endings in source and make files and shell scripts
─  checking for empty or unneeded directories
─  building ‘MotrpacRatTraining6moData_1.3.2.tar.gz’ (1.3s)
   
* installing *source* package ‘MotrpacRatTraining6moData’ ...
** using staged installation
** R
** data
*** moving datasets to lazyload DB
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (MotrpacRatTraining6moData)

Troubleshooting

If you get this error after setting options(timeout=1e5):

Downloading GitHub repo MoTrPAC/MotrpacRatTraining6moData@HEAD
Error in utils::download.file(url, path, method = method, quiet = quiet,  : 
  download from 'https://api.github.com/repos/MoTrPAC/MotrpacRatTraining6moData/tarball/HEAD' failed
Error in `action()`:
! `class` is absent but must be supplied.
Run `rlang::last_error()` to see where the error occurred.

...this seems to be an intermittent issue seen only on Mac, not Linux or Windows. This was resolved by installing the newest version of R.

Last resort

If you can't get devtools::install_github("MoTrPAC/MotrpacRatTraining6moData") to work, try this:

  1. Go to https://api.github.com/repos/MoTrPAC/MotrpacRatTraining6moData/tarball/HEAD, which will automatically start downloading this repository in a tarball
  2. Install the package from source:
    install.packages("~/Downloads/MoTrPAC-MotrpacRatTraining6moData-0729e2e.tar.gz", 
      repos = NULL, 
      type = "source")
    library(MotrpacRatTraining6moData)

Explore available data objects

Find the index of available objects here. Click on the name of an object in this index to see its documentation.

Alternatively, explore data objects in R:

# load the package
library(MotrpacRatTraining6moData)

# list available objects
data(package = "MotrpacRatTraining6moData")

# get the documentation for a given data object
?TRNSCRPT_LIVER_DA

# load a data object into your environment using `data()`
data(TRNSCRPT_LIVER_DA)
# or just use it directly, e.g., 
head(TRNSCRPT_LIVER_DA)

Access epigenomics data through Google Cloud Storage

Due to file size, only normalized sample-level data and differential analysis results corresponding to training-regulated features (5% IHW FDR) are contained in this package for chromatin accessibility (ATAC) and DNA methylation (METHYL). The full sets of epigenetic results may be downloaded either with R functions in the MotrpacRatTraining6mo package or through the following public URLs.

Note that clicking on a Link automatically starts a download. To instead copy the URL for an object, right-click the Link and select Copy Link Address.

To download and load epigenetic data within R, use one of the following functions in the MotrpacRatTraining6mo package:

  • load_sample_data(): For sample-level data from a single tissue and ome.
  • combine_normalized_data(): For sample-level data from multiple tissues or omes. Use include_epigen = TRUE.
  • combine_da_results(): For differential analysis results from multiple tissues or omes. Use include_epigen = TRUE.
  • Several other functions specifically for loading epigenetic data are documented here.

Note that the size in this table is the compressed size. Each object occupies several times more memory when loaded into R. The total compressed size for all of these objects is 8.68 GiB (~9.32 GB).

Type Assay Tissue Object Size (MiB) Click to download
Differential analysis results ATAC BAT ATAC_BAT_DA 329.71 Link
Differential analysis results ATAC HEART ATAC_HEART_DA 238.99 Link
Differential analysis results ATAC HIPPOC ATAC_HIPPOC_DA 223.06 Link
Differential analysis results ATAC KIDNEY ATAC_KIDNEY_DA 272.7 Link
Differential analysis results ATAC LIVER ATAC_LIVER_DA 202.48 Link
Differential analysis results ATAC LUNG ATAC_LUNG_DA 259.19 Link
Differential analysis results ATAC SKM-GN ATAC_SKMGN_DA 320.42 Link
Differential analysis results ATAC WAT-SC ATAC_WATSC_DA 359.23 Link
Normalized sample-level data ATAC BAT ATAC_BAT_NORM_DATA 43.11 Link
Normalized sample-level data ATAC HEART ATAC_HEART_NORM_DATA 31.34 Link
Normalized sample-level data ATAC HIPPOC ATAC_HIPPOC_NORM_DATA 33.52 Link
Normalized sample-level data ATAC KIDNEY ATAC_KIDNEY_NORM_DATA 40.03 Link
Normalized sample-level data ATAC LIVER ATAC_LIVER_NORM_DATA 27.53 Link
Normalized sample-level data ATAC LUNG ATAC_LUNG_NORM_DATA 34.54 Link
Normalized sample-level data ATAC SKM-GN ATAC_SKMGN_NORM_DATA 43.31 Link
Normalized sample-level data ATAC WAT-SC ATAC_WATSC_NORM_DATA 45.98 Link
Raw sample-level counts ATAC BAT ATAC_BAT_RAW_COUNTS 49.38 Link
Raw sample-level counts ATAC HEART ATAC_HEART_RAW_COUNTS 47.68 Link
Raw sample-level counts ATAC HIPPOC ATAC_HIPPOC_RAW_COUNTS 50.63 Link
Raw sample-level counts ATAC KIDNEY ATAC_KIDNEY_RAW_COUNTS 52.6 Link
Raw sample-level counts ATAC LIVER ATAC_LIVER_RAW_COUNTS 47.25 Link
Raw sample-level counts ATAC LUNG ATAC_LUNG_RAW_COUNTS 48.92 Link
Raw sample-level counts ATAC SKM-GN ATAC_SKMGN_RAW_COUNTS 50.96 Link
Raw sample-level counts ATAC WAT-SC ATAC_WATSC_RAW_COUNTS 48.91 Link
Feature annotation ATAC all ATAC_FEATURE_ANNOT 27.73 Link
Differential analysis results METHYL BAT METHYL_BAT_DA 281.5 Link
Differential analysis results METHYL HEART METHYL_HEART_DA 244.44 Link
Differential analysis results METHYL HIPPOC METHYL_HIPPOC_DA 194.39 Link
Differential analysis results METHYL KIDNEY METHYL_KIDNEY_DA 138.43 Link
Differential analysis results METHYL LIVER METHYL_LIVER_DA 218.21 Link
Differential analysis results METHYL LUNG METHYL_LUNG_DA 340.03 Link
Differential analysis results METHYL SKM-GN METHYL_SKMGN_DA 278.02 Link
Differential analysis results METHYL WAT-SC METHYL_WATSC_DA 220.35 Link
Normalized sample-level data METHYL BAT METHYL_BAT_NORM_DATA 76.76 Link
Normalized sample-level data METHYL HEART METHYL_HEART_NORM_DATA 59.43 Link
Normalized sample-level data METHYL HIPPOC METHYL_HIPPOC_NORM_DATA 56.76 Link
Normalized sample-level data METHYL KIDNEY METHYL_KIDNEY_NORM_DATA 38.66 Link
Normalized sample-level data METHYL LIVER METHYL_LIVER_NORM_DATA 55.02 Link
Normalized sample-level data METHYL LUNG METHYL_LUNG_NORM_DATA 83.75 Link
Normalized sample-level data METHYL SKM-GN METHYL_SKMGN_NORM_DATA 71.99 Link
Normalized sample-level data METHYL WAT-SC METHYL_WATSC_NORM_DATA 57.15 Link
Raw data METHYL BAT BAT_RAW_DATA 394.79 Link
Raw data METHYL HEART HEART_RAW_DATA 359.21 Link
Raw data METHYL HIPPOC HIPPOC_RAW_DATA 361.81 Link
Raw data METHYL KIDNEY KIDNEY_RAW_DATA 363.32 Link
Raw data METHYL LIVER LIVER_RAW_DATA 349.14 Link
Raw data METHYL LUNG LUNG_RAW_DATA 349.14 Link
Raw data METHYL SKM-GN SKMGN_RAW_DATA 378.84 Link
Raw data METHYL WAT-SC WATSC_RAW_DATA 366.74 Link
Raw sample-level counts METHYL BAT METHYL_BAT_RAW_COUNTS 71.6 Link
Raw sample-level counts METHYL HEART METHYL_HEART_RAW_COUNTS 56.99 Link
Raw sample-level counts METHYL HIPPOC METHYL_HIPPOC_RAW_COUNTS 52.56 Link
Raw sample-level counts METHYL KIDNEY METHYL_KIDNEY_RAW_COUNTS 36.7 Link
Raw sample-level counts METHYL LIVER METHYL_LIVER_RAW_COUNTS 52.54 Link
Raw sample-level counts METHYL LUNG METHYL_LUNG_RAW_COUNTS 77.17 Link
Raw sample-level counts METHYL SKM-GN METHYL_SKMGN_RAW_COUNTS 67.99 Link
Raw sample-level counts METHYL WAT-SC METHYL_WATSC_RAW_COUNTS 53.91 Link
Feature annotation METHYL all METHYL_FEATURE_ANNOT 152.77 Link

Getting help

See the vignette for examples of how to use this package. Still have questions? For questions, bug reporting, and data requests for this package, please submit a new issue and include as many details as possible.

If the concern is related to functions provided in the MotrpacRatTraining6mo package, please submit an issue here instead.

Acknowledgements

MoTrPAC is supported by the National Institutes of Health (NIH) Common Fund through cooperative agreements managed by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Arthritis and Musculoskeletal Diseases (NIAMS), and National Institute on Aging (NIA). Specifically, the MoTrPAC Study is supported by NIH grants U24OD026629 (Bioinformatics Center), U24DK112349, U24DK112342, U24DK112340, U24DK112341, U24DK112326, U24DK112331, U24DK112348 (Chemical Analysis Sites), U01AR071133, U01AR071130, U01AR071124, U01AR071128, U01AR071150, U01AR071160, U01AR071158 (Clinical Centers), U24AR071113 (Consortium Coordinating Center), U01AG055133, U01AG055137 and U01AG055135 (PASS/Animal Sites).

Data use agreement

Recipients and their Agents agree that in publications using any data from MoTrPAC public-use data sets they will acknowledge MoTrPAC as the source of data, including the version number of the data sets used, e.g.:

  • Data used in the preparation of this article were obtained from the Molecular Transducers of Physical Activity Consortium (MoTrPAC) database, which is available for public access at motrpac-data.org. Specific datasets used are [version numbers].

  • Data used in the preparation of this article were obtained from the Molecular Transducers of Physical Activity Consortium (MoTrPAC) MotrpacRatTraining6moData R package [version number].

Citing MoTrPAC data

MoTrPAC Study Group. 2022. Temporal dynamics of the multi-omic response to endurance exercise training across tissues. bioRxiv doi: 10.1101/2022.09.21.508770