diff --git a/DESCRIPTION b/DESCRIPTION index 61d620a..a76943a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,7 +2,7 @@ Type: Package Package: MotrpacRatTraining6mo Title: Analysis of the MoTrPAC endurance exercise training data in 6-month-old rats -Version: 1.6.5 +Version: 1.6.6 Authors@R: c( person("Nicole", "Gay", , "nicole.r.gay@gmail.com", role = c("cre", "aut")), person("David", "Amar", , "ddam.am@gmail.com", role = "aut"), @@ -29,7 +29,7 @@ Imports: dplyr, edgeR, ggplot2, - ggraph, + ggraph (>= 2.2.0), grid, igraph, limma, diff --git a/NEWS.md b/NEWS.md index 5b83a45..b8c398b 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,7 +1,11 @@ +# MotrpacRatTraining6mo 1.6.6 (2024-09-29) + +* Fix bug due to backwards incompatibility between `ggraph` 2.1.0 and 2.2.0. Require `ggraph >= 2.2.0`. + # MotrpacRatTraining6mo 1.6.5 (2023-11-08) * Point to development version of `RCy3` from GitHub to avoid `R-CMD check` error `RCy3: Can't install dependency uchardet`. -* Install `plotrix` from GitHub instead of CRAN to avoid `R-CMD check` warning `Requires (indirectly) orphaned package: ‘plotrix’`. +* Install `plotrix` from GitHub instead of CRAN to avoid `R-CMD check` warning `Requires (indirectly) orphaned package: 'plotrix'`. CRAN has marked `plotrix` as orphaned. `plotrix` is a dependency for `mutoss`, which is a dependency for `metap`. * Increment required `MotrpacRatTraining6moData` version. diff --git a/R/bayesian_graphical_clustering.R b/R/bayesian_graphical_clustering.R index af2285a..e91285f 100644 --- a/R/bayesian_graphical_clustering.R +++ b/R/bayesian_graphical_clustering.R @@ -922,7 +922,7 @@ get_tree_plot_for_tissue <- function( d_g_our_layout[grepl(n,d_g_our_layout$name),"y"] = l_y_lim[1] + (j-1)*yjump } # make sure that the 0w node is in the same line as of the no response - d_g_our_layout[d_g_our_layout$name == "0w","y"] = d_g_our_layout[grepl("F0_M0",d_g_our_layout$name),"y"][1] + d_g_our_layout[d_g_our_layout$name == "0w","y"] = d_g_our_layout[grepl("F0_M0",d_g_our_layout$name),"y"][1,1] # set node sizes and other features igraph::V(d_g)$setsize = d_nodes[V(d_g)$name,"size"] igraph::V(d_g)$setsize[V(d_g)$name == "0w"] = stats::median(igraph::V(d_g)$setsize) diff --git a/README.md b/README.md index 69f7dfd..e7e5297 100644 --- a/README.md +++ b/README.md @@ -32,7 +32,7 @@ This package provides functions to fetch, explore, and reproduce the processed d 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](https://www.biorxiv.org/content/10.1101/2022.09.21.508770v2). +(MoTrPAC). See our publication in [*Nature*](https://www.nature.com/articles/s41586-023-06877-w). **See the [vignette](https://motrpac.github.io/MotrpacRatTraining6mo/articles/MotrpacRatTraining6mo.html) for examples of how to use this package.** While some of the functions in this package can be used by themselves, they @@ -104,6 +104,5 @@ 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 +## Citing MoTrPAC data +MoTrPAC Study Group. Temporal dynamics of the multi-omic response to endurance exercise training. *Nature* **629**, 174–183 (2024). https://doi.org/10.1038/s41586-023-06877-w diff --git a/_pkgdown.yml b/_pkgdown.yml index 2c859d1..5d1d8c4 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -4,14 +4,14 @@ template: home: links: - - text: Preprint - href: https://www.biorxiv.org/content/10.1101/2022.09.21.508770v2 + - text: Publications + href: https://www.nature.com/collections/cfiiibcebh reference: - title: Load data - desc: Load data and analysis results used in the preprint + desc: Load data and analysis results used in the publication contents: - list_available_data diff --git a/vignettes/MotrpacRatTraining6mo.Rmd b/vignettes/MotrpacRatTraining6mo.Rmd index 4e39395..7d95b0e 100644 --- a/vignettes/MotrpacRatTraining6mo.Rmd +++ b/vignettes/MotrpacRatTraining6mo.Rmd @@ -74,8 +74,8 @@ This package provides functions to fetch, explore, and reproduce 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](https://www.biorxiv.org/content/10.1101/2022.09.21.508770v2). -**We highly recommend skimming the preprint +(MoTrPAC). See our publication in [*Nature*](https://www.nature.com/articles/s41586-023-06877-w). +**We highly recommend skimming the paper before using this package as it provides important context and much greater detail than we can provide here.** @@ -151,7 +151,7 @@ e.g., [`?METAB_FEATURE_ID_MAP`](https://motrpac.github.io/MotrpacRatTraining6moD ## Study design -Details of the experimental design can be found in the [supplementary methods of the bioRxiv preprint](https://www.biorxiv.org/content/biorxiv/early/2022/10/05/2022.09.21.508770/DC1/embed/media-1.pdf?download=true). +Details of the experimental design can be found in the [supplementary information of our *Nature* publication](https://www.nature.com/articles/s41586-023-06877-w#Sec14). Briefly, 6-month-old young adult rats were subjected to progressive endurance exercise training for 1, 2, 4, or 8 weeks, with tissues collected 48 hours after the last training bout. @@ -355,7 +355,7 @@ and you can load data objects into your environment using `data()`, e.g., ## Differential analysis More details about the differential analysis methods are available in the -[supplementary methods of the bioRxiv preprint](https://www.biorxiv.org/content/biorxiv/early/2022/10/05/2022.09.21.508770/DC1/embed/media-1.pdf?download=true). +[supplementary information of our *Nature* publication](https://www.nature.com/articles/s41586-023-06877-w#Sec14). Simply put, the *training* differential analysis considers all training groups for each sex (sedentary controls and 4 training time points) to determine if the analyte significantly changes in either @@ -501,7 +501,7 @@ run `repfdr` on your own data, and explore and visualize the nodes, edges, and p (collectively referred to as clusters) in the resulting graph. For additional details and advantages of this approach over traditional clustering methods, see the -[supplementary methods of the bioRxiv preprint](https://www.biorxiv.org/content/biorxiv/early/2022/10/05/2022.09.21.508770/DC1/embed/media-1.pdf?download=true). +[supplementary information of our *Nature* publication](https://www.nature.com/articles/s41586-023-06877-w#Sec14). If you are more interested in exploring the existing pathway enrichment results, skip to [Visualization of pathway enrichment results](#vizEnrich). @@ -582,7 +582,7 @@ hist(unlist(lapply(paths, length)), breaks=100) ``` `extract_main_clusters()` returns the subset of graphical clusters for which pathway -enrichment was performed for the [preprint](https://www.biorxiv.org/content/10.1101/2022.09.21.508770v2), +enrichment was performed for the [*Nature* publication](https://www.nature.com/articles/s41586-023-06877-w), namely the 2 largest nodes, 2 largest edges, 10 largest non-null paths, and all 8-week nodes from the graphical representation of training-regulated features in each tissue. ```{r extract main clusters} @@ -753,8 +753,8 @@ artificially small p-values. Pathway enrichment results were adjusted over *all* tests using [IHW](https://www.nature.com/articles/nmeth.3885) with tissue as a covariate. -Pathway enrichment results for graphical clusters of interest presented in the -[preprint](https://www.biorxiv.org/content/10.1101/2022.09.21.508770v2) are provided in `GRAPH_PW_ENRICH`. +Pathway enrichment results for graphical clusters of interest presented in our +[publication](https://www.nature.com/articles/s41586-023-06877-w) are provided in `GRAPH_PW_ENRICH`. ### Enrichment with standard PWs `cluster_pathway_enrichment()` is a wrapper for diff --git a/vignettes/key_metabolites.Rmd b/vignettes/key_metabolites.Rmd index 99c0164..9abc374 100644 --- a/vignettes/key_metabolites.Rmd +++ b/vignettes/key_metabolites.Rmd @@ -38,9 +38,9 @@ knitr::opts_chunk$set( ## Motivation -This report was motivated by this reviewer comment regarding the [preprint](https://www.biorxiv.org/content/10.1101/2022.09.21.508770v2): +This report was motivated by this reviewer comment regarding our [*Nature* publication](https://www.nature.com/articles/s41586-023-06877-w): -**Reviewer 1:** Given the extensive collection of metabolomics data (targeted and untargeted) for so many tissues and temporal timepoints (Figure 1C) it will be interesting to explore the changes of several key cellular metabolites in addition to the KEGG pathways. For example, it will be interesting to see sex- and time-specific changes in the plasma, muscles, heart of such metabolites as glucose, pyruvate, lactate, acetate, perhaps key intermediates of glycolysis and the TCA cycle. If the data is available, it will be also interesting to characterize the changes in the energy and redox ratios, i.e. ATP/ADP, NADH/NAD+ (or individual concentrations, such as ATP). Given the central roles of these metabolites in cellular physiology/health and multiple changes in the corresponding metabolic pathways (Figure 7), it will be good to present and discuss the observed metabolic changes. +**Reviewer 1:** Given the extensive collection of metabolomics data (targeted and untargeted) for so many tissues and temporal timepoints (Figure 1C) it will be interesting to explore the changes of several key cellular metabolites in addition to the KEGG pathways. For example, it will be interesting to see sex- and time-specific changes in the plasma, muscles, heart of such metabolites as glucose, pyruvate, lactate, acetate, perhaps key intermediates of glycolysis and the TCA cycle. If the data is available, it will be also interesting to characterize the changes in the energy and redox ratios, i.e. ATP/ADP, NADH/NAD+ (or individual concentrations, such as ATP). Given the central roles of these metabolites in cellular physiology/health and multiple changes in the corresponding metabolic pathways ([Figure 6]), it will be good to present and discuss the observed metabolic changes. ```{r setup, message = FALSE, warning = FALSE} library(MotrpacRatTraining6mo)