replace preprint with nature pub

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

_pkgdown.yml

@@ -4,8 +4,8 @@ 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:

vignettes/MotrpacRatTraining6mo.Rmd

@@ -74,7 +74,7 @@ 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).
+(MoTrPAC). See our publication in [*Nature*](https://www.nature.com/articles/s41586-023-06877-w).
 **We highly recommend skimming the preprint 
 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
 </div>
 
 ## 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 

vignettes/key_metabolites.Rmd

@@ -38,7 +38,7 @@ 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.