Begin writing JOSS paper.

R/utils.R

@@ -5,13 +5,13 @@
 #'
 #' @param mat A numeric matrix or data frame.
 #' @param root A logical value indicating whether to take the square root of the cosine dissimilarity
-#' matrix to obtain Euclidean distances. Default is FALSE.
+#' matrix. Default is FALSE.
 #'
 #' @return A dissimilarity matrix of class "dist" containing the cosine dissimilarity between each pair of rows.
 #'
 #' @examples
 #' TRUE
-cosine_dissimilarity <- function(mat, root = F) {
+cosine_dissimilarity <- function(mat, root = FALSE) {
   sim = mat / sqrt(rowSums(mat * mat))
   sim = sim %*% t(sim)
 

joss/paper.bib

@@ -1,17 +1,3 @@
-@article{RN13,
-   author = {Edera, Alejandro A and Milone, Diego H and Stegmayer, Georgina},
-   title = {Anc2vec: embedding gene ontology terms by preserving ancestors relationships},
-   journal = {Briefings in Bioinformatics},
-   volume = {23},
-   number = {2},
-   abstract = {The gene ontology (GO) provides a hierarchical structure with a controlled vocabulary composed of terms describing functions and localization of gene products. Recent works propose vector representations, also known as embeddings, of GO terms that capture meaningful information about them. Significant performance improvements have been observed when these representations are used on diverse downstream tasks, such as the measurement of semantic similarity between GO terms and functional similarity between proteins. Despite the success shown by these approaches, existing embeddings of GO terms still fail to capture crucial structural features of the GO. Here, we present anc2vec, a novel protocol based on neural networks for constructing vector representations of GO terms by preserving three important ontological features: its ontological uniqueness, ancestors hierarchy and sub-ontology membership. The advantages of using anc2vec are demonstrated by systematic experiments on diverse tasks: visualization, sub-ontology prediction, inference of structurally related terms, retrieval of terms from aggregated embeddings, and prediction of protein–protein interactions. In these tasks, experimental results show that the performance of anc2vec representations is better than those of recent approaches. This demonstrates that higher performances on diverse tasks can be achieved by embeddings when the structure of the GO is better represented. Full source code and data are available at https://github.com/sinc-lab/anc2vec.},
-   ISSN = {1477-4054},
-   DOI = {10.1093/bib/bbac003},
-   url = {https://doi.org/10.1093/bib/bbac003},
-   year = {2022},
-   type = {Journal Article}
-}
-
 @article{RN78,
    author = {Nguyen, Jennifer H. and Curtis, Melissa A. and Imami, Ali S. and Ryan, William G. and Alganem, Khaled and Neifer, Kari L. and Nawor, Charlotte N. and Kistler, Brian P. and Miller, Gary W. and Shukla, Rammohan and McCullumsmith, Robert E. and Burkett, James P.},
    title = {Developmental pyrethroid exposure disrupts molecular pathways for circadian rhythms and synaptic plasticity in mouse brain},

joss/paper.md

@@ -19,11 +19,23 @@ affiliations:
 
 # Summary
 
-PAVER is...
+The terminal outputs of many omics experiments are long lists of genes, proteins, or metabolites. These lists are often analyzed using pathway analysis tools, which identify pathways that are enriched for the list of genes. However, these tools often return long lists of pathways themselves, which can be difficult to interpret. PAVER is an R package that automatically curates long lists of pathways into groups of most representative terms (MRTs) and provides publication-ready intuitive visualizations. It makes it easy to integrate multiple pathway analyses, identify relevant biological insights and is designed to work with any pathway database.
 
-# Statement of need
+# Statement of Need
 
-We need PAVER because...
+The surge in high-throughput multiomics technologies has revolutionized biological research but simultaneously introduced a significant data interpretation challenge. Current tools, predominantly built around the Gene Ontology (GO), provide exhaustive pathway analysis results that are cumbersome and time-intensive to manually curate. Moreover, there's a notable limitation in harnessing insights from non-GO pathway databases like KEGG and Reactome. There's a pressing need for a comprehensive solution that not only integrates pathway analyses from multiple pathway analyses but also distills the vast results into interpretable insights for researchers. PAVER clusters pathways to identify the most representative terms using embedding representations, streamlining the interpretation process. Moreover, it is designed for compatibility with any pathway database, offering researchers a versatile and efficient tool to navigate the complexities of multiomics data interpretation.
+
+PAVER was designed to be easy to use by researchers and students with minimal coding experience. It has already been using in a number of scientific publications to aid in the intepretation of pathway analyses [@RN78].
+
+# Overview
+
+![Caption for example figure.](figures/overview.png)
+
+# Example
+
+# Licensing and Availability
+
+The PAVER R package is licensed under the GNU General Public License v3.0. It is available on GitHub, and can be installed using remotes::install_github("willgryan/PAVER"). All code is open-source and hosted on GitHub, and bugs can be reported using the issue tracker at https://github.com/willgryan/PAVER/issues/.
 
 # Acknowledgements