accommodate CRAN

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: scregclust
-Title: Reconstructing the regulatory programs of target genes in single cell data
+Title: Reconstructing the Regulatory Programs of Target Genes in scRNA-seq data
 Version: 0.1.10
 Authors@R: c(
     person("Felix", "Held", ,"[email protected]", role = c("aut", "cre"),
@@ -8,8 +8,8 @@ Authors@R: c(
            comment = c(ORCID = "0000-0001-5422-4243")),
     person("Sven", "Nelander", ,"[email protected]", role = c("aut"),
            comment = c(ORCID = "0000-0003-1758-1262")))
-Description: This package provides an implementation of the scregclust
-  algorithm (Larsson, Held, et al., 2024, <doi:10.1038/s41467-024-53954-3>)
+Description: Implementation of the scregclust algorithm
+  (Larsson, Held, et al., 2024, <doi:10.1038/s41467-024-53954-3>)
   which aims to reconstruct the regulatory programs of target genes in single cell data.
   Target genes are clustered into modules and each module is associated with a linear
   model accounting for the regulatory program driving the genes it contains.

R/plotting.R

@@ -261,7 +261,7 @@ collect_silhouette_data <- function(list_of_fits) {
 #'                     Either a single number to be used for all fits in
 #'                     `list_of_fits`, or one for each individual fit.
 #'
-#' @return A [`ggplot2`] plot showing the the silhouette scores for each
+#' @return A ggplot2 plot showing the the silhouette scores for each
 #'         supplied fit.
 #'
 #' @concept plotting
@@ -439,7 +439,7 @@ plot_silhouettes <- function(list_of_fits, penalization, final_config = 1L) {
 #'                     for the same penalty parameter across all fits in
 #'                     `list_of_fits`, or one for each individual fit.
 #'
-#' @return A [ggplot2] plot showing the average silhouette score and the
+#' @return A ggplot2 plot showing the average silhouette score and the
 #'         average predictive \eqn{R^2}
 #'
 #' @concept plotting

README.md

@@ -8,7 +8,7 @@
 
 The goal of *scregclust* is to cluster genes by regulatory programs. To do so, genes are clustered into modules which in turn are associated with regulators. The algorithm alternates between associating regulators to modules and reallocating target genes into modules.
 
-- The documentation for this package can be found at [https://scmethods.github.io/scregclust](https://scmethods.github.io/scregclust)
+- The documentation for this package can be found at [https://scmethods.github.io/scregclust/](https://scmethods.github.io/scregclust/)
 - A detailed description of the algorithm and an in-depth evaluation of its properties can be found in our original research article [Larsson, Held, et al. (2024) Reconstructing the regulatory programs underlying the phenotypic plasticity of neural cancers. Nature Communications 15, 9699 DOI 10.1038/s41467-024-53954-3](https://doi.org/10.1038/s41467-024-53954-3)
 
 ## Installation

vignettes/pbmc.Rmd

@@ -138,7 +138,7 @@ for convenience.
 # saveRDS(fit, file = "pbmc_scregclust.rds")
 
 url <- paste0(
-  "https://github.com/sven-nelander/scregclust/raw/main/datasets/",
+  "https://github.com/scmethods/scregclust/raw/main/datasets/",
   "pbmc_scregclust.rds"
 )
 path <- "pbmc_scregclust.rds"