Fixed and accepted by CRAN, 2.2.0 release

Author: mvfki

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: rliger
 Version: 2.2.0
-Date: 2025-04-24
+Date: 2025-05-01
 Type: Package
 Title: Linked Inference of Genomic Experimental Relationships
 Description: Uses an extension of nonnegative matrix factorization to identify shared and dataset-specific factors. See Welch J, Kozareva V, et al (2019) <doi:10.1016/j.cell.2019.05.006>, and Liu J, Gao C, Sodicoff J, et al (2020) <doi:10.1038/s41596-020-0391-8> for more details.

R/GSEA.R

@@ -382,7 +382,7 @@ plotGODot <- function(
                 yend = .data[['term_name']],
                 color = -log10(.data[['p_value']])
             ),
-            size = 0.5
+            linewidth = 0.5
         ) +
         ggplot2::geom_point() +
         ggplot2::scale_size_continuous(range = c(minDotSize, maxDotSize)) +

R/h5ad.R

@@ -50,7 +50,10 @@
 #' }
 #' @export
 #' @examples
+#' print("The example below works, but causes PDF manual rendering issue for some reason")
+#' \dontrun{
 #' writeH5AD(pbmc, filename = tempfile(fileext = ".h5ad"))
+#' }
 writeH5AD <- function(
         object,
         ...

R/integration.R

@@ -2120,6 +2120,7 @@ centroidAlign.Seurat <- function(
 #' \code{byDataset = FALSE} or each dataset a value otherwise.
 #' @export
 #' @examples
+#' \donttest{
 #' if (requireNamespace("RcppPlanc", quietly = TRUE)) {
 #'     pbmc <- pbmc %>%
 #'     normalize %>%
@@ -2129,6 +2130,7 @@ centroidAlign.Seurat <- function(
 #'     alignFactors
 #'     calcAgreement(pbmc)
 #' }
+#' }
 calcAgreement <- function(
         object,
         ndims = 40,

R/suggestK.R

@@ -14,7 +14,7 @@
 #'
 #' Note that this function is supposed to take a long time when a larger number
 #' of random starts is requested (e.g. 50) for a robust suggestion. It is safe
-#' to interupt the progress (e.g. Ctrl+C) and the function will still return
+#' to interrupt the progress (e.g. Ctrl+C) and the function will still return
 #' the recorded objective errors already completed.
 #' @param object A \linkS4class{liger} object.
 #' @param kTest A numeric vector of k values to be tested. Default 5, 10, 15,
@@ -29,10 +29,20 @@
 #' \item{stats}{A data frame containing the k values, objective errors, and
 #' random starts.}
 #' \item{figure}{A ggplot2 object showing the objective errors and variance
-#' for each k value.}
+#' for each k value. The left y-axis corresponds to the dots and bands, the
+#' right second y-axis maps to the blue line that stands for the variance. }
 #' @examples
+#' \donttest{
 #' pbmcPlot <- scaleNotCenter(pbmcPlot)
-#' suggestK(pbmcPlot, kTest = c(10, 15, 20), nRandomStart = 5)
+#' # Minimum test example, not for demonstrative recommendation
+#' suggests <- suggestK(
+#'     object = pbmcPlot,
+#'     kTest = c(2, 3),
+#'     nRandomStart = 2,
+#'     nIteration = 2
+#' )
+#' suggests$figure
+#' }
 suggestK <- function(
         object,
         kTest = seq(5, 50, 5),

R/suggestK2.R

@@ -20,7 +20,7 @@
 LIGER (installed as `rliger` ) is a package for integrating and analyzing multiple single-cell datasets, developed by the Macosko lab and maintained/extended by the Welch lab. It relies on integrative non-negative matrix factorization to identify shared and dataset-specific factors.
 
 Check out our [Cell paper](https://doi.org/10.1016/j.cell.2019.05.006) for a more complete description of the methods and analyses. To access data used in our SN and BNST analyses, visit our study "SCP466" on the
-[Single Cell Portal](https://singlecell.broadinstitute.org/single_cell). 
+Single Cell Portal. 
 
 LIGER can be used to compare and contrast experimental datasets in a variety of contexts, for instance: