CRAN release 2.0.0; with final minor fixes

.Rbuildignore

@@ -23,3 +23,4 @@ LICENSE
 ^vignettes/articles$
 ^doc$
 ^Meta$
+cran-comments.md

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: rliger
 Version: 2.0.0
-Date: 2024-03-19
+Date: 2024-03-20
 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.
@@ -30,13 +30,14 @@ Author: Joshua Welch [aut],
         Andrew Robbins [ctb]
 Maintainer: Yichen Wang <wayichen@umich.edu>
 BugReports: https://github.com/welch-lab/liger/issues
-URL: https://github.com/welch-lab/liger
+URL: https://welch-lab.github.io/liger/
 License: GPL-3
 biocViews:
 LazyData: true
 RoxygenNote: 7.3.1
 VignetteBuilder: knitr
 Encoding: UTF-8
+Additional_repositories: https:/welch-lab.r-universe.dev, https://blaserlab.r-universe.dev
 LinkingTo: Rcpp, RcppArmadillo, RcppProgress
 Depends:
     methods,
@@ -89,6 +90,3 @@ Suggests:
     SingleCellExperiment,
     SummarizedExperiment,
     testthat
-Remotes:
-    chris-mcginnis-ucsf/DoubletFinder,
-    welch-lab/RcppPlanc

NEWS.md

@@ -1,3 +1,15 @@
+## rliger Next
+
+- Standardized H5 writing specification that can be shared with other platforms.
+  - Currently we allow analysis with 10X cellranger output H5 file and H5AD file from anndata>=0.8.0
+  - Writing to H5AD file should follow anndata specification otherwise the file cannot be read back to a Python seesion.
+  - Writing to 10X H5 file should be carefully investigated.
+- Ability to reorganize datasets
+  - Allow doing something like `reorganize(ligerObj, variable = "somethingNotDataset")` and resulting in a new liger object with different ligerDataset grouping.
+- Ability to do downstream analysis on H5 data
+  - Pseudo-bulk should be easy because we are just aggregating cells.
+  - Wilcoxon might be a bit harder because ranks are calculated per gene but the H5 sparse data is column majored. Might need to find a fast on-disk transposition method.
+
 ## rliger 2.0.0
 
 - Added `ligerDataset` class for per-dataset information storage, with inheritance for specific modalities
@@ -8,10 +20,11 @@
 - Added gene name pattern for expression percentage QC metric
 - Added native Seurat object support for the core integration workflow
 - Added a documentation website built with pkgdown
-- Added new iNMF variant method, consensus iNMF (c-iNMF), in `runCINMF()`
+- Added new iNMF variant method, consensus iNMF (c-iNMF), in `runCINMF()`. Not stable.
 - Changed `liger` object class structure
-- Changed iNMF (previously `optimizeALS()`), UINMF (previously `optimizeALS(unshared = TRUE)`) and online iNMF (previously `online_iNMF()`) implementation with vastly improved performance. Now named by `runINMF()`, `runUINMF()` and `runOnlineINMF()` respectively, and wrapped in `runIntegration()`.
+- Moved iNMF (previously `optimizeALS()`), UINMF (previously `optimizeALS(unshared = TRUE)`) and online iNMF (previously `online_iNMF()`) implementation to new package *RcppPlanc* with vastly improved performance. Now wrapped in `runINMF()`, `runUINMF()` and `runOnlineINMF()` respectively, and all can be invoked with `runIntegration()`.
 - Updated H5AD support to match up with Python anndata package 0.8.0 specs
+- Renamed many function/argument names to follow camelCase style, original names are still available while deprecation warnings are issued
 
 ## rliger 1.0.1
 

R/ATAC.R

@@ -168,6 +168,7 @@ imputeKNN <- function(
 #' @seealso \code{\link{imputeKNN}}
 #' @export
 #' @examples
+#' \donttest{
 #' bmmc <- normalize(bmmc)
 #' bmmc <- selectGenes(bmmc)
 #' bmmc <- scaleNotCenter(bmmc)
@@ -181,6 +182,7 @@ imputeKNN <- function(
 #'         pathToCoords = system.file("extdata/hg19_genes.bed", package = "rliger")
 #'     )
 #' }
+#' }
 linkGenesAndPeaks <- function(
         object,
         useDataset,

R/clustering.R

@@ -37,15 +37,18 @@
 #' @param groupSingletons Whether to group single cells that make up their own
 #' cluster in with the cluster they are most connected to. Default \code{TRUE},
 #' if \code{FALSE}, assign all singletons to a \code{"singleton"} group.
+#' @param saveSNN Logical, whether to store the SNN graph, as a dgCMatrix
+#' object, in the object. Default \code{FALSE}.
 #' @param clusterName Name of the variable that will store the clustering result
 #' in \code{cellMeta} slot of \code{object}. Default \code{"leiden_cluster"} and
 #' \code{"louvain_cluster"}.
 #' @param seed Seed of the random number generator. Default \code{1}.
 #' @param verbose Logical. Whether to show information of the progress. Default
 #' \code{getOption("ligerVerbose")} or \code{TRUE} if users have not set.
-#' @return \code{object} with refined cluster assignment updated in
-#' \code{clusterName} variable in \code{cellMeta} slot. Can be fetched
-#' with \code{object[[clusterName]]}
+#' @return \code{object} with cluster assignment updated in \code{clusterName}
+#' variable in \code{cellMeta} slot. Can be fetched with
+#' \code{object[[clusterName]]}. If \code{saveSNN = TRUE}, the SNN graph will
+#' be stored at \code{object@uns$snn}.
 #' @rdname runCluster
 #' @export
 #' @examples
@@ -65,6 +68,7 @@ runCluster <- function(
         useRaw = NULL,
         useDims = NULL,
         groupSingletons = TRUE,
+        saveSNN = FALSE,
         clusterName = paste0(method, "_cluster"),
         seed = 1,
         verbose = getOption("ligerVerbose", TRUE)
@@ -121,8 +125,11 @@ runCluster <- function(
                               groupSingletons = groupSingletons,
                               verbose = verbose)
     cellMeta(object, clusterName, check = FALSE) <- clusts
+    if (isTRUE(saveSNN)) object@uns$snn <- snn
     if (isTRUE(verbose))
-        cli::cli_process_done(msg_done = "{method} clustering on {type} cell factor loadings ... Found {nlevels(clusts)} clusters.")
+        cli::cli_process_done(
+            msg_done = "{method} clustering on {type} cell factor loadings ... Found {nlevels(clusts)} cluster{?s}."
+        )
     object@uns$defaultCluster <- object@uns$defaultCluster %||% clusterName
     if (isTRUE(verbose))
         cli::cli_alert_info("{.field cellMeta} variable {.val {clusterName}} is now set as default.")

R/h5Utility.R

@@ -388,8 +388,7 @@ closeAllH5.ligerDataset <- function(object) {
 #' @return Nothing is returned. H5 file will be created on disk.
 #' @examples
 #' raw <- rawData(pbmc, "ctrl")
-#' writeH5(raw, "ctrl.h5")
-#' unlink("ctrl.h5")
+#' writeH5(raw, tempfile(pattern = "ctrl_", fileext = ".h5"))
 writeH5 <- function(x, file, ...) {
     UseMethod("writeH5", x)
 }

R/heatmap.R

@@ -35,6 +35,7 @@
 #' @export
 #' @rdname plotHeatmap
 #' @examples
+#' \donttest{
 #' plotGeneHeatmap(pbmcPlot, varFeatures(pbmcPlot))
 #' plotGeneHeatmap(pbmcPlot, varFeatures(pbmcPlot),
 #'                 useCellMeta = c("leiden_cluster", "dataset"),
@@ -44,6 +45,7 @@
 #' plotFactorHeatmap(pbmcPlot, cellIdx = pbmcPlot$leiden_cluster %in% 1:3,
 #'                   useCellMeta = c("leiden_cluster", "dataset"),
 #'                   cellSplitBy = "leiden_cluster")
+#' }
 plotGeneHeatmap <- function(
         object,
         features,

R/integration.R

@@ -1651,13 +1651,15 @@ quantile_norm <- function( # nocov start
 #' \code{byDataset = FALSE} or each dataset a value otherwise.
 #' @export
 #' @examples
-#' pbmc <- pbmc %>%
-#'   normalize %>%
-#'   selectGenes %>%
-#'   scaleNotCenter %>%
-#'   runINMF %>%
-#'   quantileNorm
-#' calcAgreement(pbmc)
+#' if (requireNamespace("RcppPlanc", quietly = TRUE)) {
+#'     pbmc <- pbmc %>%
+#'     normalize %>%
+#'     selectGenes %>%
+#'     scaleNotCenter %>%
+#'     runINMF %>%
+#'     quantileNorm
+#'     calcAgreement(pbmc)
+#' }
 calcAgreement <- function(
         object,
         ndims = 40,
@@ -1777,7 +1779,7 @@ calcAgreement <- function(
 #' @param clusterVar The name of one variable in \code{cellMeta(object)}.
 #' Default \code{NULL} uses default clusters.
 #' @param nNeighbors Number of neighbors to use in calculating alignment.
-#' Default \code{NULL} uses code{floor(0.01 * ncol(object))}, with a lower bound
+#' Default \code{NULL} uses \code{floor(0.01*ncol(object))}, with a lower bound
 #' of 10 in all cases except where the total number of sampled cells is less
 #' than 10.
 #' @param cellIdx,cellComp Character, logical or numeric index that can
@@ -1791,13 +1793,15 @@ calcAgreement <- function(
 #' @return The alignment metric.
 #' @export
 #' @examples
-#' pbmc <- pbmc %>%
-#'   normalize %>%
-#'   selectGenes %>%
-#'   scaleNotCenter %>%
-#'   runINMF %>%
-#'   quantileNorm
-#' calcAlignment(pbmc)
+#' if (requireNamespace("RcppPlanc", quietly = TRUE)) {
+#'     pbmc <- pbmc %>%
+#'     normalize %>%
+#'     selectGenes %>%
+#'     scaleNotCenter %>%
+#'     runINMF %>%
+#'     quantileNorm
+#'     calcAlignment(pbmc)
+#' }
 calcAlignment <- function(
         object,
         clustersUse = NULL,

README.md

@@ -55,6 +55,8 @@ Meanwhile, since version 2.0.0, LIGER is massively updated for usability and int
 * [Introduction to new liger object and other related classes](https://welch-lab.github.io/liger/articles/liger_object.html)
 * [Running Liger directly on Seurat objects](https://welch-lab.github.io/liger/articles/liger_with_seurat.html)
 
+If you need to refer to the tutorials for the old version of rliger, please check the [GitHub archive v1.0.1](https://github.com/welch-lab/liger/tree/v1.0.1/vignettes), download the desired rendered HTML files and open them in your browser.
+
 ## Sample Datasets
 
 The `rliger` package provides different types of small toy dataset for basic demos of the functions. After attaching the package in an R session, you can load them with:

cran-comments.md

@@ -0,0 +1,28 @@
+# rliger 2.0.0
+
+## R CMD check results
+
+There were no ERRORs or WARNINGs.
+
+There were two NOTEs from winbuilder:
+
+```
+* checking CRAN incoming feasibility ... [86s] NOTE
+Maintainer: 'Yichen Wang <wayichen@umich.edu>'
+
+Suggests or Enhances not in mainstream repositories:
+  DoubletFinder, RcppPlanc
+Availability using Additional_repositories specification:
+  DoubletFinder   yes   https://blaserlab.r-universe.dev
+  RcppPlanc       yes   https:/welch-lab.r-universe.dev 
+```
+
+```
+* checking package dependencies ... NOTE
+Packages suggested but not available for checking:
+  'DoubletFinder', 'RcppPlanc'
+```
+
+*DoubletFinder* and *RcppPlanc* are hosted on R-universe and used conditionally in rliger. 
+
+Besides, we kindly request that CRAN team can approve our submission for *RcppPlanc* so we can move it to Imports.

tests/testthat/test_downstream.R

@@ -70,12 +70,14 @@ test_that("clustering", {
     pbmc <- runOnlineINMF(pbmc, k = 20, minibatchSize = 100)
     expect_message(runCluster(pbmc, nRandomStarts = 1),
                    "leiden clustering on unnormalized")
+
     expect_message(runCluster(pbmc, nRandomStarts = 1, method = "louvain"),
                    "louvain clustering on unnormalized")
 
     pbmc <- quantileNorm(pbmc)
-    expect_message(runCluster(pbmc, nRandomStarts = 1),
+    expect_message(pbmc <- runCluster(pbmc, nRandomStarts = 1, saveSNN = TRUE),
                    "leiden clustering on quantile normalized")
+    expect_is(pbmc@uns$snn, "dgCMatrix")
     expect_message(runCluster(pbmc, nRandomStarts = 1, method = "louvain"),
                    "louvain clustering on quantile normalized")