Update version to 1.19.2, introduce 'append_samples()' method for com…

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: tidySingleCellExperiment
 Title: Brings SingleCellExperiment to the Tidyverse 
-Version: 1.19.1
+Version: 1.19.2
 Authors@R: c(person("Stefano", "Mangiola", 
     comment=c(ORCID="0000-0001-7474-836X"),
     email="[email protected]",

NAMESPACE

@@ -2,6 +2,7 @@
 
 S3method(add_count,SingleCellExperiment)
 S3method(anti_join,SingleCellExperiment)
+S3method(append_samples,SingleCellExperiment)
 S3method(arrange,SingleCellExperiment)
 S3method(as_tibble,SingleCellExperiment)
 S3method(bind_cols,SingleCellExperiment)
@@ -158,6 +159,7 @@ importFrom(tidyr,unnest)
 importFrom(tidyselect,all_of)
 importFrom(tidyselect,eval_select)
 importFrom(ttservice,aggregate_cells)
+importFrom(ttservice,append_samples)
 importFrom(ttservice,bind_cols)
 importFrom(ttservice,bind_rows)
 importFrom(ttservice,join_features)

R/dplyr_methods.R

@@ -29,14 +29,7 @@ arrange.SingleCellExperiment <- function(.data, ..., .by_group=FALSE) {
 #' @name bind_rows
 #' @rdname bind_rows
 #' @inherit ttservice::bind_rows
-#' 
-#' @examples
-#' data(pbmc_small)
-#' tt <- pbmc_small
-#' bind_rows(tt, tt)
-#'
-#' tt_bind <- tt |> select(nCount_RNA, nFeature_RNA)
-#' tt |> bind_cols(tt_bind)
+#' @noRd
 #' 
 #' @references
 #' Hutchison, W.J., Keyes, T.J., The tidyomics Consortium. et al. The tidyomics ecosystem: enhancing omic data analyses. Nat Methods 21, 1166–1170 (2024). https://doi.org/10.1038/s41592-024-02299-2
@@ -48,6 +41,13 @@ arrange.SingleCellExperiment <- function(.data, ..., .by_group=FALSE) {
 #' @importFrom SingleCellExperiment cbind
 #' @export
 bind_rows.SingleCellExperiment <- function(..., .id=NULL, add.cell.ids=NULL) {
+    lifecycle::deprecate_warn(
+        when = "1.19.2",
+        what = "bind_rows()",
+        with = "append_samples()",
+        details = "bind_rows is not a generic method in dplyr and may cause conflicts. Use append_samples."
+    )
+
     tts <- flatten_if(dots_values(...), is_spliced)
 
     new_obj <- SingleCellExperiment::cbind(tts[[1]], tts[[2]])
@@ -62,6 +62,45 @@ bind_rows.SingleCellExperiment <- function(..., .id=NULL, add.cell.ids=NULL) {
     new_obj
 }
 
+#' @name append_samples
+#' @rdname append_samples
+#' @title Append samples from multiple SingleCellExperiment objects
+#' 
+#' @description
+#' Append samples from multiple SingleCellExperiment objects by column-binding them.
+#' This function is equivalent to `cbind` but provides a tidyverse-like interface.
+#' 
+#' @param x First SingleCellExperiment object to combine
+#' @param ... Additional SingleCellExperiment objects to combine by samples
+#' @param .id Object identifier (currently not used)
+#' 
+#' @return A combined SingleCellExperiment object
+#' 
+#' @examples
+#' data(pbmc_small)
+#' append_samples(pbmc_small, pbmc_small)
+#' 
+#' @importFrom ttservice append_samples
+#' @importFrom rlang flatten_if
+#' @importFrom rlang is_spliced
+#' @importFrom SingleCellExperiment cbind
+#' @export
+append_samples.SingleCellExperiment <- function(x, ..., .id = NULL) {
+    # Combine all arguments into a list
+    tts <- flatten_if(list(x, ...), is_spliced)
+    new_obj <- do.call(cbind, tts)
+
+    # If duplicated cell names
+    if (any(duplicated(colnames(new_obj)))) {
+        warning("tidySingleCellExperiment says:",
+                " you have duplicated cell names, they will be made unique.")
+        unique_colnames <- make.unique(colnames(new_obj), sep = "_")
+        colnames(new_obj) <- unique_colnames
+    }
+
+    new_obj
+}
+
 #' @importFrom rlang flatten_if
 #' @importFrom rlang is_spliced
 #' @importFrom rlang dots_values

README.Rmd

@@ -461,7 +461,7 @@ pbmc_small_nested_interactions <-
             cell_signaling(genes=rownames(data), cluster=cluster) |>
             inter_network(data=data, signal=_, genes=rownames(data), cluster=cluster) %$%
             `individual-networks` |>
-            map_dfr(~ bind_rows(as_tibble(.x)))
+            map_dfr(~ append_samples(as_tibble(.x)))
     }))
 
 pbmc_small_nested_interactions |>

README.md

@@ -314,10 +314,6 @@ pbmc_small_pca <-
     ## TRUE, : You're computing too large a percentage of total singular values, use a
     ## standard svd instead.
 
-    ## Warning in (function (A, nv = 5, nu = nv, maxit = 1000, work = nv + 7, reorth =
-    ## TRUE, : did not converge--results might be invalid!; try increasing work or
-    ## maxit
-
 ``` r
 pbmc_small_pca
 ```
@@ -742,7 +738,7 @@ pbmc_small_nested_interactions <-
             cell_signaling(genes=rownames(data), cluster=cluster) |>
             inter_network(data=data, signal=_, genes=rownames(data), cluster=cluster) %$%
             `individual-networks` |>
-            map_dfr(~ bind_rows(as_tibble(.x)))
+            map_dfr(~ append_samples(as_tibble(.x)))
     }))
 
 pbmc_small_nested_interactions |>

inst/NEWS.rd

@@ -1,6 +1,14 @@
 \name{NEWS}
 \title{News for Package \pkg{tidySingleCellExperiment}}
 
+\section{Changes in version 1.19.2, Bioconductor 3.22 Release}{
+\itemize{
+    \item Soft deprecated \code{bind_rows()} in favor of \code{append_samples()} from ttservice.
+    \item Added \code{append_samples()} method for SingleCellExperiment objects.
+    \item \code{bind_rows()} is not a generic method in dplyr and may cause conflicts.
+    \item Users are encouraged to use \code{append_samples()} instead.
+}}
+
 \section{Changes in version 1.4.0, Bioconductor 3.14 Release}{
 \itemize{
     \item Improved sample_n, and sample_frac functions.

tests/testthat/test-dplyr_methods.R

@@ -24,9 +24,9 @@ df$factor <- sample(
 #     expect_identical(fd, df)
 # })
 
-test_that("bind_rows()", {
+test_that("append_samples()", {
     # warn about duplicated cells names
-    expect_warning(fd <- bind_rows(df, df))
+    expect_warning(fd <- append_samples(df, df))
     # cell names should be unique after binding
     expect_true(!any(duplicated(pull(fd, .cell))))
 })

vignettes/introduction.Rmd

@@ -273,6 +273,24 @@ pbmc_small_cluster %>%
         .value=.abundance_counts, scale="column")
 ```
 
+# Combining datasets
+
+We can use `append_samples()` to combine multiple SingleCellExperiment objects by samples.
+This is useful when you have multiple datasets that you want to analyze together.
+
+```{r}
+# Create two subsets of the data
+pbmc_subset1 <- pbmc_small_cluster %>%
+    filter(groups == "g1")
+
+pbmc_subset2 <- pbmc_small_cluster %>%
+    filter(groups == "g2")
+
+# Combine them using append_samples
+combined_data <- append_samples(pbmc_subset1, pbmc_subset2)
+combined_data
+```
+
 # Reduce dimensions
 
 We can calculate the first 3 UMAP dimensions using `r BiocStyle::Biocpkg("scater")`.
@@ -454,7 +472,7 @@ pbmc_small_nested_interactions <-
             cell_signaling(genes=rownames(data), cluster=cluster) %>%
             inter_network(data=data, signal=., genes=rownames(data), cluster=cluster) %$%
             `individual-networks` %>%
-            map_dfr(~ bind_rows(as_tibble(.x)))
+            map_dfr(~ append_samples(as_tibble(.x)))
     }))
 
 pbmc_small_nested_interactions %>%