Append samples

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: tidySummarizedExperiment
 Title: Brings SummarizedExperiment to the Tidyverse 
-Version: 1.19.4
+Version: 1.19.5
 Authors@R: c(person("Stefano", "Mangiola", email = "mangiolastefano@gmail.com",
                   role = c("aut", "cre")) )
 Author: Stefano Mangiola [aut, cre] <mangiolastefano@gmail.com>
@@ -19,7 +19,7 @@ License: GPL-3
 Depends:
     R (>= 4.3.0),
     SummarizedExperiment,
-    ttservice (>= 0.4.0)
+    ttservice (>= 0.5.0)
 Imports:
 	dplyr,
     tibble (>= 3.0.4),

NAMESPACE

@@ -1,5 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method(append_samples,SummarizedExperiment)
 S3method(as_tibble,SummarizedExperiment)
 S3method(bind_cols,RangedSummarizedExperiment)
 S3method(bind_cols,SummarizedExperiment)
@@ -132,6 +133,7 @@ importFrom(tidyr,unite)
 importFrom(tidyr,unnest)
 importFrom(tidyselect,eval_select)
 importFrom(tidyselect,one_of)
+importFrom(ttservice,append_samples)
 importFrom(ttservice,bind_cols)
 importFrom(ttservice,bind_rows)
 importFrom(ttservice,plot_ly)

R/dplyr_methods.R

@@ -2,13 +2,7 @@
 #' @rdname bind_rows
 #' @inherit ttservice::bind_rows
 #' @param add.cell.ids Appends the corresponding values to
-#' @examples
-#' data(se)
-#' ttservice::bind_rows(se, se)
-#'
-#' se_bind <- se |> select(dex,  albut)
-#' se |> ttservice::bind_cols(se_bind)
-#' 
+#' @noRd
 #' @importFrom rlang dots_values
 #' @importFrom rlang flatten_if
 #' @importFrom rlang is_spliced
@@ -17,8 +11,15 @@
 #' @importFrom SummarizedExperiment assays<-
 #' @importFrom S4Vectors SimpleList
 #' @importFrom ttservice bind_rows
+#' @importFrom lifecycle deprecate_warn
 #' @export
 bind_rows.SummarizedExperiment <- function(..., .id=NULL, add.cell.ids=NULL) {
+    lifecycle::deprecate_warn(
+        when = "1.19.5",
+        what = "bind_rows()",
+        with = "append_samples()",
+        details = "bind_rows is not a generic method in dplyr and may cause conflicts. Use append_samples from ttservice instead."
+    )
     tts <- flatten_if(dots_values(...), is_spliced)
 
     new_obj <- 
@@ -55,6 +56,55 @@ bind_rows.SummarizedExperiment <- function(..., .id=NULL, add.cell.ids=NULL) {
     new_obj
 }
 
+#' @name append_samples
+#' @rdname append_samples
+#' @title Append samples from multiple SummarizedExperiment objects
+#' 
+#' @description
+#' Append samples from multiple SummarizedExperiment objects by column-binding them.
+#' This function is equivalent to `cbind` but provides a tidyverse-like interface.
+#' 
+#' @param x First SummarizedExperiment object to combine
+#' @param ... Additional SummarizedExperiment objects to combine by samples
+#' @param .id Object identifier (currently not used)
+#' 
+#' @return A combined SummarizedExperiment object
+#' 
+#' @examples
+#' data(se)
+#' append_samples(se, se)
+#' 
+#' @importFrom ttservice append_samples
+#' @importFrom rlang dots_values
+#' @importFrom rlang flatten_if
+#' @importFrom rlang is_spliced
+#' @importFrom SummarizedExperiment cbind
+#' @importFrom SummarizedExperiment assays
+#' @importFrom SummarizedExperiment assays<-
+#' @importFrom S4Vectors SimpleList
+#' @export
+append_samples.SummarizedExperiment <- 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 sample names
+    if (any(duplicated(colnames(new_obj)))) {
+        warning("tidySummarizedExperiment says:",
+                " you have duplicated sample names, they will be made unique.")
+        unique_colnames <- make.unique(colnames(new_obj), sep = "_")
+        colnames(new_obj) <- unique_colnames
+
+        # Change also all assays colnames
+        assays(new_obj) <- assays(new_obj)@listData |> lapply(function(.x) {
+            colnames(.x) <- unique_colnames
+            .x
+        }) |> SimpleList()
+    }
+
+    new_obj
+}
+
 #' @importFrom rlang flatten_if
 #' @importFrom rlang is_spliced
 #' @importFrom rlang dots_values
@@ -1089,3 +1139,5 @@ group_split.SummarizedExperiment <- function(.tbl, ..., .keep = TRUE) {
   }
 
 }
+
+

inst/NEWS.rd

@@ -1,6 +1,14 @@
 \name{NEWS}
 \title{News for Package \pkg{tidySummarizedExperiment}}
 
+\section{Changes in version 1.19.5}{
+\itemize{
+    \item Soft deprecated \code{bind_rows()} in favor of \code{append_samples()} from ttservice.
+    \item Added \code{append_samples()} method for SummarizedExperiment 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 1.19.2, Bioconductor 3.22 Release}{
 \itemize{
     \item Updated documentation to properly reflect S3 method structure.

tests/testthat/test-dplyr_methods.R

@@ -3,15 +3,14 @@ context("dplyr test")
 library(tidySummarizedExperiment)
 
 
-test_that("bind_rows", {
-    pasilla_bind <- bind_rows(pasilla, pasilla)
-
-    pasilla_bind %>%
-        count(.sample, .feature) %>%
-        dplyr::count(n) %>%
-        filter(n > 1) %>%
-        nrow() %>%
-        expect_equal(0)
+test_that("append_samples", {
+    pasilla_bind <- append_samples(pasilla, pasilla)
+
+    # Check that the combined object has the expected number of samples
+    expect_equal(ncol(pasilla_bind), ncol(pasilla) * 2)
+
+    # Check that it's still a SummarizedExperiment
+    expect_true(inherits(pasilla_bind, "SummarizedExperiment"))
 })
 
 test_that("distinct", {

vignettes/introduction.Rmd

@@ -165,6 +165,21 @@ pasilla_tidy %>%
     unite("group", c(condition, type))
 ```
 
+We can use `append_samples` to combine multiple SummarizedExperiment objects by samples. It is equivalent to `cbind` but it is a tidyverse-like function.
+
+```{r}
+# Create two subsets of the data
+pasilla_subset1 <- pasilla_tidy %>%
+    filter(condition == "untreated")
+
+pasilla_subset2 <- pasilla_tidy %>%
+    filter(condition == "treated")
+
+# Combine them using append_samples
+combined_data <- append_samples(pasilla_subset1, pasilla_subset2)
+combined_data
+```
+
 We can also combine commands with the tidyverse pipe `%>%`.
 
 For example, we could combine `group_by` and `summarise` to get the total counts for each sample.