Fixup smoke test for FindClusters

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: Seurat
-Version: 5.2.0
+Version: 5.2.0.9001
 Title: Tools for Single Cell Genomics
 Description: A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031>, and Hao, Hao, et al (2020) <doi:10.1101/2020.10.12.335331> for more details.
 Authors@R: c(

NEWS.md

@@ -1,3 +1,8 @@
+# Unreleased
+
+## Changes
+- Fixed `test_find_clusters.R` to accommodate variability in label assignments given by `FindClusters` across different systems ([#9641](https://github.com/satijalab/seurat/pull/9641))
+
 # Seurat 5.2.0 (2024-12-20)
 
 ## Changes

tests/testthat/test_find_clusters.R

@@ -34,13 +34,17 @@ context("FindClusters")
 test_that("Smoke test for `FindClusters`", {
   test_case <- get_test_data()
 
-  # Spot check cluster assignments with using defaults.
+  # Validate cluster assignments using default parameters.
   results <- FindClusters(test_case)$seurat_clusters
-  expect_equal(results[[1]], factor(3, levels=0:5))
-  expect_equal(results[[15]], factor(4, levels=0:5))
-  expect_equal(results[[24]], factor(0, levels=0:5))
-  expect_equal(results[[72]], factor(5, levels=0:5))
-  expect_equal(results[[length(results)]], factor(2, levels=0:5))
+  # Check that every cell was assigned to a cluster label.
+  expect_false(any(is.na(results)))
+  # Check that the expected cluster labels were assigned.
+  expect_equal(as.numeric(levels(results)), c(0, 1, 2, 3, 4, 5))
+  # Check that the cluster sizes match the expected distribution.
+  expect_equal(
+    as.numeric(sort(table(results))),
+    c(9, 10, 10, 11, 20, 20)
+  )
 
   # Check that every clustering algorithm can be run without errors.
   expect_no_error(FindClusters(test_case, algorithm = 1))