Error: x.cell_ID is type environment which is not supported by data.table join in doLeidenCluster function

[yuhan1li]

Description
I encountered an error while running the doLeidenCluster function on my visium_brain object. The error message is as follows:
Error: x.cell_ID is type environment which is not supported by data.table join

Steps to Reproduce
Here’s the code snippet that caused the error:
visium_brain <- doLeidenCluster(
gobject = visium_brain,
resolution = 0.4,
n_iterations = 1000
)

Expected Behavior
I expected the function to run without errors and return the updated visium_brain object with Leiden clustering results.
What I Have Tried：
1、Verified that the visium_brain object is correctly initialized.
2、Checked that all required libraries are loaded.
3、Attempted to inspect the x.cell_ID attribute in visium_brain,
class(visium_brain@cell_ID)
[1] "list"

class(visium_brain@cell_ID[["cell"]])
[1] "character"

Session Info
Here’s my session information for reference:

sessionInfo()
R version 4.4.0 (2024-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0

locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats graphics grDevices utils datasets methods base

other attached packages:
[1] reticulate_1.37.0 patchwork_1.2.0 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1
[6] dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
[11] ggplot2_3.5.1 tidyverse_2.0.0 Seurat_5.1.0 SeuratObject_5.0.2 sp_2.1-4
[16] Giotto_4.1.0 GiottoClass_0.3.3 GiottoVisuals_0.2.4

loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 rstudioapi_0.16.0 jsonlite_1.8.8 magrittr_2.0.3 spatstat.utils_3.0-4
[6] magick_2.8.3 zlibbioc_1.52.0 vctrs_0.6.5 ROCR_1.0-11 spatstat.explore_3.2-7
[11] GiottoUtils_0.1.11 terra_1.7-78 htmltools_0.5.8.1 sctransform_0.4.1 parallelly_1.37.1
[16] KernSmooth_2.23-22 htmlwidgets_1.6.4 ica_1.0-3 plyr_1.8.9 plotly_4.10.4
[21] zoo_1.8-12 igraph_2.0.3 mime_0.12 lifecycle_1.0.4 pkgconfig_2.0.3
[26] Matrix_1.7-0 R6_2.5.1 fastmap_1.2.0 fitdistrplus_1.1-11 future_1.33.2
[31] shiny_1.8.1.1 digest_0.6.35 colorspace_2.1-0 S4Vectors_0.44.0 tensor_1.5
[36] RSpectra_0.16-1 irlba_2.3.5.1 progressr_0.14.0 fansi_1.0.6 spatstat.sparse_3.0-3
[41] timechange_0.3.0 httr_1.4.7 polyclip_1.10-6 abind_1.4-5 compiler_4.4.0
[46] withr_3.0.0 backports_1.5.0 fastDummies_1.7.3 R.utils_2.12.3 MASS_7.3-60.2
[51] gtools_3.9.5 tools_4.4.0 lmtest_0.9-40 httpuv_1.6.15 future.apply_1.11.2
[56] goftest_1.2-3 R.oo_1.26.0 glue_1.7.0 dbscan_1.1-12 nlme_3.1-164
[61] promises_1.3.0 grid_4.4.0 checkmate_2.3.1 Rtsne_0.17 cluster_2.1.6
[66] reshape2_1.4.4 generics_0.1.3 gtable_0.3.5 spatstat.data_3.0-4 tzdb_0.4.0
[71] R.methodsS3_1.8.2 hms_1.1.3 data.table_1.15.4 utf8_1.2.4 XVector_0.46.0
[76] BiocGenerics_0.52.0 spatstat.geom_3.2-9 RcppAnnoy_0.0.22 ggrepel_0.9.5 RANN_2.6.1
[81] pillar_1.9.0 spam_2.10-0 RcppHNSW_0.6.0 later_1.3.2 splines_4.4.0
[86] lattice_0.22-6 survival_3.5-8 deldir_2.0-4 tidyselect_1.2.1 miniUI_0.1.1.1
[91] pbapply_1.7-2 gridExtra_2.3 IRanges_2.40.1 scattermore_1.2 stats4_4.4.0
[96] matrixStats_1.4.1 stringi_1.8.4 lazyeval_0.2.2 codetools_0.2-20 colorRamp2_0.1.0
[101] cli_3.6.2 uwot_0.2.2 xtable_1.8-4 munsell_0.5.1 Rcpp_1.0.12
[106] spatstat.random_3.2-3 globals_0.16.3 png_0.1-8 parallel_4.4.0 dotCall64_1.1-1
[111] listenv_0.9.1 viridisLite_0.4.2 scales_1.3.0 ggridges_0.5.6 leiden_0.4.3.1
[116] rlang_1.1.4 cowplot_1.1.3

Reproducible Example
If necessary, I can provide a reproducible example, though the issue seems related to how x.cell_ID is handled internally by doLeidenCluster.

Additional Context
I’m using the doLeidenCluster function from the Giotto package. My object visium_brain was created and processed following the steps in the package documentation.

Questions
Is there a specific preprocessing step I might have missed that causes x.cell_ID to be an environment type?
Are there any known compatibility issues with data.table or doLeidenCluster in recent package versions?

[RubD]

Can you share minimal code to reproduce the problem? I believe you might have done some additional steps that causes this issue.

[yuhan1li]

Can you share minimal code to reproduce the problem? I believe you might have done some additional steps that causes this issue.

Thank you for your response! Below is the minimal code I used to reproduce the issue.

The data path I am using is ./k11.Giotto/数据/10x_brain/, and my Python environment path is /home/liuhuilin2/anaconda3/envs/giotto_env/bin/python. Here’s the code:
`
library(Giotto)
library(Seurat)
library(tidyverse)
library(patchwork)
##Create Giotto Instructions
python_path <- "/home/liuhuilin2/anaconda3/envs/giotto_env/bin/python"
instructions <- createGiottoInstructions(save_dir = "./out_example/",
save_plot = TRUE,
show_plot = FALSE,
return_plot = FALSE,
python_path = python_path)
##provide path to visium folder
data_path <- "./k11.Giotto/数据/10x_brain/"

##directly from visium folder
visium_brain <- createGiottoVisiumObject(visium_dir = data_path,
expr_data = "filter",
png_name = "tissue_lowres_image.png",
gene_column_index = 2,
instructions = instructions)

##subset on spots that were covered by tissue
metadata <- pDataDT(visium_brain)
in_tissue_barcodes <- metadata[in_tissue == 1]$cell_ID

visium_brain <- subsetGiotto(visium_brain,
cell_ids = in_tissue_barcodes)

##filter
visium_brain <- filterGiotto(gobject = visium_brain,
expression_threshold = 1,
feat_det_in_min_cells = 50,
min_det_feats_per_cell = 1000,
expression_values = "raw",
verbose = TRUE)

normalize

visium_brain <- normalizeGiotto(gobject = visium_brain,
scalefactor = 6000,
verbose = TRUE)

add gene & cell statistics

visium_brain <- addStatistics(gobject = visium_brain)

5 highly variable features / genes (HVF)

visium_brain <- calculateHVF(gobject = visium_brain,
save_plot = TRUE)

##run PCA on expression values (default)
gene_metadata <- fDataDT(visium_brain)
featgenes <- gene_metadata[hvf == "yes" & perc_cells > 3 & mean_expr_det > 0.4]$feat_ID

run PCA on expression values (default)

visium_brain <- runPCA(gobject = visium_brain,
feats_to_use = featgenes)

run UMAP and tSNE on PCA space (default)

visium_brain <- runUMAP(visium_brain,
dimensions_to_use = 1:10)

plotUMAP(gobject = visium_brain)

visium_brain <- runtSNE(visium_brain,
dimensions_to_use = 1:10)

plotTSNE(gobject = visium_brain)

6 Clustering

SNN network (default)

visium_brain <- createNearestNetwork(gobject = visium_brain,
dimensions_to_use = 1:10,
k = 15)

visium_brain <- doLeidenCluster(gobject = visium_brain,
resolution = 0.4,
n_iterations = 1000)

` The above code runs smoothly until the final step.