Please always include the output of your sessionInfo() in any issue.

I am facing the same questions here, any solutions now?
Here is the sessionInfo()
sessionInfo()
R version 4.2.1 (2022-06-23 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)

Matrix products: default

locale:
[1] LC_COLLATE=Chinese (Simplified)_China.utf8 LC_CTYPE=Chinese (Simplified)_China.utf8
[3] LC_MONETARY=Chinese (Simplified)_China.utf8 LC_NUMERIC=C
[5] LC_TIME=Chinese (Simplified)_China.utf8

attached base packages:
[1] splines stats4 stats graphics grDevices datasets utils methods base

other attached packages:
[1] scales_1.2.1 scater_1.26.1 scuttle_1.8.4 ggbeeswarm_0.7.2
[5] SeuratWrappers_0.3.4 SingleCellExperiment_1.20.1 SummarizedExperiment_1.28.0 GenomicRanges_1.50.2
[9] GenomeInfoDb_1.34.9 MatrixGenerics_1.10.0 matrixStats_1.0.0 miloR_1.99.12
[13] edgeR_3.40.2 limma_3.54.2 scRNAtoolVis_0.0.7 ClusterGVis_0.1.1
[17] monocle_2.26.0 DDRTree_0.1.5 irlba_2.3.5.1 VGAM_1.1-9
[21] CytoTRACE_0.3.3 SeuratDisk_0.0.0.9021 presto_1.0.0 qs_0.25.7
[25] org.Hs.eg.db_3.16.0 AnnotationDbi_1.60.2 IRanges_2.32.0 S4Vectors_0.36.2
[29] Biobase_2.58.0 BiocGenerics_0.44.0 harmony_1.2.0 Rcpp_1.0.11
[33] SCP_0.5.1 Matrix_1.6-5 UCell_2.2.0 clusterProfiler_4.6.2
[37] AUCell_1.20.2 ggpubr_0.6.0 phylogram_2.1.0 data.table_1.14.8
[41] reshape2_1.4.4 cowplot_1.1.1 vctrs_0.6.3 rlang_1.1.1
[45] patchwork_1.1.3 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.0
[49] dplyr_1.1.2 purrr_1.0.2 readr_2.1.5 tidyr_1.3.0
[53] tibble_3.2.1 ggplot2_3.4.3 tidyverse_2.0.0 Seurat_5.0.3
[57] SeuratObject_5.0.1 sp_2.0-0 RColorBrewer_1.1-3

loaded via a namespace (and not attached):
[1] ggh4x_0.2.8 graphlayouts_1.0.0 pbapply_1.7-2 lattice_0.21-8
[5] GSVA_1.46.0 fastICA_1.2-4 jjAnno_0.0.3 usethis_2.2.2
[9] ggcirclize_0.0.2 blob_1.2.4 survival_3.5-5 nloptr_2.0.3
[13] spatstat.data_3.0-1 later_1.3.1 DBI_1.1.3 R.utils_2.12.2
[17] rappdirs_0.3.3 uwot_0.1.16 jpeg_0.1-10 zlibbioc_1.44.0
[21] htmlwidgets_1.6.2 GlobalOptions_0.1.2 future_1.33.0 hdf5r_1.3.9
[25] leiden_0.4.3 parallel_4.2.1 tidygraph_1.2.3 KernSmooth_2.23-22
[29] promises_1.2.1 DelayedArray_0.24.0 pkgload_1.3.2.1 dbscan_1.1-12
[33] magick_2.8.3 graph_1.76.0 RcppParallel_5.1.7 RSpectra_0.16-1
[37] fs_1.6.3 fastmatch_1.1-4 digest_0.6.33 png_0.1-8
[41] qlcMatrix_0.9.7 sctransform_0.4.1 scatterpie_0.2.1 DOSE_3.24.2
[45] slingshot_2.6.0 ggraph_2.1.0 docopt_0.7.1 pkgconfig_2.0.3
[49] GO.db_3.16.0 spatstat.random_3.1-5 ggnewscale_0.4.9 DelayedMatrixStats_1.20.0
[53] minqa_1.2.6 iterators_1.0.14 reticulate_1.31 circlize_0.4.15
[57] spam_2.9-1 beeswarm_0.4.0 GetoptLong_1.0.5 zoo_1.8-12
[61] tidyselect_1.2.0 ica_1.0-3 gson_0.1.0 viridisLite_0.4.2
[65] pkgbuild_1.4.2 glue_1.6.2 EBImage_4.40.1 TrajectoryUtils_1.6.0
[69] monocle3_1.3.5 ggsignif_0.6.4 httpuv_1.6.11 BiocNeighbors_1.16.0
[73] annotate_1.76.0 jsonlite_1.8.7 XVector_0.38.0 bit_4.0.5
[77] mime_0.12 princurve_2.1.6 gridExtra_2.3 gplots_3.1.3
[81] Rsamtools_2.16.0 stringi_1.7.12 processx_3.8.2 RcppRoll_0.3.0
[85] spatstat.sparse_3.0-2 scattermore_1.2 spatstat.explore_3.2-1 yulab.utils_0.0.9
[89] bitops_1.0-7 cli_3.6.1 rhdf5filters_1.10.1 RSQLite_2.3.1
[93] pheatmap_1.0.12 timechange_0.3.0 org.Mm.eg.db_3.16.0 rstudioapi_0.15.0
[97] fftwtools_0.9-11 nlme_3.1-162 qvalue_2.30.0 fastcluster_1.2.6
[101] locfit_1.5-9.8 listenv_0.9.0 miniUI_0.1.1.1 leidenbase_0.1.27
[105] gridGraphics_0.5-1 R.oo_1.25.0 urlchecker_1.0.1 dbplyr_2.3.3
[109] sessioninfo_1.2.2 lifecycle_1.0.3 munsell_0.5.0 R.methodsS3_1.8.2
[113] ggsci_3.0.0 visNetwork_2.1.2 caTools_1.18.2 codetools_0.2-19
[117] magic_1.6-1 ggSCvis_0.0.2 vipor_0.4.7 lmtest_0.9-40
[121] msigdbr_7.5.1 xtable_1.8-4 ROCR_1.0-11 BiocManager_1.30.21.1
[125] Signac_1.10.0 abind_1.4-5 farver_2.1.1 parallelly_1.36.0
[129] RANN_2.6.1 aplot_0.2.0 tiff_0.1-12 sparsesvd_0.2-2
[133] parallelDist_0.2.6 ggtree_3.9.1 philentropy_0.8.0 RcppAnnoy_0.0.21
[137] goftest_1.2-3 packcircles_0.3.6 ggdendro_0.1.23 profvis_0.3.8
[141] cluster_2.1.4 future.apply_1.11.0 tidytree_0.4.5 ellipsis_0.3.2
[145] prettyunits_1.1.1 ggridges_0.5.4 igraph_1.5.1 fgsea_1.24.0
[149] slam_0.1-50 remotes_2.4.2.1 spatstat.utils_3.0-3 geometry_0.4.7
[153] htmltools_0.5.6 BiocFileCache_2.6.1 utf8_1.2.3 plotly_4.10.2
[157] XML_3.99-0.14 withr_2.5.0 fitdistrplus_1.1-11 BiocParallel_1.32.6
[161] bit64_4.0.5 foreach_1.5.2 Biostrings_2.66.0 combinat_0.0-8
[165] progressr_0.14.0 GOSemSim_2.24.0 data.tree_1.1.0 rsvd_1.0.5
[169] ScaledMatrix_1.6.0 devtools_2.4.5 memoise_2.0.1 RApiSerialize_0.1.2
[173] tzdb_0.4.0 callr_3.7.3 ps_1.7.5 curl_5.0.2
[177] DiagrammeR_1.0.11 fansi_1.0.4 fastDummies_1.7.3 GSEABase_1.60.0
[181] tensor_1.5 CellTrek_0.0.94 renv_1.0.2 cachem_1.0.8
[185] desc_1.4.2 randomForestSRC_3.2.3 deldir_1.0-9 HDO.db_0.99.1
[189] babelgene_22.9 rjson_0.2.21 rstatix_0.7.2 ggrepel_0.9.3
[193] rprojroot_2.0.3 clue_0.3-64 tools_4.2.1 magrittr_2.0.3
[197] RCurl_1.98-1.12 car_3.1-2 ape_5.7-1 ggplotify_0.1.2
[201] xml2_1.3.5 httr_1.4.7 boot_1.3-28.1 globals_0.16.2
[205] R6_2.5.1 Rhdf5lib_1.20.0 RcppHNSW_0.6.0 progress_1.2.2
[209] KEGGREST_1.38.0 treeio_1.25.4 gtools_3.9.4 shape_1.4.6
[213] akima_0.6-3.4 beachmat_2.14.2 HDF5Array_1.26.0 BiocSingular_1.14.0
[217] ggrastr_1.0.2 rhdf5_2.42.1 carData_3.0-5 ggfun_0.1.2
[221] colorspace_2.1-0 generics_0.1.3 pillar_1.9.0 tweenr_2.0.2
[225] HSMMSingleCell_1.18.0 R.cache_0.16.0 GenomeInfoDbData_1.2.9 plyr_1.8.8
[229] dotCall64_1.0-2 gtable_0.3.4 stringfish_0.16.0 ComplexHeatmap_2.15.4
[233] shadowtext_0.1.2 biomaRt_2.54.1 fastmap_1.1.1 doParallel_1.0.17
[237] broom_1.0.5 filelock_1.0.2 backports_1.4.1 lme4_1.1-35.1
[241] enrichplot_1.18.4 hms_1.1.3 ggforce_0.4.1 Rtsne_0.16
[245] shiny_1.7.5 polyclip_1.10-4 grid_4.2.1 numDeriv_2016.8-1.1
[249] lazyeval_0.2.2 dynamicTreeCut_1.63-1 crayon_1.5.2 MASS_7.3-60
[253] downloader_0.4 sparseMatrixStats_1.10.0 viridis_0.6.5 compiler_4.2.1
[257] spatstat.geom_3.2-4

Looking for your reply,
Best wishes!

The simplest solution is that you don't need to calculate nhood distances any more. Use the newer refinement_scheme="graph" for makeNhoods and fdr.weighting="graph-overlap" for testNhoods.

output of your sessionInfo() in any issue.

It's not work for me, the same error still comes up.
R version 4.3.1 (2023-06-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.6 LTS

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

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

time zone: Asia/Shanghai
tzcode source: system (glibc)

attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets methods base

other attached packages:
[1] miloR_2.0.0 scales_1.3.0 scater_1.28.0
[4] scuttle_1.10.3 SeuratWrappers_0.3.5 Seurat_5.1.0
[7] ggpubr_0.6.0 ggbeeswarm_0.7.2 MASS_7.3-60.0.1
[10] viridis_0.6.5 viridisLite_0.4.2 ggforce_0.4.2
[13] ggrepel_0.9.5 patchwork_1.2.0 ggh4x_0.2.8
[16] lubridate_1.9.3 forcats_1.0.0 purrr_1.0.2
[19] tibble_3.2.1 tidyverse_2.0.0 circlize_0.4.16
[22] ComplexHeatmap_2.16.0 enrichplot_1.20.3 stringr_1.5.1
[25] tidyr_1.3.1 COSG_0.9.0 AUCell_1.22.0
[28] MAST_1.26.0 edgeR_3.42.4 DESeq2_1.40.2
[31] limma_3.56.2 GSEABase_1.62.0 graph_1.78.0
[34] annotate_1.78.0 XML_3.99-0.16.1 GSVA_1.48.3
[37] org.Hs.eg.db_3.17.0 AnnotationDbi_1.62.2 gplots_3.1.3.1
[40] clusterProfiler_4.8.3 data.table_1.15.4 fgsea_1.26.0
[43] readr_2.1.5 Hmisc_5.1-3 ggsci_3.2.0
[46] gtools_3.9.5 ggalluvial_0.12.5 NMF_0.27
[49] cluster_2.1.6 rngtools_1.5.2 registry_0.5-1
[52] igraph_2.0.3 qs_0.26.3 clustree_0.5.1
[55] ggraph_2.2.1 dplyr_1.1.4 ggplot2_3.5.1
[58] SeuratObject_5.0.2 sp_2.1-4 monocle3_1.3.7
[61] SingleCellExperiment_1.22.0 SummarizedExperiment_1.30.2 GenomicRanges_1.52.1
[64] GenomeInfoDb_1.36.4 IRanges_2.34.1 S4Vectors_0.38.2
[67] MatrixGenerics_1.12.3 matrixStats_1.4.0 bigmemory_4.6.4
[70] Biobase_2.60.0 BiocGenerics_0.46.0

loaded via a namespace (and not attached):
[1] ica_1.0-3 plotly_4.10.4 Formula_1.2-5
[4] devtools_2.4.5 zlibbioc_1.46.0 tidyselect_1.2.1
[7] bit_4.0.5 doParallel_1.0.17 clue_0.3-65
[10] lattice_0.22-6 rjson_0.2.21 blob_1.2.4
[13] urlchecker_1.0.1 S4Arrays_1.2.0 parallel_4.3.1
[16] png_0.1-8 cli_3.6.3 ggplotify_0.1.2
[19] goftest_1.2-3 BiocNeighbors_1.18.0 uwot_0.2.2
[22] shadowtext_0.1.4 curl_5.2.1 mime_0.12
[25] evaluate_0.24.0 tidytree_0.4.6 leiden_0.4.3.1
[28] stringi_1.8.4 backports_1.5.0 desc_1.4.3
[31] httpuv_1.6.15 magrittr_2.0.3 splines_4.3.1
[34] RApiSerialize_0.1.3 sctransform_0.4.1 sessioninfo_1.2.2
[37] DBI_1.2.3 HDF5Array_1.28.1 withr_3.0.1
[40] lmtest_0.9-40 tidygraph_1.3.1 BiocManager_1.30.23
[43] htmlwidgets_1.6.4 fs_1.6.4 reticulate_1.38.0
[46] zoo_1.8-12 XVector_0.40.0 knitr_1.48
[49] timechange_0.3.0 foreach_1.5.2 fansi_1.0.6
[52] caTools_1.18.2 ggtree_3.8.2 rhdf5_2.44.0
[55] R.oo_1.26.0 RSpectra_0.16-2 irlba_2.3.5.1
[58] fastDummies_1.7.3 gridGraphics_0.5-1 ellipsis_0.3.2
[61] lazyeval_0.2.2 survival_3.7-0 scattermore_1.2
[64] crayon_1.5.3 RcppAnnoy_0.0.22 RColorBrewer_1.1-3
[67] progressr_0.14.0 tweenr_2.0.3 later_1.3.2
[70] ggridges_0.5.6 codetools_0.2-20 base64enc_0.1-3
[73] GlobalOptions_0.1.2 profvis_0.3.8 KEGGREST_1.40.1
[76] Rtsne_0.17 shape_1.4.6.1 foreign_0.8-87
[79] pkgconfig_2.0.3 spatstat.univar_3.0-0 aplot_0.2.3
[82] spatstat.sparse_3.1-0 ape_5.8 gridBase_0.4-7
[85] xtable_1.8-4 Genshinpalette_0.0.1.9000 car_3.1-2
[88] plyr_1.8.9 httr_1.4.7 tools_4.3.1
[91] globals_0.16.3 pkgbuild_1.4.4 beeswarm_0.4.0
[94] htmlTable_2.4.3 broom_1.0.6 checkmate_2.3.2
[97] nlme_3.1-165 HDO.db_0.99.1 lme4_1.1-35.5
[100] digest_0.6.36 numDeriv_2016.8-1.1 Matrix_1.6-5
[103] farver_2.1.2 tzdb_0.4.0 reshape2_1.4.4
[106] yulab.utils_0.1.5 rpart_4.1.23 glue_1.7.0
[109] cachem_1.1.0 polyclip_1.10-7 generics_0.1.3
[112] Biostrings_2.68.1 parallelly_1.38.0 pkgload_1.4.0
[115] RcppHNSW_0.6.0 ScaledMatrix_1.8.1 carData_3.0-5
[118] minqa_1.2.7 pbapply_1.7-2 job_0.3.1
[121] spam_2.10-0 gson_0.1.0 utf8_1.2.4
[124] graphlayouts_1.1.1 ggsignif_0.6.4 gridExtra_2.3
[127] shiny_1.9.1 GenomeInfoDbData_1.2.10 R.utils_2.12.3
[130] rhdf5filters_1.12.1 RCurl_1.98-1.16 memoise_2.0.1
[133] rmarkdown_2.27 downloader_0.4 R.methodsS3_1.8.2
[136] future_1.34.0 RANN_2.6.1 stringfish_0.16.0
[139] bigmemory.sri_0.1.8 spatstat.data_3.1-2 rstudioapi_0.16.0
[142] spatstat.utils_3.0-5 hms_1.1.3 fitdistrplus_1.2-1
[145] munsell_0.5.1 cowplot_1.1.3 colorspace_2.1-1
[148] rlang_1.1.4 DelayedMatrixStats_1.22.6 sparseMatrixStats_1.12.2
[151] dotCall64_1.1-1 xfun_0.46 remotes_2.5.0
[154] iterators_1.0.14 abind_1.4-5 GOSemSim_2.26.1
[157] treeio_1.24.3 Rhdf5lib_1.22.1 bitops_1.0-8
[160] ps_1.7.7 promises_1.3.0 scatterpie_0.2.3
[163] RSQLite_2.3.7 qvalue_2.32.0 DelayedArray_0.26.7
[166] GO.db_3.17.0 compiler_4.3.1 boot_1.3-30
[169] beachmat_2.16.0 listenv_0.9.1 Rcpp_1.0.13
[172] BiocSingular_1.16.0 tensor_1.5 usethis_3.0.0
[175] uuid_1.2-1 BiocParallel_1.34.2 spatstat.random_3.3-1
[178] R6_2.5.1 fastmap_1.2.0 fastmatch_1.1-4
[181] rstatix_0.7.2 vipor_0.4.7 ROCR_1.0-11
[184] rsvd_1.0.5 nnet_7.3-19 gtable_0.3.5
[187] KernSmooth_2.23-24 miniUI_0.1.1.1 deldir_2.0-4
[190] htmltools_0.5.8.1 RcppParallel_5.1.8 bit64_4.0.5
[193] spatstat.explore_3.3-1 lifecycle_1.0.4 processx_3.8.4
[196] nloptr_2.1.1 callr_3.7.6 vctrs_0.6.5
[199] spatstat.geom_3.3-2 DOSE_3.26.2 ggfun_0.1.5
[202] future.apply_1.11.2 pracma_2.4.4 pillar_1.9.0
[205] locfit_1.5-9.10 jsonlite_1.8.8 GetoptLong_1.0.5

Make sure you are using up to date versions of all relevant packages, including Matrix and matrixStats

Hi! Facing the same issue here. When I run
data_T1_milo <- calcNhoodDistance(data_T1_milo, d=30, reduced.dim = "PCA")
I get
Error in Matrix.DeprecatedCoerce(cd1, cd2) : (converted from warning) 'as(<dgTMatrix>, "dgCMatrix")' is deprecated. Use 'as(., "CsparseMatrix")' instead. See help("Deprecated") and help("Matrix-deprecated").
My sessionInfo() is the following one:
`R version 4.4.1 (2024-06-14)
Platform: x86_64-apple-darwin20
Running under: macOS Sonoma 14.2.1

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/Madrid
tzcode source: internal

attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods base

other attached packages:
[1] Matrix_1.7-0 patchwork_1.2.0.9000 scater_1.32.1
[4] scran_1.32.0 scuttle_1.14.0 SingleCellExperiment_1.26.0
[7] SummarizedExperiment_1.34.0 Biobase_2.64.0 GenomicRanges_1.56.1
[10] GenomeInfoDb_1.40.1 IRanges_2.38.1 S4Vectors_0.42.1
[13] BiocGenerics_0.50.0 MatrixGenerics_1.16.0 matrixStats_1.4.1
[16] miloR_2.0.0 edgeR_4.2.1 limma_3.60.4
[19] GSVA_1.52.3 escape_2.1.2 RColorBrewer_1.1-3
[22] scRepertoire_2.0.5 dplyr_1.1.4 plyr_1.8.9
[25] ggplot2_3.5.1 Seurat_5.1.0 SeuratObject_5.0.2
[28] sp_2.1-4

loaded via a namespace (and not attached):
[1] spatstat.sparse_3.1-0 httr_1.4.7 numDeriv_2016.8-1.1
[4] tools_4.4.1 sctransform_0.4.1 utf8_1.2.4
[7] R6_2.5.1 HDF5Array_1.30.1 ggdist_3.3.2
[10] lazyeval_0.2.2 uwot_0.2.2 rhdf5filters_1.14.1
[13] withr_3.0.1 gridExtra_2.3 progressr_0.14.0
[16] quantreg_5.98 cli_3.6.3 textshaping_0.4.0
[19] spatstat.explore_3.3-2 fastDummies_1.7.4 iNEXT_3.0.1
[22] labeling_0.4.3 spatstat.data_3.1-2 ggridges_0.5.6
[25] pbapply_1.7-2 systemfonts_1.1.0 R.utils_2.12.3
[28] stringdist_0.9.12 parallelly_1.38.0 VGAM_1.1-11
[31] rstudioapi_0.16.0 RSQLite_2.3.7 generics_0.1.3
[34] gtools_3.9.5 ica_1.0-3 spatstat.random_3.3-1
[37] distributional_0.4.0 ggbeeswarm_0.7.2 fansi_1.0.6
[40] abind_1.4-5 R.methodsS3_1.8.2 lifecycle_1.0.4
[43] yaml_2.3.10 rhdf5_2.46.1 SparseArray_1.4.8
[46] Rtsne_0.17 grid_4.4.1 blob_1.2.4
[49] dqrng_0.4.1 promises_1.3.0 crayon_1.5.3
[52] miniUI_0.1.1.1 lattice_0.22-6 msigdbr_7.5.1
[55] beachmat_2.20.0 cowplot_1.1.3 annotate_1.80.0
[58] KEGGREST_1.44.1 magick_2.8.4 metapod_1.12.0
[61] pillar_1.9.0 knitr_1.48 rjson_0.2.22
[64] future.apply_1.11.2 codetools_0.2-20 leiden_0.4.3.1
[67] glue_1.7.0 spatstat.univar_3.0-0 data.table_1.16.0
[70] vctrs_0.6.5 png_0.1-8 spam_2.10-0
[73] gtable_0.3.5 assertthat_0.2.1 cachem_1.1.0
[76] xfun_0.47 S4Arrays_1.4.1 mime_0.12
[79] tidygraph_1.3.1 survival_3.7-0 bluster_1.14.0
[82] statmod_1.5.0 fitdistrplus_1.2-1 ROCR_1.0-11
[85] nlme_3.1-166 bit64_4.0.5 RcppAnnoy_0.0.22
[88] evd_2.3-7 irlba_2.3.5.1 vipor_0.4.7
[91] KernSmooth_2.23-24 colorspace_2.1-1 DBI_1.2.3
[94] UCell_2.6.2 ggrastr_1.0.2 tidyselect_1.2.1
[97] bit_4.0.5 compiler_4.4.1 AUCell_1.24.0
[100] graph_1.80.0 BiocNeighbors_1.22.0 SparseM_1.84-2
[103] ggdendro_0.2.0 DelayedArray_0.30.1 plotly_4.10.4
[106] scales_1.3.0 lmtest_0.9-40 SpatialExperiment_1.12.0
[109] stringr_1.5.1 digest_0.6.37 goftest_1.2-3
[112] spatstat.utils_3.1-0 rmarkdown_2.28 XVector_0.44.0
[115] htmltools_0.5.8.1 pkgconfig_2.0.3 sparseMatrixStats_1.16.0
[118] fastmap_1.2.0 rlang_1.1.4 htmlwidgets_1.6.4
[121] UCSC.utils_1.0.0 DelayedMatrixStats_1.26.0 shiny_1.9.1
[124] farver_2.1.2 zoo_1.8-12 jsonlite_1.8.8
[127] BiocParallel_1.38.0 R.oo_1.26.0 BiocSingular_1.20.0
[130] magrittr_2.0.3 GenomeInfoDbData_1.2.12 dotCall64_1.1-1
[133] Rhdf5lib_1.24.2 munsell_0.5.1 Rcpp_1.0.13
[136] evmix_2.12 babelgene_22.9 viridis_0.6.5
[139] reticulate_1.38.0 truncdist_1.0-2 stringi_1.8.4
[142] ggalluvial_0.12.5 ggraph_2.2.1 zlibbioc_1.50.0
[145] MASS_7.3-61 parallel_4.4.1 listenv_0.9.1
[148] ggrepel_0.9.5 deldir_2.0-4 Biostrings_2.72.1
[151] graphlayouts_1.1.1 splines_4.4.1 tensor_1.5
[154] locfit_1.5-9.10 igraph_2.0.3 spatstat.geom_3.3-2
[157] cubature_2.1.1 RcppHNSW_0.6.0 reshape2_1.4.4
[160] ScaledMatrix_1.12.0 XML_3.99-0.17 evaluate_0.24.0
[163] tweenr_2.0.3 httpuv_1.6.15 MatrixModels_0.5-3
[166] RANN_2.6.2 tidyr_1.3.1 purrr_1.0.2
[169] polyclip_1.10-7 future_1.34.0 scattermore_1.2
[172] ggforce_0.4.2 rsvd_1.0.5 xtable_1.8-4
[175] RSpectra_0.16-2 later_1.3.2 ggpointdensity_0.1.0
[178] viridisLite_0.4.2 ragg_1.3.2 gsl_2.1-8
[181] tibble_3.2.1 memoise_2.0.1 beeswarm_0.4.0
[184] AnnotationDbi_1.66.0 cluster_2.1.6 globals_0.16.3
[187] GSEABase_1.64.0 `

And in the easiest solution that @MikeDMorgan commented about using the newer refinement_scheme="graph" for makeNhoods and fdr.weighting="graph-overlap" for testNhoods, should I just not run calcNhoodDistance() function and proceed with the pipeline?

Thank you so much for your time and for this incredible tool,

Aina

Hi @ainarill , yes you no longer need to run calcNhoodDistances when using the graph-based refinement and spatial FDR correction.