Error: (converted from warning) 'as(<dgTMatrix>, "dgCMatrix")' is deprecated.

[Lil-5]

sce_milo <- calcNhoodDistance(sce_milo, d=50)
Error: (converted from warning) 'as(, "dgCMatrix")' is deprecated.
Use 'as(., "CsparseMatrix")' instead.
See help("Deprecated") and help("Matrix-deprecated").

My seurat version is 4.4.0 and matrix version is 1.6-1.1

[MikeDMorgan]

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

[Lil-5]

Thank you for the information. Your R version is 4.3.2 and the miloR version is 1.99.12. 小伍小伍 ***@***.*** &nbsp;

…

------------------&nbsp;原始邮件&nbsp;------------------ 发件人: "MarioniLab/miloR" ***@***.***&gt;; 发送时间:&nbsp;2024年3月15日(星期五) 下午4:44 ***@***.***&gt;; ***@***.******@***.***&gt;; 主题:&nbsp;Re: [MarioniLab/miloR] Error: (converted from warning) 'as(<dgTMatrix&gt;, "dgCMatrix")' is deprecated. (Issue #314) Please always include the output of your sessionInfo() in any issue. — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you authored the thread.Message ID: ***@***.***&gt;

[DingtaoHu]

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!

[MikeDMorgan]

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.