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Running refined sampling with graph
Error in neighborhood(X_graph, order = 1, nodes = random_vertices[i]) :
INTEGER() can only be applied to a 'integer', not a 'double'
milo <- makeNhoods(milo, prop = 0.1, k = 15, d=30, refined = TRUE)
Checking valid object
Running refined sampling with reduced_dim
Error in h(simpleError(msg, call)) :
error in evaluating the argument 'i' in selecting a method for function '[<-': INTEGER() can only be applied to a 'integer', not a 'double'
Session info
Output of sessionInfo()
sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS
Describe the bug
Hi
I am trying to run makeNhoods with different parameters, and end u with the same error
Minimum code example
milo <- makeNhoods(milo, prop = 0.1, k = 15, d=30, refined = TRUE, refinement_scheme = 'graph')
Checking valid object
Running refined sampling with graph
Error in neighborhood(X_graph, order = 1, nodes = random_vertices[i]) :
INTEGER() can only be applied to a 'integer', not a 'double'
milo <- makeNhoods(milo, prop = 0.1, k = 15, d=30, refined = TRUE)
Checking valid object
Running refined sampling with reduced_dim
Error in h(simpleError(msg, call)) :
error in evaluating the argument 'i' in selecting a method for function '[<-': INTEGER() can only be applied to a 'integer', not a 'double'
Session info
Output of
sessionInfo()
sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS
Matrix products: default
BLAS: /opt/R-4.3.1/lib/libRblas.so
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.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 LC_MONETARY=en_US.UTF-8
[6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Europe/Copenhagen
tzcode source: system (glibc)
attached base packages:
[1] stats4 parallel grid stats graphics grDevices utils datasets methods base
other attached packages:
[1] igraph_2.0.1.1 Seurat_4.4.0 patchwork_1.2.0 scuttle_1.10.3 SingleCellExperiment_1.22.0
[6] SummarizedExperiment_1.30.2 Biobase_2.60.0 GenomicRanges_1.52.1 GenomeInfoDb_1.36.4 IRanges_2.34.1
[11] S4Vectors_0.38.2 BiocGenerics_0.46.0 MatrixGenerics_1.12.3 matrixStats_1.2.0 miloR_1.8.1
[16] edgeR_3.42.4 limma_3.56.2 performance_0.10.8 selectiveInference_1.2.5 MASS_7.3-60
[21] adaptMCMC_1.5 coda_0.19-4.1 survival_3.5-8 intervals_0.15.4 boot_1.3-28.1
[26] glmnet_4.1-8 Matrix_1.6-5 ggrepel_0.9.4 harmony_1.2.0 Rcpp_1.0.12
[31] circlize_0.4.15 ComplexHeatmap_2.16.0 SeuratObject_4.1.4 lubridate_1.9.3 forcats_1.0.0
[36] stringr_1.5.1 dplyr_1.1.3 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1
[41] tibble_3.2.1 ggplot2_3.4.4 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] spatstat.sparse_3.0-3 bitops_1.0-7 httr_1.4.7 RColorBrewer_1.1-3 insight_0.19.8
[6] doParallel_1.0.17 tools_4.3.1 sctransform_0.4.1 backports_1.4.1 utf8_1.2.4
[11] R6_2.5.1 lazyeval_0.2.2 uwot_0.1.16 GetoptLong_1.0.5 withr_3.0.0
[16] sp_2.1-1 gridExtra_2.3 progressr_0.14.0 cli_3.6.2 spatstat.explore_3.2-5
[21] labeling_0.4.3 spatstat.data_3.0-4 ggridges_0.5.6 pbapply_1.7-2 pscl_1.5.9
[26] parallelly_1.36.0 readxl_1.4.3 rstudioapi_0.15.0 FNN_1.1.4 generics_0.1.3
[31] shape_1.4.6 gtools_3.9.5 ica_1.0-3 spatstat.random_3.2-1 car_3.1-2
[36] ggbeeswarm_0.7.2 fansi_1.0.5 abind_1.4-5 lifecycle_1.0.4 carData_3.0-5
[41] Rtsne_0.17 dqrng_0.3.2 promises_1.2.1 crayon_1.5.2 miniUI_0.1.1.1
[46] lattice_0.22-5 beachmat_2.16.0 cowplot_1.1.3 metapod_1.8.0 pillar_1.9.0
[51] rjson_0.2.21 future.apply_1.11.1 codetools_0.2-19 leiden_0.4.3.1 glue_1.7.0
[56] ggvenn_0.1.10 data.table_1.15.0 remotes_2.4.2.1 vctrs_0.6.4 png_0.1-8
[61] cellranger_1.1.0 gtable_0.3.4 S4Arrays_1.0.6 mime_0.12 tidygraph_1.3.1
[66] iterators_1.0.14 ramify_0.3.3 statmod_1.5.0 bluster_1.10.0 ellipsis_0.3.2
[71] fitdistrplus_1.1-11 ROCR_1.0-11 nlme_3.1-164 RcppAnnoy_0.0.22 irlba_2.3.5.1
[76] vipor_0.4.7 KernSmooth_2.23-22 colorspace_2.1-0 tidyselect_1.2.0 compiler_4.3.1
[81] BiocNeighbors_1.18.0 DelayedArray_0.26.7 plotly_4.10.4 scales_1.3.0 lmtest_0.9-40
[86] digest_0.6.34 goftest_1.2-3 spatstat.utils_3.0-4 XVector_0.40.0 htmltools_0.5.6.1
[91] pkgconfig_2.0.3 sparseMatrixStats_1.12.2 fastmap_1.1.1 rlang_1.1.3 GlobalOptions_0.1.2
[96] htmlwidgets_1.6.2 shiny_1.8.0 DelayedMatrixStats_1.22.6 farver_2.1.1 zoo_1.8-12
[101] jsonlite_1.8.8 BiocParallel_1.34.2 BiocSingular_1.16.0 RCurl_1.98-1.14 magrittr_2.0.3
[106] GenomeInfoDbData_1.2.10 munsell_0.5.0 viridis_0.6.5 reticulate_1.34.0 stringi_1.8.3
[111] ggalluvial_0.12.5 ggraph_2.1.0 zlibbioc_1.46.0 plyr_1.8.9 listenv_0.9.1
[116] deldir_2.0-2 graphlayouts_1.1.0 splines_4.3.1 tensor_1.5 hms_1.1.3
[121] locfit_1.5-9.8 ggpubr_0.6.0 spatstat.geom_3.2-7 ggsignif_0.6.4 reshape2_1.4.4
[126] ScaledMatrix_1.8.1 BiocManager_1.30.22 tzdb_0.4.0 foreach_1.5.2 tweenr_2.0.2
[131] httpuv_1.6.12 RANN_2.6.1 polyclip_1.10-6 future_1.33.1 clue_0.3-65
[136] scattermore_1.2 ggforce_0.4.1 rsvd_1.0.5 broom_1.0.5 xtable_1.8-4
[141] rstatix_0.7.2 later_1.3.2 viridisLite_0.4.2 beeswarm_0.4.0 cluster_2.1.4
[146] timechange_0.3.0 globals_0.16.2
Thanks!
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