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GregorySchwartz / tooManyCellsR

An R wrapper for too-many-cells, for clustering single cells and analyzing cell clade relationships with colorful visualizations.
GNU General Public License v3.0
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Error in system2("too-many-cells", args = c(args, autoArgs), stdout = TRUE) : error in running command #6

Open howtofindme opened 10 months ago

howtofindme commented 10 months ago

hi, thanks for your execellent job! when i run your tutuial:

res=TooManyCellsR::tooManyCells(mat = pbmc@assays$RNA@counts, args = c( "make-tree" , "--no-filter" , "--normalization", "NoneNorm" , "--draw-max-node-size", "40" , "--draw-max-leaf-node-size", "70" ) )

it throws me an error what should I do : image

here is my sessioninfo: sessionInfo() 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
[3] 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
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

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

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

other attached packages: [1] TooManyCellsR_0.1.1.0 ggsurvfit_1.0.0
[3] ggpointdensity_0.1.0 ggpubr_0.6.0
[5] ggstatsplot_0.12.0 airway_1.20.0
[7] SummarizedExperiment_1.30.2 Biobase_2.60.0
[9] GenomicRanges_1.52.0 GenomeInfoDb_1.36.3
[11] IRanges_2.34.1 S4Vectors_0.38.1
[13] BiocGenerics_0.46.0 MatrixGenerics_1.12.3
[15] matrixStats_1.0.0 ggplot2_3.4.3
[17] harmony_0.1.1 Rcpp_1.0.11
[19] stringr_1.5.0 dplyr_1.1.3
[21] SeuratObject_4.1.3 Seurat_4.3.0.1

loaded via a namespace (and not attached): [1] fs_1.6.3 spatstat.sparse_3.0-2 bitops_1.0-7
[4] devtools_2.4.5 httr_1.4.7 RColorBrewer_1.1-3
[7] insight_0.19.3 profvis_0.3.8 tools_4.3.1
[10] sctransform_0.3.5 backports_1.4.1 utf8_1.2.3
[13] R6_2.5.1 statsExpressions_1.5.1 lazyeval_0.2.2
[16] uwot_0.1.16 urlchecker_1.0.1 withr_2.5.0
[19] sp_2.0-0 prettyunits_1.1.1 gridExtra_2.3
[22] progressr_0.14.0 cli_3.6.1 performance_0.10.4
[25] spatstat.explore_3.2-3 sandwich_3.0-2 labeling_0.4.3
[28] prismatic_1.1.1 BWStest_0.2.2 mvtnorm_1.2-3
[31] spatstat.data_3.0-1 ggridges_0.5.4 pbapply_1.7-2
[34] parallelly_1.36.0 sessioninfo_1.2.2 rstudioapi_0.15.0
[37] generics_0.1.3 ica_1.0-3 spatstat.random_3.1-5
[40] car_3.1-2 Matrix_1.6-1 fansi_1.0.4
[43] abind_1.4-5 lifecycle_1.0.3 multcomp_1.4-25
[46] carData_3.0-5 Rtsne_0.16 paletteer_1.5.0
[49] grid_4.3.1 promises_1.2.1 crayon_1.5.2
[52] miniUI_0.1.1.1 lattice_0.21-8 cowplot_1.1.1
[55] zeallot_0.1.0 pillar_1.9.0 knitr_1.43
[58] estimability_1.4.1 future.apply_1.11.0 kSamples_1.2-9
[61] codetools_0.2-19 leiden_0.4.3 glue_1.6.2
[64] data.table_1.14.8 remotes_2.4.2.1 vctrs_0.6.3
[67] png_0.1-8 gtable_0.3.4 rematch2_2.1.2
[70] datawizard_0.8.0 cachem_1.0.8 xfun_0.40
[73] S4Arrays_1.0.6 mime_0.12 correlation_0.8.4
[76] coda_0.19-4 survival_3.5-7 gmp_0.7-2
[79] ellipsis_0.3.2 fitdistrplus_1.1-11 TH.data_1.1-2
[82] ROCR_1.0-11 nlme_3.1-163 usethis_2.2.2
[85] RcppAnnoy_0.0.21 rprojroot_2.0.3 irlba_2.3.5.1
[88] KernSmooth_2.23-22 colorspace_2.1-0 tidyselect_1.2.0
[91] processx_3.8.2 emmeans_1.8.8 compiler_4.3.1
[94] curl_5.0.2 desc_1.4.2 DelayedArray_0.26.7
[97] plotly_4.10.2 bayestestR_0.13.1 scales_1.2.1
[100] lmtest_0.9-40 callr_3.7.3 multcompView_0.1-9
[103] digest_0.6.33 goftest_1.2-3 spatstat.utils_3.0-3
[106] XVector_0.40.0 htmltools_0.5.6 pkgconfig_2.0.3
[109] highr_0.10 fastmap_1.1.1 rlang_1.1.1
[112] htmlwidgets_1.6.2 shiny_1.7.5 SuppDists_1.1-9.7
[115] farver_2.1.1 zoo_1.8-12 jsonlite_1.8.7
[118] RCurl_1.98-1.12 magrittr_2.0.3 GenomeInfoDbData_1.2.10 [121] patchwork_1.1.3 parameters_0.21.1 munsell_0.5.0
[124] viridis_0.6.4 reticulate_1.31 stringi_1.7.12
[127] zlibbioc_1.46.0 MASS_7.3-60 plyr_1.8.8
[130] pkgbuild_1.4.2 parallel_4.3.1 listenv_0.9.0
[133] ggrepel_0.9.3 deldir_1.0-9 PMCMRplus_1.9.7
[136] splines_4.3.1 tensor_1.5 ps_1.7.5
[139] igraph_1.5.1 spatstat.geom_3.2-5 ggsignif_0.6.4
[142] effectsize_0.8.5 reshape2_1.4.4 pkgload_1.3.2.1
[145] rstantools_2.3.1.1 evaluate_0.21 RcppParallel_5.1.7
[148] httpuv_1.6.11 MatrixModels_0.5-2 RANN_2.6.1
[151] BayesFactor_0.9.12-4.4 tidyr_1.3.0 purrr_1.0.2
[154] polyclip_1.10-4 future_1.33.0 scattermore_1.2
[157] broom_1.0.5 xtable_1.8-4 Rmpfr_0.9-3
[160] rstatix_0.7.2 later_1.3.1 viridisLite_0.4.2
[163] tibble_3.2.1 memoise_2.0.1 cluster_2.1.4
[166] globals_0.16.2

Best, Young

GregorySchwartz commented 10 months ago

Thank you for your interest! tooManyCellR is a wrapper for TooManyCells, so you must first install TooManyCells (https://gregoryschwartz.github.io/too-many-cells/).