Error: processing vignette 'psuper_intro.Rmd' failed with diagnostics: all(colSums(avg_knn_mat) == 1) is not TRUE

[larafeulner]

Hi, I've run into this error when installing psupertime with build_vignettes=TRUE:

─ installing the package to build vignettes E creating vignettes (18.3s) --- re-building ‘psuper_intro.Rmd’ using rmarkdown Quitting from lines 37-50 (psuper_intro.Rmd) Error: processing vignette 'psuper_intro.Rmd' failed with diagnostics: all(colSums(avg_knn_mat) == 1) is not TRUE --- failed re-building ‘psuper_intro.Rmd’
SUMMARY: processing the following file failed: ‘psuper_intro.Rmd’ Error: Vignette re-building failed. Execution halted Error: Failed to install 'psupertime' from GitHub: ! System command 'R' failed

I took a look at https://github.com/wmacnair/psupertime/blob/master/R/psupertime.R and the issue appears to be related to line 524: stopifnot( all(colSums(avg_knn_mat)==1) )

I'm using R 4.2.1 for arm64 (the new Apple Silicon M1/M2 chips). Unfortunately this may be an arm64 specific issue somehow as it installed fine on my older Intel MacBook (R 4.2.0) and on a Windows laptop with R 4.2.1 and all package dependencies newly installed. I'm at a loss as to what's causing the problem though.

I've installed it without the vignette for now and am hoping the issue won't recur with my own data. But regardless I figured it'd be best to report in case you can look into it. Thanks!

[larafeulner]

Updating to add that unfortunately running: psuper_object <- psupertime(sce_object, timepoints, sel_genes='all') on my own data did generate the same error.

Error in .make_x_data(x, sel_genes, ps_params) : all(colSums(avg_knn_mat) == 1) is not TRUE

I assume it'll work on my older MacBook since the vignette installed so will try there, but if you have a new Mac and can figure it out I'd appreciate it. Thanks!

[nikofleischer]

I am having the same problem :( as the last update was 2 years ago this is probably a dependency error. If you mange to set up a working environment I would appreciate a the lockfile in case you are using renv :)

[odavalos]

@larafeulner I'm unsure if this is still useful, but I installed psupertime with build_vignettes=TRUE on an M1 mac. I run an intel version of R 4.2.1 using Rig. Rig is great for installing and using tricky Bioc packages that do not work with arm64.

Bioc dependencies must be installed beforehand, or you will encounter issues. Here is what I did in the exact order.

BiocManager::install("topGO") BiocManager::install("BiocStyle") devtools::install_github('wmacnair/psupertime', build_vignettes=TRUE)

R version 4.2.1 (2022-06-23) Platform: x86_64-apple-darwin17.0 (64-bit) Running under: macOS Ventura 13.2.1

Matrix products: default LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib

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

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

loaded via a namespace (and not attached): [1] utf8_1.2.2 reticulate_1.26
[3] ks_1.13.5 tidyselect_1.2.0
[5] htmlwidgets_1.5.4 grid_4.2.1
[7] BiocParallel_1.32.0 Rtsne_0.16
[9] devtools_2.4.5 munsell_0.5.0
[11] codetools_0.2-18 ica_1.0-3
[13] future_1.28.0 miniUI_0.1.1.1
[15] withr_2.5.0 spatstat.random_3.1-3
[17] colorspace_2.0-3 progressr_0.11.0
[19] Biobase_2.58.0 rstudioapi_0.14
[21] Seurat_4.2.0 stats4_4.2.1
[23] SingleCellExperiment_1.20.0 ROCR_1.0-11
[25] ggsignif_0.6.4 tensor_1.5
[27] listenv_0.8.0 MatrixGenerics_1.10.0
[29] GenomeInfoDbData_1.2.9 polyclip_1.10-4
[31] pheatmap_1.0.12 Nebulosa_1.8.0
[33] rprojroot_2.0.3 parallelly_1.32.1
[35] vctrs_0.5.0 generics_0.1.3
[37] ggthemes_4.2.4 R6_2.5.1
[39] doParallel_1.0.17 GenomeInfoDb_1.34.0
[41] bitops_1.0-7 spatstat.utils_3.0-1
[43] cachem_1.0.6 DelayedArray_0.24.0
[45] assertthat_0.2.1 promises_1.2.0.1
[47] BiocIO_1.8.0 scales_1.2.1
[49] rgeos_0.5-9 gtable_0.3.1
[51] globals_0.16.1 processx_3.8.0
[53] goftest_1.2-3 rlang_1.0.6
[55] splines_4.2.1 rtracklayer_1.58.0
[57] rstatix_0.7.2 lazyeval_0.2.2
[59] spatstat.geom_3.0-6 broom_1.0.1
[61] BiocManager_1.30.19 yaml_2.3.6
[63] reshape2_1.4.4 abind_1.4-5
[65] backports_1.4.1 httpuv_1.6.6
[67] usethis_2.1.6 tools_4.2.1
[69] ggplot2_3.4.0 ellipsis_0.3.2
[71] spatstat.core_2.4-4 RColorBrewer_1.1-3
[73] BiocGenerics_0.44.0 sessioninfo_1.2.2
[75] ggridges_0.5.4 Rcpp_1.0.9
[77] plyr_1.8.7 zlibbioc_1.44.0
[79] purrr_0.3.5 RCurl_1.98-1.9
[81] ps_1.7.2 prettyunits_1.1.1
[83] ggpubr_0.5.0 rpart_4.1.19
[85] deldir_1.0-6 pbapply_1.5-0
[87] viridis_0.6.2 urlchecker_1.0.1
[89] cowplot_1.1.1 S4Vectors_0.36.0
[91] zoo_1.8-11 SeuratObject_4.1.2
[93] SummarizedExperiment_1.28.0 ggrepel_0.9.1
[95] cluster_2.1.4 fs_1.5.2
[97] magrittr_2.0.3 scGrabBag_0.0.0.9000
[99] data.table_1.14.4 scattermore_0.8
[101] lmtest_0.9-40 RANN_2.6.1
[103] mvtnorm_1.1-3 fitdistrplus_1.1-8
[105] matrixStats_0.62.0 pkgload_1.3.2
[107] patchwork_1.1.2 mime_0.12
[109] evaluate_0.17 xtable_1.8-4
[111] XML_3.99-0.12 mclust_6.0.0
[113] IRanges_2.32.0 gridExtra_2.3
[115] compiler_4.2.1 tibble_3.1.8
[117] KernSmooth_2.23-20 crayon_1.5.2
[119] htmltools_0.5.3 mgcv_1.8-41
[121] later_1.3.0 tidyr_1.2.1
[123] DBI_1.1.3 MASS_7.3-58.1
[125] Matrix_1.5-1 car_3.1-1
[127] cli_3.4.1 parallel_4.2.1
[129] igraph_1.3.5 GenomicRanges_1.50.0
[131] pkgconfig_2.0.3 GenomicAlignments_1.34.0
[133] sp_1.5-0 plotly_4.10.0
[135] spatstat.sparse_3.0-0 foreach_1.5.2
[137] XVector_0.38.0 stringr_1.4.1
[139] callr_3.7.3 digest_0.6.30
[141] sctransform_0.3.5 RcppAnnoy_0.0.20
[143] pracma_2.4.2 spatstat.data_3.0-0
[145] Biostrings_2.66.0 leiden_0.4.3
[147] uwot_0.1.14 curl_4.3.3
[149] restfulr_0.0.15 shiny_1.7.3
[151] Rsamtools_2.14.0 rjson_0.2.21
[153] lifecycle_1.0.3 nlme_3.1-160
[155] jsonlite_1.8.3 carData_3.0-5
[157] desc_1.4.2 viridisLite_0.4.1
[159] fansi_1.0.3 pillar_1.8.1
[161] lattice_0.20-45 fastmap_1.1.0
[163] httr_1.4.4 pkgbuild_1.4.0
[165] survival_3.4-0 glue_1.6.2
[167] remotes_2.4.2 png_0.1-7
[169] iterators_1.0.14 stringi_1.7.8
[171] profvis_0.3.7 memoise_2.0.1
[173] dplyr_1.0.10 irlba_2.3.5.1
[175] future.apply_1.9.1