Unable to run Monocle3 or 2: undefined columns selected

[leawenger]

Hello,

After updating the installation of Monocle on my laptop, I have found it very difficult to run anything.

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1) Running the old code on a new cell data set

If I run the old version with this code: mono_RGC <- estimateSizeFactors(mono_RGC) mono_RGC <- estimateDispersions(mono_RGC) mono_RGC <- detectGenes(mono_RGC, min_expr = 0.2) fData(mono_RGC)$use_for_ordering <- fData(mono_RGC)$num_cells_expressed > 0.05 * ncol(mono_RGC) plot_pc_variance_explained(mono_RGC, return_all = F) mono_RGC <- reduceDimension(mono_RGC, max_components = 2, num_dim = 15, reduction_method = 'tSNE', verbose = T) mono_RGC <- clusterCells(mono_RGC, verbose = F) plot_cell_clusters(mono_RGC, color_by = 'as.factor(Cluster)') mono_RGC_expressed_genes <- row.names(subset(fData(mono_RGC),num_cells_expressed >= 5)) clustering_DEG_genes <- differentialGeneTest(mono_RGC[mono_RGC_expressed_genes,], fullModelFormulaStr = '~Cluster', cores = 1) mono_Glia_ordering_genes <- row.names(clustering_DEG_genes)[order(clustering_DEG_genes$qval)][1:1000] mono_Glia <- setOrderingFilter(mono_RGC, ordering_genes = mono_Glia_ordering_genes) mono_RGC <- reduceDimension(mono_RGC, method = 'DDRTree') mono_RGC <- orderCells(mono_RGC, reverse = T) plot_cell_trajectory(mono_RGC, color_by = "State")

I get to the last command and get:
mono_RGC <- orderCells(mono_RGC, reverse = T) Error in orderCells(mono_RGC, reverse = T) : orderCells doesn't support more than 10k centroids (cells)

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2) Using an old cell data set to see if it works Using CDS R objects that previously worked fine, when I try and 'plot_cell_trajectory' I get:

Error in plot_cell_trajectory(monocle_org75, color_by = "State") : no slot of name "rge_method" for this object of class "CellDataSet"

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3) Using Monocle 3 Using the Monocle 3 tutorial code, I can run all the commands until plot_cell_trajectory, where I get the error: plot_cell_trajectory(mono_RGC, x = 1, y = 2, color_by = 'Pseudotime') Error in [.data.frame(pData(cds), , color_by) : undefined columns selected

The code for the Monocle3 release I used: mono_RGC <- updateCDS(mono_RGC) DelayedArray:::set_verbose_block_processing(TRUE) options(DelayedArray.block.size=1000e6) mono_RGC <- estimateSizeFactors(mono_RGC) mono_RGC <- estimateDispersions(mono_RGC) mono_RGC <- preprocessCDS(mono_RGC, num_dim = 20) mono_RGC <- reduceDimension(mono_RGC, reduction_method = 'UMAP') mono_RGC <- partitionCells(mono_RGC) mono_RGC <- learnGraph(mono_RGC, RGE_method = 'SimplePPT') plot_cell_trajectory(mono_RGC, x = 1, y = 2, color_by = 'Pseudotime')

I have tried a fresh installation on a different computer and am getting similar errors. Does anyone have any idea what I may be doing wrong?

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My session info: py_config() python: /anaconda3/bin/python3.6 libpython: /anaconda3/lib/libpython3.6m.dylib pythonhome: /anaconda3:/anaconda3 version: 3.6.6 |Anaconda, Inc.| (default, Jun 28 2018, 11:07:29) [GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)] numpy: /anaconda3/lib/python3.6/site-packages/numpy numpy_version: 1.16.0

NOTE: Python version was forced by use_python function

R version 3.5.1 (2018-07-02) Platform: x86_64-apple-darwin15.6.0 (64-bit) Running under: macOS High Sierra 10.13.6

Matrix products: default BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

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

attached base packages: [1] parallel stats4 stats graphics grDevices utils datasets [8] methods base

other attached packages: [1] dplyr_0.8.0.1 monocle_2.99.3 L1Graph_0.1.1
[4] lpSolveAPI_5.5.2.0-17 DDRTree_0.1.5 irlba_2.3.3
[7] igraph_1.2.4 Biobase_2.40.0 DelayedArray_0.6.6
[10] BiocParallel_1.14.2 IRanges_2.14.12 S4Vectors_0.18.3
[13] BiocGenerics_0.26.0 matrixStats_0.54.0 reticulate_1.11
[16] Seurat_2.3.4 Matrix_1.2-15 cowplot_0.9.4
[19] ggplot2_3.1.0

loaded via a namespace (and not attached): [1] R.utils_2.8.0 tidyselect_0.2.5 htmlwidgets_1.3
[4] grid_3.5.1 trimcluster_0.1-2.1 docopt_0.6.1
[7] Rtsne_0.15 devtools_2.0.1 munsell_0.5.0
[10] codetools_0.2-16 ica_1.0-2 units_0.6-2
[13] future_1.11.1.1 miniUI_0.1.1.1 withr_2.1.2
[16] colorspace_1.4-0 fastICA_1.2-1 knitr_1.21
[19] rstudioapi_0.9.0 ROCR_1.0-7 robustbase_0.93-3
[22] dtw_1.20-1 pbmcapply_1.3.1 gbRd_0.4-11
[25] listenv_0.7.0 Rdpack_0.10-1 lars_1.2
[28] slam_0.1-45 bit64_0.9-7 pheatmap_1.0.12
[31] rprojroot_1.3-2 coda_0.19-2 LearnBayes_2.15.1
[34] xfun_0.5 diptest_0.75-7 R6_2.4.0
[37] doParallel_1.0.14 VGAM_1.1-1 hdf5r_1.0.1
[40] manipulateWidget_0.10.0 flexmix_2.3-15 bitops_1.0-6
[43] assertthat_0.2.0 promises_1.0.1 SDMTools_1.1-221
[46] scales_1.0.0 nnet_7.3-12 gtable_0.2.0
[49] npsurv_0.4-0 globals_0.12.4 processx_3.2.1
[52] rlang_0.3.1 splines_3.5.1 lazyeval_0.2.1
[55] acepack_1.4.1 checkmate_1.9.1 rgl_0.99.16
[58] reshape2_1.4.3 crosstalk_1.0.0 backports_1.1.3
[61] httpuv_1.4.5.1 Hmisc_4.2-0 usethis_1.4.0
[64] tools_3.5.1 spData_0.3.0 gplots_3.0.1.1
[67] RColorBrewer_1.1-2 proxy_0.4-22 sessioninfo_1.1.1
[70] ggridges_0.5.1 Rcpp_1.0.0 plyr_1.8.4
[73] base64enc_0.1-3 classInt_0.3-1 purrr_0.3.1
[76] densityClust_0.3 prettyunits_1.0.2 ps_1.3.0
[79] rpart_4.1-13 deldir_0.1-16 pbapply_1.4-0
[82] viridis_0.5.1 zoo_1.8-4 ggrepel_0.8.0
[85] cluster_2.0.7-1 fs_1.2.6 magrittr_1.5
[88] data.table_1.12.0 gmodels_2.18.1 lmtest_0.9-36
[91] RANN_2.6.1 mvtnorm_1.0-8 fitdistrplus_1.0-14
[94] pkgload_1.0.2 lsei_1.2-0 mime_0.6
[97] xtable_1.8-3 mclust_5.4.2 sparsesvd_0.1-4
[100] gridExtra_2.3 testthat_2.0.1 HSMMSingleCell_0.114.0
[103] compiler_3.5.1 tibble_2.0.1 KernSmooth_2.23-15
[106] crayon_1.3.4 R.oo_1.22.0 htmltools_0.3.6
[109] segmented_0.5-3.0 later_0.8.0 spdep_1.0-2
[112] Formula_1.2-3 snow_0.4-3 tidyr_0.8.3
[115] expm_0.999-3 DBI_1.0.0 MASS_7.3-51.1
[118] fpc_2.1-11.1 sf_0.7-3 boot_1.3-20
[121] cli_1.0.1 R.methodsS3_1.7.1 gdata_2.18.0
[124] metap_1.1 pkgconfig_2.0.2 foreign_0.8-71
[127] sp_1.3-1 plotly_4.8.0 foreach_1.4.4
[130] webshot_0.5.1 bibtex_0.4.2 callr_3.1.1
[133] stringr_1.4.0 digest_0.6.18 tsne_0.1-3
[136] htmlTable_1.13.1 DelayedMatrixStats_1.2.0 kernlab_0.9-27
[139] shiny_1.2.0 gtools_3.8.1 modeltools_0.2-22
[142] nlme_3.1-137 jsonlite_1.6 desc_1.2.0
[145] viridisLite_0.3.0 limma_3.36.5 pillar_1.3.1
[148] lattice_0.20-38 pkgbuild_1.0.2 httr_1.4.0
[151] DEoptimR_1.0-8 survival_2.43-3 remotes_2.0.2
[154] glue_1.3.0 qlcMatrix_0.9.7 FNN_1.1.3
[157] png_0.1-7 prabclus_2.2-7 iterators_1.0.10
[160] glmnet_2.0-16 bit_1.1-14 class_7.3-15
[163] stringi_1.3.1 mixtools_1.1.0 memoise_1.1.0
[166] doSNOW_1.0.16 latticeExtra_0.6-28 caTools_1.17.1.1
[169] e1071_1.7-0.1 ape_5.2

[afaissa]

Hi,

I am having the same problem with 10x data.

dim(HSMM) Features Samples 63678 23127

Error:

HSMM <- orderCells(HSMM) Error in orderCells(HSMM) : orderCells doesn't support more than 10k centroids (cells)

Is that anything I could do to solve this error?

[gerrardmai]

face the same problem

cds <- updataCDS(cds) Error in updataCDS(cds) : could not find function "updataCDS"

[AilingZhong]

Hi, when I did this steps ordercells <- orderCells(CDS_tumor), the Error is: orderCells(CDS_tumor_order_reducedime) : orderCells doesn't support more than 10k centroids (cells),

Does anyone know how to solve this problem？Thanks!

[sruthi-hub]

face the same problem

cds <- updataCDS(cds) Error in updataCDS(cds) : could not find function "updataCDS"

Did you find an answer? I have the same issue. @gerrardmai