ncborcherding
commented
8 months ago @Sogand65
Thanks for reaching out - you need to update your GSVA version to their current system (which is not on bioconductor 3.18 yet).
devtools::install_github("rcastelo/GSVA")Hope that helps, Nick
Hi, Thanks for all quick responses and solving issues. I have issues running escape on HPC. Since I havea large data set and wanted to calculate ssGSEA on go gene set, I have to run it on the HPC. I already installed escape and other dependencies on cluster. But I constantly getting the following error: library(Seurat) library(escape) ... my code ...
runEscape (Adding assay to Seurat object)
GS.GO <- getGeneSets(species = "Homo sapiens", library = "C5", subcategory = "BP") cbmc <- runEscape(cbmc, method = "ssGSEA", gene.sets = GS.GO, groups = 5000, new.assay.name = "escape.go.ssGSEA")
error: Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘gsva’ for signature ‘"missing", "missing"’ Calls: runEscape -> escape.matrix Execution halted
I tried sessionInfo() on the R on cluster and got:
Matrix products: default BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.15.so; LAPACK version 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: America/Chicago tzcode source: system (glibc)
attached base packages: [1] stats graphics grDevices utils datasets methods base
other attached packages: [1] Seurat_5.0.2 SeuratObject_5.0.1 sp_2.1-3 escape_1.99.1
loaded via a namespace (and not attached): [1] RcppAnnoy_0.0.22 splines_4.3.1
[3] later_1.3.2 bitops_1.0-7
[5] tibble_3.2.1 R.oo_1.26.0
[7] polyclip_1.10-6 graph_1.80.0
[9] XML_3.99-0.16.1 fastDummies_1.7.3
[11] lifecycle_1.0.4 globals_0.16.2
[13] lattice_0.21-8 MASS_7.3-60
[15] ggdist_3.3.1 magrittr_2.0.3
[17] plotly_4.10.4 httpuv_1.6.14
[19] sctransform_0.4.1 spam_2.10-0
[21] spatstat.sparse_3.0-3 reticulate_1.35.0
[23] cowplot_1.1.3 pbapply_1.7-2
[25] DBI_1.2.2 RColorBrewer_1.1-3
[27] abind_1.4-5 zlibbioc_1.48.0
[29] Rtsne_0.17 GenomicRanges_1.54.1
[31] purrr_1.0.2 R.utils_2.12.3
[33] BiocGenerics_0.48.1 msigdbr_7.5.1
[35] RCurl_1.98-1.14 GenomeInfoDbData_1.2.11
[37] IRanges_2.36.0 S4Vectors_0.40.2
[39] ggrepel_0.9.5 irlba_2.3.5.1
[41] spatstat.utils_3.0-4 listenv_0.9.1
[43] GSVA_1.50.0 goftest_1.2-3
[45] RSpectra_0.16-1 spatstat.random_3.2-3
[47] annotate_1.80.0 fitdistrplus_1.1-11
[49] parallelly_1.37.1 DelayedMatrixStats_1.24.0
[51] leiden_0.4.3.1 codetools_0.2-19
[53] DelayedArray_0.28.0 tidyselect_1.2.0
[55] UCell_2.6.2 ScaledMatrix_1.10.0
[57] spatstat.explore_3.2-6 matrixStats_1.2.0
[59] stats4_4.3.1 jsonlite_1.8.8
[61] BiocNeighbors_1.20.2 ellipsis_0.3.2
[63] progressr_0.14.0 ggridges_0.5.6
[65] survival_3.5-5 tools_4.3.1
[67] ica_1.0-3 Rcpp_1.0.12
[69] glue_1.7.0 gridExtra_2.3
[71] SparseArray_1.2.4 MatrixGenerics_1.14.0
[73] distributional_0.4.0 GenomeInfoDb_1.38.6
[75] AUCell_1.24.0 dplyr_1.1.4
[77] HDF5Array_1.30.1 fastmap_1.1.1
[79] rhdf5filters_1.14.1 fansi_1.0.6
[81] ggpointdensity_0.1.0 digest_0.6.34
[83] rsvd_1.0.5 R6_2.5.1
[85] mime_0.12 colorspace_2.1-0
[87] scattermore_1.2 tensor_1.5
[89] spatstat.data_3.0-4 RSQLite_2.3.5
[91] R.methodsS3_1.8.2 utf8_1.2.4
[93] tidyr_1.3.1 generics_0.1.3
[95] data.table_1.15.2 httr_1.4.7
[97] htmlwidgets_1.6.4 S4Arrays_1.2.0
[99] uwot_0.1.16 pkgconfig_2.0.3
[101] gtable_0.3.4 blob_1.2.4
[103] lmtest_0.9-40 SingleCellExperiment_1.24.0 [105] XVector_0.42.0 htmltools_0.5.7
[107] dotCall64_1.1-1 GSEABase_1.64.0
[109] scales_1.3.0 Biobase_2.62.0
[111] png_0.1-8 reshape2_1.4.4
[113] nlme_3.1-162 cachem_1.0.8
[115] zoo_1.8-12 rhdf5_2.46.1
[117] stringr_1.5.1 KernSmooth_2.23-21
[119] parallel_4.3.1 miniUI_0.1.1.1
[121] AnnotationDbi_1.64.1 pillar_1.9.0
[123] grid_4.3.1 vctrs_0.6.5
[125] RANN_2.6.1 promises_1.2.1
[127] BiocSingular_1.18.0 beachmat_2.18.1
[129] xtable_1.8-4 cluster_2.1.4
[131] cli_3.6.2 compiler_4.3.1
[133] rlang_1.1.3 crayon_1.5.2
[135] future.apply_1.11.1 plyr_1.8.9
[137] stringi_1.8.3 deldir_2.0-4
[139] viridisLite_0.4.2 BiocParallel_1.36.0
[141] babelgene_22.9 munsell_0.5.0
[143] Biostrings_2.70.2 lazyeval_0.2.2
[145] spatstat.geom_3.2-9 Matrix_1.6-5
[147] RcppHNSW_0.6.0 patchwork_1.2.0
[149] sparseMatrixStats_1.14.0 bit64_4.0.5
[151] future_1.33.1 ggplot2_3.5.0
[153] Rhdf5lib_1.24.2 KEGGREST_1.42.0
[155] shiny_1.8.0 SummarizedExperiment_1.32.0 [157] ROCR_1.0-11 igraph_2.0.2
[159] memoise_2.0.1 bit_4.0.5
can you please help me how can I solve the following issue?
Thanks for your help!