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Error in irGSEA.integrate #52

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randystyle21 opened this issue Dec 12, 2024 · 0 comments
Open

Error in irGSEA.integrate #52

randystyle21 opened this issue Dec 12, 2024 · 0 comments

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@randystyle21
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Hello, Chuiqin.

I think I am encountering the same problem with irGSEA.integrate function .

`result.dge <- irGSEA.integrate(object = cluster,

  •                            group.by = "cytokine",
    
  •                            metadata = NULL, col.name = NULL,
    
  •                            method = c("AUCell","singscore","ssgsea"))
    

Calculate differential gene set : AUCell
Calculate differential gene set : singscore
Calculate differential gene set : ssgsea
Error in UseMethod("distinct") :
no applicable method for 'distinct' applied to an object of class "NULL"
In addition: Warning messages:
1: In mean.fxn(object[features, cells.1, drop = FALSE]) : NaNs produced
2: In mean.fxn(object[features, cells.2, drop = FALSE]) : NaNs produced
3: In mean.fxn(object[features, cells.1, drop = FALSE]) : NaNs produced
4: In mean.fxn(object[features, cells.2, drop = FALSE]) : NaNs produced
5: In mean.fxn(object[features, cells.1, drop = FALSE]) : NaNs produced
6: In mean.fxn(object[features, cells.2, drop = FALSE]) : NaNs produced
7: In mean.fxn(object[features, cells.1, drop = FALSE]) : NaNs produced
8: In mean.fxn(object[features, cells.2, drop = FALSE]) : NaNs produced
9: In mean.fxn(object[features, cells.1, drop = FALSE]) : NaNs produced
10: In mean.fxn(object[features, cells.2, drop = FALSE]) : NaNs produced`

I thought it was the same problem so that I ran FindMarkers function with fc.slot, and it went through properly.

test <- FindMarkers(cluster, ident.1 = "Control", ident.2="CK", fc.slot="scale.data", method="wilcox", assay ="AUCell")

Probably it's due to the newer version of the R? Thanks a ton in advance!

sessionInfo()
R version 4.4.2 (2024-10-31)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.1 LTS

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.12.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0

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: America/Los_Angeles
tzcode source: system (glibc)

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

other attached packages:
[1] irGSEA_3.3.2 DropletUtils_1.26.0 UniProt.ws_2.46.1 RSQLite_2.3.9 ggrepel_0.9.6
[6] ggpubr_0.6.0 RColorBrewer_1.1-3 classInt_0.4-10 patchwork_1.3.0 ensembldb_2.30.0
[11] AnnotationFilter_1.30.0 GenomicFeatures_1.58.0 AnnotationDbi_1.68.0 SingleCellExperiment_1.28.1 SummarizedExperiment_1.36.0
[16] Biobase_2.66.0 GenomicRanges_1.58.0 GenomeInfoDb_1.42.1 IRanges_2.40.1 S4Vectors_0.44.0
[21] MatrixGenerics_1.18.0 matrixStats_1.4.1 cowplot_1.1.3 scales_1.3.0 RCurl_1.98-1.16
[26] Matrix_1.7-1 lubridate_1.9.4 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
[31] purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1
[36] tidyverse_2.0.0 Seurat_5.1.0 SeuratObject_5.0.2 sp_2.1-4 AnnotationHub_3.14.0
[41] BiocFileCache_2.14.0 dbplyr_2.5.0 BiocGenerics_0.52.0

loaded via a namespace (and not attached):
[1] igraph_2.1.2 graph_1.84.0 ica_1.0-3 plotly_4.10.4 Formula_1.2-5
[6] devtools_2.4.5 zlibbioc_1.52.0 BiocBaseUtils_1.8.0 tidyselect_1.2.1 bit_4.5.0.1
[11] doParallel_1.0.17 clue_0.3-66 lattice_0.22-5 rjson_0.2.23 brew_1.0-10
[16] distillery_1.2-2 blob_1.2.4 urlchecker_1.0.1 rngtools_1.5.2 S4Arrays_1.6.0
[21] RMTstat_0.3.1 parallel_4.4.2 drat_0.2.5 png_0.1-8 cli_3.6.3
[26] ggplotify_0.1.2 ProtGenerics_1.38.0 goftest_1.2-3 BiocIO_1.16.0 kernlab_0.9-33
[31] BiocNeighbors_2.0.1 uwot_0.2.2 curl_6.0.1 RcppML_0.3.7 mime_0.12
[36] evaluate_1.0.1 tidytree_0.4.6 leiden_0.4.3.1 ComplexHeatmap_2.22.0 stringi_1.8.4
[41] backports_1.5.0 desc_1.4.3 XML_3.99-0.17 httpuv_1.6.15 flexmix_2.3-19
[46] magrittr_2.0.3 rappdirs_0.3.3 splines_4.4.2 mclust_6.1.1 doRNG_1.8.6
[51] Lmoments_1.3-1 sctransform_0.4.1 ggbeeswarm_0.7.2 sessioninfo_1.2.2 DBI_1.2.3
[56] Nebulosa_1.16.0 terra_1.7-83 HDF5Array_1.34.0 withr_3.0.2 class_7.3-22
[61] lmtest_0.9-40 GSEABase_1.68.0 rtracklayer_1.66.0 BiocManager_1.30.25 extRemes_2.2
[66] htmlwidgets_1.6.4 fs_1.6.5 segmented_2.1-3 triebeard_0.4.1 labeling_0.4.3
[71] SparseArray_1.6.0 mixtools_2.0.0 annotate_1.84.0 reticulate_1.40.0 zoo_1.8-12
[76] XVector_0.46.0 knitr_1.49 UCSC.utils_1.2.0 dendsort_0.3.4 AUCell_1.28.0
[81] timechange_0.3.0 foreach_1.5.2 fansi_1.0.6 viper_1.40.0 grid_4.4.2
[86] data.table_1.16.4 ggtree_3.14.0 rhdf5_2.50.0 quantreg_5.99.1 R.oo_1.27.0
[91] RSpectra_0.16-2 irlba_2.3.5.1 pointr_0.2.0 ggrastr_1.0.2 fastDummies_1.7.4
[96] gridGraphics_0.5-1 ellipsis_0.3.2 SpatialExperiment_1.16.0 lazyeval_0.2.2 yaml_2.3.10
[101] survival_3.7-0 scattermore_1.2 BiocVersion_3.20.0 crayon_1.5.3 RcppAnnoy_0.0.22
[106] progressr_0.15.1 later_1.4.1 ggridges_0.5.6 codetools_0.2-20 GlobalOptions_0.1.2
[111] profvis_0.4.0 KEGGREST_1.46.0 sccore_1.0.5 Rtsne_0.17 shape_1.4.6.1
[116] limma_3.62.1 urltools_1.7.3 Rsamtools_2.22.0 filelock_1.0.3 pkgconfig_2.0.3
[121] xml2_1.3.6 spatstat.univar_3.1-1 pagoda2_1.0.12 GenomicAlignments_1.42.0 aplot_0.2.3
[126] spatstat.sparse_3.1-0 ape_5.8 viridisLite_0.4.2 xtable_1.8-4 car_3.1-3
[131] decoupleR_2.12.0 N2R_1.0.3 plyr_1.8.9 httr_1.4.7 tools_4.4.2
[136] globals_0.16.3 pkgbuild_1.4.5 beeswarm_0.4.0 broom_1.0.7 nlme_3.1-165
[141] MatrixModels_0.5-3 digest_0.6.37 farver_2.1.2 tzdb_0.4.0 reshape2_1.4.4
[146] ks_1.14.3 yulab.utils_0.1.8 gghalves_0.1.4 glue_1.8.0 cachem_1.1.0
[151] polyclip_1.10-7 rjsoncons_1.3.1 generics_0.1.3 Biostrings_2.74.0 mvtnorm_1.3-2
[156] presto_1.0.0 parallelly_1.40.1 pkgload_1.4.0 statmod_1.5.0 RcppHNSW_0.6.0
[161] ScaledMatrix_1.14.0 carData_3.0-5 pbapply_1.7-2 spam_2.11-0 VAM_1.1.0
[166] dqrng_0.4.1 utf8_1.2.4 ggsignif_0.6.4 GSVA_2.0.2 gridExtra_2.3
[171] shiny_1.9.1 GenomeInfoDbData_1.2.13 R.utils_2.12.3 rhdf5filters_1.18.0 memoise_2.0.1
[176] RobustRankAggreg_1.2.1 R.methodsS3_1.8.2 singscore_1.26.0 future_1.34.0 RANN_2.6.2
[181] Cairo_1.6-2 spatstat.data_3.1-4 rstudioapi_0.17.1 cluster_2.1.6 msigdbr_7.5.1
[186] spatstat.utils_3.1-1 hms_1.1.3 fitdistrplus_1.2-1 munsell_0.5.1 colorspace_2.1-1
[191] rlang_1.1.4 Rook_1.2 DelayedMatrixStats_1.28.0 sparseMatrixStats_1.18.0 dotCall64_1.2
[196] circlize_0.4.16 scuttle_1.16.0 mgcv_1.9-1 xfun_0.49 e1071_1.7-16
[201] modeltools_0.2-23 remotes_2.5.0 iterators_1.0.14 abind_1.4-8 treeio_1.30.0
[206] ggsci_3.2.0 Rhdf5lib_1.28.0 bitops_1.0-9 ps_1.8.1 promises_1.3.2
[211] fgsea_1.32.0 DelayedArray_0.32.0 proxy_0.4-27 compiler_4.4.2 prettyunits_1.2.0
[216] beachmat_2.22.0 SparseM_1.84-2 listenv_0.9.1 Rcpp_1.0.13-1 BiocSingular_1.22.0
[221] edgeR_4.4.1 roxygen2_7.3.2 tensor_1.5 usethis_3.1.0 MASS_7.3-61
[226] progress_1.2.3 UCell_2.10.1 BiocParallel_1.40.0 scde_2.34.0 babelgene_22.9
[231] spatstat.random_3.3-2 R6_2.5.1 RcppArmadillo_14.2.2-1 fastmap_1.2.0 fastmatch_1.1-4
[236] rstatix_0.7.2 vipor_0.4.7 ROCR_1.0-11 rsvd_1.0.5 nnet_7.3-19
[241] gtable_0.3.6 KernSmooth_2.23-24 miniUI_0.1.1.1 deldir_2.0-4 htmltools_0.5.8.1
[246] bit64_4.5.2 spatstat.explore_3.3-3 lifecycle_1.0.4 processx_3.8.4 callr_3.7.6
[251] restfulr_0.0.15 vctrs_0.6.5 spatstat.geom_3.3-4 ggfun_0.1.8 future.apply_1.11.3
[256] pracma_2.4.4 pillar_1.9.0 magick_2.8.5 pcaMethods_1.98.0 locfit_1.5-9.10

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