manqicai
commented
6 months ago Hi. I am running into the following error when running EnsDeconv():
Error in h(simpleError(msg, call)) : error in evaluating the argument 'x' in selecting a method for function 't': NA/NaN/Inf in foreign function call (arg 2)
I have checked the bulk-data structure and checked to see if there were any NA/NaN/Inf values. There are none. Also, the ref_mat seems to be fine as well as the meta_ref.
I did troubleshoot a bit. And observed the following:
- When I switched the bulk proteomic dataframe with the testdata$count_bulk, using the same ref_list as mine, then EnsDeconv gave the same error i.e. Error in h(simpleError(msg, call)) : error in evaluating the argument 'x' in selecting a method for function 't': NA/NaN/Inf in foreign function call (arg 2)
- However when I switched the ref_list to the one provided as the test per the vignette i.e did the following: params = get_params(data_type = "singlecell-rna", data_name = names(testdata$ref_list), n_markers = 50, Marker.Method = "t", TNormalization = "none", CNormalization = "none", dmeths = "CIBERSORT") res = EnsDeconv(count_bulk = as.matrix(proteomic), ref_list = testdata$ref_list, params = params) As in keeping the count_bulk the same as our proteomic dataset instead of the test one while changing the ref_list to the testdata$ref_list, then EnsDeconv worked
This suggests to me that the error is coming from ref_list somewhere either from the ref_mat or meta_ref. It is just that when I check the structure of the list i.e. list from my data and the testdata$reflist, it is similar. I definitely need to run this and am looking for any solutions. Please let me know.
There is one thing - I had changed the rownames of the ref_mat i.e. the mat derived from the seurat object that I had. I had derived the mat (ref_matrix) from the seurat object in the following manner: object@assays$RNA$counts. But had changed the ID type of the rownames mat to align with that of the proteomic/bulk_proteomic dataframe i.e. proteomic/count_data.
Please let me know. Will appreciate any help. Thanks. Following was the session info:
R version 4.3.1 (2023-06-16) Platform: aarch64-apple-darwin20 (64-bit) Running under: macOS Monterey 12.7.3
Matrix products: default BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/New_York tzcode source: internal
attached base packages: [1] grid stats4 stats graphics grDevices utils datasets methods base
other attached packages: [1] ellipse_0.5.0 scran_1.30.2 sparseMatrixStats_1.14.0 [4] EnsDeconv_0.2.1 foreach_1.5.2 MuSiC_1.0.0 [7] TOAST_1.16.0 quadprog_1.5-8 EpiDISH_2.18.0 [10] nnls_1.5 RCy3_2.22.1 ggcorrplot_0.1.4.999 [13] factoextra_1.0.7 rgl_1.2.8 pastecs_1.4.2 [16] fastDummies_1.7.3 effects_4.2-2 car_3.1-2 [19] carData_3.0-5 compute.es_0.2-5 vioplot_0.4.0 [22] zoo_1.8-12 sm_2.2-6.0 CorLevelPlot_0.99.0 [25] GEOquery_2.70.0 RcisTarget_1.19.2 DelayedArray_0.28.0 [28] SparseArray_1.2.4 S4Arrays_1.2.0 abind_1.4-5 [31] qvalue_2.34.0 scattermore_1.2 zellkonverter_1.12.1 [34] Cairo_1.6-2 WriteXLS_6.5.0 SomaDataIO_6.0.0.9000 [37] tiff_0.1-12 readxl_1.4.3 pander_0.6.5 [40] clustifyr_1.14.0 ComplexHeatmap_2.18.0 Polychrome_1.5.1 [43] svglite_2.1.3 GOplot_1.0.2 gridExtra_2.3 [46] ggdendro_0.1.23 DOSE_3.28.2 clusterProfiler_4.10.0 [49] MAST_1.28.0 rsvd_1.0.5 TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 [52] knitr_1.45 GGally_2.2.1 PALMO_0.1.1 [55] wordcloud_2.6 EnhancedVolcano_1.20.0 NMF_0.27 [58] cluster_2.1.6 rngtools_1.5.2 registry_0.5-1 [61] ggalluvial_0.12.5 reticulate_1.35.0 CellChat_2.1.1 [64] loomR_0.2.1.9000 hdf5r_1.3.9 R6_2.5.1 [67] anndata_0.7.5.6 GeneOverlap_1.38.0 enrichR_3.2 [70] scDblFinder_1.16.0 scater_1.30.1 scuttle_1.12.0 [73] scAnnotatR_1.8.0 gt_0.10.1 zenith_1.4.2 [76] ExperimentHub_2.10.0 dreamlet_1.0.0 variancePartition_1.32.3 [79] gridGraphics_0.5-1 BiocManager_1.30.22 BiocParallel_1.36.0 [82] Augur_1.0.3 motifmatchr_1.24.0 TFBSTools_1.40.0 [85] JASPAR2020_0.99.10 chromVAR_1.24.0 BSgenome.Mmusculus.UCSC.mm10_1.4.3 [88] BSgenome_1.70.2 rtracklayer_1.62.0 BiocIO_1.12.0 [91] Biostrings_2.70.2 XVector_0.42.0 openxlsx_4.2.5.2 [94] EnsDb.Mmusculus.v79_2.99.0 Signac_1.12.0 clustree_0.5.1 [97] ggraph_2.1.0 lme4_1.1-35.1 SoupX_1.6.2 [100] msigdbr_7.5.1 umap_0.2.10.0 ordinal_2023.12-4 [103] OlinkAnalyze_3.6.2 AUCell_1.24.0 GENIE3_1.24.0 [106] SCENIC_1.3.1 SCopeLoomR_0.13.0 scTenifoldKnk_1.0.2 [109] SeuratWrappers_0.3.1 remotes_2.4.2.1 scTenifoldNet_1.3 [112] FactoMineR_2.9 maSigPro_1.74.0 EnsDb.Hsapiens.v86_2.99.0 [115] biomaRt_2.58.2 magick_2.8.3 av_0.9.0 [118] gifski_1.12.0-2 org.Hs.eg.db_3.18.0 GSEABase_1.64.0 [121] graph_1.80.0 annotate_1.80.0 XML_3.99-0.16.1 [124] pathview_1.42.0 enrichplot_1.22.0 GOSemSim_2.28.1 [127] annotables_0.2.0 multtest_2.58.0 multcomp_1.4-25 [130] TH.data_1.1-2 MASS_7.3-60 survival_3.5-5 [133] mvtnorm_1.2-4 nlme_3.1-162 gprofiler2_0.2.2 [136] future_1.33.1 phateR_1.0.7 qlcMatrix_0.9.7 [139] sparsesvd_0.2-2 slam_0.1-50 presto_1.0.0 [142] gplots_3.1.3.1 gganimate_1.0.8 igraph_2.0.2 [145] scCATCH_3.2.2 rmarkdown_2.25 plotly_4.10.4 [148] kableExtra_1.4.0 celldex_1.12.0 SingleR_2.4.1 [151] HGNChelper_0.8.1 scProportionTest_0.0.0.9000 DEGreport_1.38.5 [154] data.table_1.15.0 RColorBrewer_1.1-3 DESeq2_1.42.0 [157] png_0.1-8 apeglm_1.24.0 pheatmap_1.0.12 [160] reshape2_1.4.4 Matrix.utils_0.9.7 ensembldb_2.26.0 [163] AnnotationFilter_1.26.0 GenomicFeatures_1.54.3 AnnotationDbi_1.64.1 [166] AnnotationHub_3.10.0 BiocFileCache_2.10.1 dbplyr_2.4.0 [169] scales_1.3.0 RCurl_1.98-1.14 edgeR_4.0.15 [172] hablar_0.3.2 limma_3.58.1 speckle_1.2.0 [175] ggrepel_0.9.5 Azimuth_0.5.0 shinyBS_0.61.1 [178] hdWGCNA_0.2.19 WGCNA_1.72-5 fastcluster_1.2.6 [181] dynamicTreeCut_1.63-1 harmony_1.2.0 Rcpp_1.0.12 [184] DoubletFinder_2.0.4 pbmcref.SeuratData_1.0.0 SeuratData_0.2.2.9001 [187] SeuratDisk_0.0.0.9021 viridis_0.6.5 viridisLite_0.4.2 [190] patchwork_1.2.0 cowplot_1.1.3 Matrix_1.6-5 [193] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 [196] dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 [199] tidyr_1.3.1 tibble_3.2.1 ggplot2_3.4.4 [202] tidyverse_2.0.0 Seurat_5.0.1 SeuratObject_5.0.1 [205] sp_2.1-3 monocle3_1.3.4 SingleCellExperiment_1.24.0 [208] SummarizedExperiment_1.32.0 GenomicRanges_1.54.1 GenomeInfoDb_1.38.6 [211] IRanges_2.36.0 S4Vectors_0.40.2 MatrixGenerics_1.14.0 [214] matrixStats_1.2.0 Biobase_2.62.0 BiocGenerics_0.48.1 [217] devtools_2.4.5 usethis_2.2.2
loaded via a namespace (and not attached): [1] MatrixModels_0.5-3 arrow_14.0.0.2 doParallel_1.0.17 tidygraph_1.3.1 [5] pkgconfig_2.0.3 later_1.3.2 CNEr_1.38.0 corpcor_1.6.10 [9] aplot_0.2.2 circlize_0.4.16 parallel_4.3.1 promises_1.2.1 [13] leaps_3.1 statnet.common_4.9.0 Hmisc_5.1-1 rpart_4.1.19 [17] pbmcapply_1.5.1 sessioninfo_1.2.2 graphlayouts_1.1.0 spatstat.geom_3.2-8 [21] SQUAREM_2021.1 DBI_1.2.2 preprocessCore_1.64.0 insight_0.19.8 [25] urlchecker_1.0.1 gtable_0.3.4 minqa_1.2.6 filelock_1.0.3 [29] RcppHNSW_0.6.0 lattice_0.21-8 TFMPvalue_0.0.9 tidytree_0.4.6 [33] fANCOVA_0.6-1 ggstats_0.5.1 sctransform_0.4.1 IRkernel_1.3.2 [37] poweRlaw_0.80.0 caTools_1.18.2 ggforce_0.4.1 dotCall64_1.1-1 [41] systemfonts_1.0.5 RcppAnnoy_0.0.22 grr_0.9.5 glue_1.7.0 [45] curl_5.2.0 flashClust_1.01-2 pbapply_1.7-2 mashr_0.2.79 [49] mapproj_1.2.11 mixsqp_0.3-54 KernSmooth_2.23-21 bluster_1.12.0 [53] matrixcalc_1.0-6 RSQLite_2.3.5 furrr_0.3.1 munsell_0.5.0 [57] ScaledMatrix_1.10.0 lmerTest_3.1-3 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Rsamtools_2.18.0 boot_1.3-28.1 clue_0.3-65 estimability_1.4.1 [125] spatstat.utils_3.0-4 yaml_2.3.8 rsample_1.2.0 hms_1.1.3 [129] Rdpack_2.6 fastmatch_1.1-4 gower_1.0.1 uwot_0.1.16 [133] coda_0.19-4.1 evaluate_0.23 codetools_0.2-19 BiocNeighbors_1.20.2 [137] utf8_1.2.4 ellipsis_0.3.2 fgsea_1.28.0 ggpubr_0.6.0 [141] GetoptLong_1.0.5 EnvStats_2.8.1 fitdistrplus_1.1-11 spam_2.10-0 [145] GenomicAlignments_1.38.2 FNN_1.1.4 pkgmaker_0.32.10 beeswarm_0.4.0 [149] magrittr_2.0.3 gridBase_0.4-7 HDO.db_0.99.1 ggsignif_0.6.4 [153] glmnet_4.1-8 backports_1.4.1 multcompView_0.1-9 survey_4.2-1 [157] EnrichmentBrowser_2.32.0 tinytex_0.49 nnet_7.3-19 babelgene_22.9 [161] bdsmatrix_1.3-6 RANN_2.6.1 progressr_0.14.0 terra_1.7-71 [165] SparseM_1.81 doSNOW_1.0.20 locfdr_1.1-8 timeDate_4032.109 [169] ROCR_1.0-11 mitools_2.4 rstatix_0.7.2 fs_1.6.3 [173] mcmc_0.9-8 bslib_0.6.1 ggbeeswarm_0.7.2 KEGGREST_1.42.0 [177] crayon_1.5.2 deldir_2.0-2 listenv_0.9.1 gson_0.1.0 [181] BiocVersion_3.18.1 labeling_0.4.3 askpass_1.2.0 prettyunits_1.2.0 [185] ggtree_3.10.0 docopt_0.7.1 ModelMetrics_1.2.2.2 GO.db_3.18.0 [189] DelayedMatrixStats_1.24.0 tensor_1.5 ica_1.0-3 compiler_4.3.1 [193] yardstick_1.3.0 colorspace_2.1-0 scatterplot3d_0.3-44 maps_3.4.2 [197] mnormt_2.1.1 BSgenome.Hsapiens.UCSC.hg38_1.4.5 rappdirs_0.3.3 rhdf5filters_1.14.1 [201] withr_3.0.0 pracma_2.4.4 gtools_3.9.5 checkmate_2.3.1 [205] basilisk.utils_1.14.1 digest_0.6.34 httpuv_1.6.14 spatstat.explore_3.2-6 [209] emmeans_1.10.0 Rfast_2.1.0 emdbook_1.3.13 shinydashboard_0.7.2 [213] invgamma_1.1 RcppRoll_0.3.0 Rtsne_0.17 googledrive_2.1.1 [217] MCMCpack_1.7-0 R.oo_1.26.0 httr_1.4.7 jquerylib_0.1.4 [221] spatstat.random_3.2-2 DirichletMultinomial_1.44.0 locfit_1.5-9.8 timechange_0.3.0 [225] remaCor_0.0.18 mime_0.12 impute_1.76.0 htmlwidgets_1.6.4 [229] metapod_1.10.1 IRdisplay_1.1 pals_1.8 rminiconda_0.0.1 [233] recipes_1.0.10 ggfun_0.1.4 xbioc_0.1.19 Rgraphviz_2.46.0 [237] miniUI_0.1.1.1 goftest_1.2-3 cli_3.6.2 treeio_1.26.0 [241] BiocSingular_1.18.0 zip_2.3.1 globals_0.16.2 rhdf5_2.46.1 [245] lazyeval_0.2.2 reshape_0.8.9 repr_1.1.6 xgboost_1.7.7.1 [249] R.methodsS3_1.8.2 caret_6.0-94 RhpcBLASctl_0.23-42 GlobalOptions_0.1.2 [253] tweenr_2.0.2 pROC_1.18.5 lifecycle_1.0.4 gargle_1.5.2 [257] nloptr_2.0.3 aod_1.3.3 network_1.18.2 future.apply_1.11.1 [261] Formula_1.2-5 spatstat.sparse_3.0-3 GenomeInfoDbData_1.2.11 base64enc_0.1-3 [265] basilisk_1.14.3 prodlim_2023.08.28 blob_1.2.4 ggplotify_0.1.2 [269] class_7.3-22 ape_5.7-1 fansi_1.0.6 shinyjs_2.1.0 [273] base64url_1.4 tester_0.1.7 ucminf_1.2.1 htmltools_0.5.7 [277] R.utils_2.12.3 pbkrtest_0.5.2 e1071_1.7-14 bit64_4.0.5 [281] bitops_1.0-7 foreign_0.8-84 quantreg_5.97 rjson_0.2.21 [285] mclust_6.0.1 tools_4.3.1 ashr_2.2-63 snow_0.4-4 [289] pkgload_1.3.4 rmeta_3.0 irlba_2.3.5.1 spatstat.data_3.0-4 [293] statmod_1.5.0 ProtGenerics_1.34.0 fastmap_1.1.1 ConsensusClusterPlus_1.66.0 [297] googlesheets4_1.1.1 pbdZMQ_0.3-11 xfun_0.42 dichromat_2.0-0.1 [301] admisc_0.34 vctrs_0.6.5 cellranger_1.1.0 plyr_1.8.9 [305] broom_1.0.5 parsnip_1.2.0 numDeriv_2016.8-1.1 kernlab_0.9-32 [309] shadowtext_0.1.3 rbibutils_2.2.16
Hi, thank you very much for your interests in EnsDeconv. Besides, I also want to thank you for your detailed description. However, it's hard for me to truly understand what's going on for your code. I cannot replicate this error on my end. I am wondering could you provide a minimal data example that can reproduce the error? That would be truly appreciated.
alisyedraza1
Hi. I am running into the following error when running EnsDeconv():
Error in h(simpleError(msg, call)) : error in evaluating the argument 'x' in selecting a method for function 't': NA/NaN/Inf in foreign function call (arg 2)
I have checked the bulk-data structure and checked to see if there were any NA/NaN/Inf values. There are none. Also, the ref_mat seems to be fine as well as the meta_ref.
I did troubleshoot a bit. And observed the following:
This suggests to me that the error is coming from ref_list somewhere either from the ref_mat or meta_ref. It is just that when I check the structure of the list i.e. list from my data and the testdata$reflist, it is similar. I definitely need to run this and am looking for any solutions. Please let me know.
There is one thing - I had changed the rownames of the ref_mat i.e. the mat derived from the seurat object that I had. I had derived the mat (ref_matrix) from the seurat object in the following manner: object@assays$RNA$counts. But had changed the ID type of the rownames mat to align with that of the proteomic/bulk_proteomic dataframe i.e. proteomic/count_data.
Please let me know. Will appreciate any help. Thanks. Following was the session info:
R version 4.3.1 (2023-06-16) Platform: aarch64-apple-darwin20 (64-bit) Running under: macOS Monterey 12.7.3
Matrix products: default BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/New_York tzcode source: internal
attached base packages: [1] grid stats4 stats graphics grDevices utils datasets methods base
other attached packages: [1] ellipse_0.5.0 scran_1.30.2 sparseMatrixStats_1.14.0
[4] EnsDeconv_0.2.1 foreach_1.5.2 MuSiC_1.0.0
[7] TOAST_1.16.0 quadprog_1.5-8 EpiDISH_2.18.0
[10] nnls_1.5 RCy3_2.22.1 ggcorrplot_0.1.4.999
[13] factoextra_1.0.7 rgl_1.2.8 pastecs_1.4.2
[16] fastDummies_1.7.3 effects_4.2-2 car_3.1-2
[19] carData_3.0-5 compute.es_0.2-5 vioplot_0.4.0
[22] zoo_1.8-12 sm_2.2-6.0 CorLevelPlot_0.99.0
[25] GEOquery_2.70.0 RcisTarget_1.19.2 DelayedArray_0.28.0
[28] SparseArray_1.2.4 S4Arrays_1.2.0 abind_1.4-5
[31] qvalue_2.34.0 scattermore_1.2 zellkonverter_1.12.1
[34] Cairo_1.6-2 WriteXLS_6.5.0 SomaDataIO_6.0.0.9000
[37] tiff_0.1-12 readxl_1.4.3 pander_0.6.5
[40] clustifyr_1.14.0 ComplexHeatmap_2.18.0 Polychrome_1.5.1
[43] svglite_2.1.3 GOplot_1.0.2 gridExtra_2.3
[46] ggdendro_0.1.23 DOSE_3.28.2 clusterProfiler_4.10.0
[49] MAST_1.28.0 rsvd_1.0.5 TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 [52] knitr_1.45 GGally_2.2.1 PALMO_0.1.1
[55] wordcloud_2.6 EnhancedVolcano_1.20.0 NMF_0.27
[58] cluster_2.1.6 rngtools_1.5.2 registry_0.5-1
[61] ggalluvial_0.12.5 reticulate_1.35.0 CellChat_2.1.1
[64] loomR_0.2.1.9000 hdf5r_1.3.9 R6_2.5.1
[67] anndata_0.7.5.6 GeneOverlap_1.38.0 enrichR_3.2
[70] scDblFinder_1.16.0 scater_1.30.1 scuttle_1.12.0
[73] scAnnotatR_1.8.0 gt_0.10.1 zenith_1.4.2
[76] ExperimentHub_2.10.0 dreamlet_1.0.0 variancePartition_1.32.3
[79] gridGraphics_0.5-1 BiocManager_1.30.22 BiocParallel_1.36.0
[82] Augur_1.0.3 motifmatchr_1.24.0 TFBSTools_1.40.0
[85] JASPAR2020_0.99.10 chromVAR_1.24.0 BSgenome.Mmusculus.UCSC.mm10_1.4.3
[88] BSgenome_1.70.2 rtracklayer_1.62.0 BiocIO_1.12.0
[91] Biostrings_2.70.2 XVector_0.42.0 openxlsx_4.2.5.2
[94] EnsDb.Mmusculus.v79_2.99.0 Signac_1.12.0 clustree_0.5.1
[97] ggraph_2.1.0 lme4_1.1-35.1 SoupX_1.6.2
[100] msigdbr_7.5.1 umap_0.2.10.0 ordinal_2023.12-4
[103] OlinkAnalyze_3.6.2 AUCell_1.24.0 GENIE3_1.24.0
[106] SCENIC_1.3.1 SCopeLoomR_0.13.0 scTenifoldKnk_1.0.2
[109] SeuratWrappers_0.3.1 remotes_2.4.2.1 scTenifoldNet_1.3
[112] FactoMineR_2.9 maSigPro_1.74.0 EnsDb.Hsapiens.v86_2.99.0
[115] biomaRt_2.58.2 magick_2.8.3 av_0.9.0
[118] gifski_1.12.0-2 org.Hs.eg.db_3.18.0 GSEABase_1.64.0
[121] graph_1.80.0 annotate_1.80.0 XML_3.99-0.16.1
[124] pathview_1.42.0 enrichplot_1.22.0 GOSemSim_2.28.1
[127] annotables_0.2.0 multtest_2.58.0 multcomp_1.4-25
[130] TH.data_1.1-2 MASS_7.3-60 survival_3.5-5
[133] mvtnorm_1.2-4 nlme_3.1-162 gprofiler2_0.2.2
[136] future_1.33.1 phateR_1.0.7 qlcMatrix_0.9.7
[139] sparsesvd_0.2-2 slam_0.1-50 presto_1.0.0
[142] gplots_3.1.3.1 gganimate_1.0.8 igraph_2.0.2
[145] scCATCH_3.2.2 rmarkdown_2.25 plotly_4.10.4
[148] kableExtra_1.4.0 celldex_1.12.0 SingleR_2.4.1
[151] HGNChelper_0.8.1 scProportionTest_0.0.0.9000 DEGreport_1.38.5
[154] data.table_1.15.0 RColorBrewer_1.1-3 DESeq2_1.42.0
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