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immunogenomics / presto

Fast Wilcoxon and auROC
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Error in dplyr::select(): ! <text>:1:5: unexpected symbol #20

Open gwendolinelecuyer opened 1 year ago

gwendolinelecuyer commented 1 year ago

Hello, I'm getting the following error when I run wilcoxauc()

DEG=wilcoxauc(sub.compare, 'treatment', seurat_assay='RNA', assay='data')

############### Error in dplyr::select(): ! :1:5: unexpected symbol 1: Use of ^ rlang::last_error() <simpleError in dplyr::select(., .data$feature, .data$group, .data$avgExpr, .data$logFC, .data$statistic, .data$auc, .data$pval, .data$padj, .data$pct_in, .data$pct_out): :1:5: unexpected symbol 1: Use of ^> ################

I had the same kind of issue with harmony, which was solve with this post https://github.com/immunogenomics/harmony/issues/173 I tried to do the same thing:

presto.tidy_results.new <- function (wide_res, features, groups) { res <- Reduce(cbind, lapply(wide_res, as.numeric)) %>% data.frame() colnames(res) <- names(wide_res) res$feature <- rep(features, times = length(groups)) res$group <- rep(groups, each = length(features)) res %>% dplyr::select(data$feature, data$group, data$avgExpr, data$logFC, data$statistic, data$auc, data$pval, data$padj, data$pct_in, data$pct_out) }

environment(presto.tidy_results.new) <- asNamespace('presto') assignInNamespace("tidy_results", presto.tidy_results.new, ns = "presto")

But I still have an error: ######## Error in dplyr::select(): ! Problem while evaluating data$feature. Run rlang::last_error() to see where the error occurred.

rlang::last_error() <error/rlang_error> Error in dplyr::select(): ! Problem while evaluating data$feature.

Backtrace:

  1. presto::wilcoxauc(...)
  2. dplyr:::select.data.frame(...)
  3. tidyselect::eval_select(expr(c(...)), .data)
  4. tidyselect:::eval_select_impl(...)
  5. tidyselect:::vars_select_eval(...)
  6. tidyselect:::walk_data_tree(expr, data_mask, context_mask)
  7. tidyselect:::eval_c(expr, data_mask, context_mask)
  8. tidyselect:::reduce_sels(node, data_mask, context_mask, init = init)
  9. tidyselect:::walk_data_tree(new, data_mask, context_mask)
  10. tidyselect:::eval_context(expr, context_mask, call = error_call)
  11. rlang::eval_tidy(as_quosure(expr, env), context_mask) Run rlang::last_trace() to see the full context. ###########

Sincerely, Gwendoline

Here my sessionInfo R version 4.0.5 (2021-03-31) Platform: x86_64-conda-linux-gnu (64-bit) Running under: CentOS Linux 7 (Core)

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

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

other attached packages: [1] forcats_0.5.1 purrr_0.3.5 [3] readr_2.1.2 tidyr_1.2.0 [5] tibble_3.1.8 tidyverse_1.3.2 [7] UpSetR_1.4.0 presto_1.0.0 [9] data.table_1.14.6 Rcpp_1.0.9 [11] pheatmap_1.0.12 RColorBrewer_1.1-3 [13] ggplot2_3.3.6 reshape2_1.4.4 [15] scales_1.2.1 stringr_1.4.1 [17] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0 [19] Biobase_2.50.0 GenomicRanges_1.42.0 [21] GenomeInfoDb_1.26.7 IRanges_2.24.1 [23] S4Vectors_0.28.1 BiocGenerics_0.36.1 [25] MatrixGenerics_1.2.1 matrixStats_0.63.0 [27] SeuratObject_4.1.3 Seurat_4.3.0 [29] dplyr_1.0.10

loaded via a namespace (and not attached): [1] readxl_1.4.0 backports_1.4.1 plyr_1.8.8 [4] igraph_1.3.4 lazyeval_0.2.2 sp_1.5-1 [7] splines_4.0.5 listenv_0.8.0 scattermore_0.8 [10] digest_0.6.30 htmltools_0.5.3 fansi_1.0.3 [13] magrittr_2.0.3 tensor_1.5 googlesheets4_1.0.1 [16] cluster_2.1.4 ROCR_1.0-11 tzdb_0.3.0 [19] globals_0.16.2 modelr_0.1.9 spatstat.sparse_3.0-0 [22] colorspace_2.0-3 rvest_1.0.2 ggrepel_0.9.2 [25] haven_2.5.0 crayon_1.5.2 RCurl_1.98-1.8 [28] jsonlite_1.8.3 progressr_0.11.0 spatstat.data_3.0-0 [31] survival_3.4-0 zoo_1.8-11 glue_1.6.2 [34] polyclip_1.10-4 gtable_0.3.1 gargle_1.2.1 [37] zlibbioc_1.36.0 XVector_0.30.0 leiden_0.4.3 [40] DelayedArray_0.16.3 future.apply_1.10.0 abind_1.4-5 [43] DBI_1.1.3 spatstat.random_3.0-1 miniUI_0.1.1.1 [46] viridisLite_0.4.1 xtable_1.8-4 reticulate_1.25 [49] htmlwidgets_1.5.4 httr_1.4.4 ellipsis_0.3.2 [52] ica_1.0-3 pkgconfig_2.0.3 uwot_0.1.14 [55] dbplyr_2.2.1 deldir_1.0-6 utf8_1.2.2 [58] tidyselect_1.2.0 rlang_1.0.6 later_1.3.0 [61] munsell_0.5.0 cellranger_1.1.0 tools_4.0.5 [64] cli_3.4.1 generics_0.1.3 broom_1.0.1 [67] ggridges_0.5.4 fastmap_1.1.0 goftest_1.2-3 [70] fs_1.5.2 fitdistrplus_1.1-8 RANN_2.6.1 [73] pbapply_1.6-0 future_1.29.0 nlme_3.1-159 [76] mime_0.12 xml2_1.3.3 compiler_4.0.5 [79] plotly_4.10.1 png_0.1-8 spatstat.utils_3.0-1 [82] reprex_2.0.1 stringi_1.7.8 lattice_0.20-45 [85] Matrix_1.5-3 vctrs_0.4.1 pillar_1.8.1 [88] lifecycle_1.0.1 spatstat.geom_3.0-3 lmtest_0.9-40 [91] RcppAnnoy_0.0.20 cowplot_1.1.1 bitops_1.0-7 [94] irlba_2.3.5.1 httpuv_1.6.2 patchwork_1.1.2 [97] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20 [100] gridExtra_2.3 parallelly_1.32.1 codetools_0.2-18 [103] MASS_7.3-58.1 assertthat_0.2.1 withr_2.5.0 [106] sctransform_0.3.5 GenomeInfoDbData_1.2.4 hms_1.1.2 [109] grid_4.0.5 googledrive_2.0.0 Rtsne_0.16 [112] spatstat.explore_3.0-5 shiny_1.7.3 lubridate_1.8.0