csangara
commented
1 year ago Hi Jasper,
From #124
The warning comes from 1 or 2 ligands in the ligand-target matrix that have the same prediction value across all evaluated genes. You should not worry about that.
Dear developers,
thanks for this great tool! I am working with a snRNAseq dataset trying to identify a signaling network between resident cell types in a model for inflammation. I just followed your Differential NicheNet analysis, which was a great asset to my analysis. Durin the workflow, I c encountered the following warning message:
Warning: all target gene probability score predictions have same valueWarning: the standard deviation is zeroWarning: the standard deviation is zero.
when executing the following command:
ligand_activities_targets = get_ligand_activities_targets(niche_geneset_list = niche_geneset_list, ligand_target_matrix = ligand_target_matrix, top_n_target = top_n_target)
I was able to continue my analysis which yielded some biologically meaningful results but still, I was wondering what this warning message means. When I look at the "ligand_activities_targets" dataframe, the "activity" metrics differ between the different signaling pathways, so I don't see why the warning message claims that they're all the same.
Thanks a lot!
Best,
Jasper
Here is my SessionInfo():
R version 4.2.2 Patched (2022-11-10 r83330) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 20.04.5 LTS
Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0 LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=de_DE.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=de_DE.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C
attached base packages: [1] stats graphics grDevices utils datasets methods base
other attached packages: [1] RColorBrewer_1.1-3 scCustomize_1.1.1 circlize_0.4.15 nichenetr_1.1.1 data.table_1.14.8 lubridate_1.9.2
[7] forcats_1.0.0 stringr_1.5.0 dplyr_1.1.1 purrr_1.0.1 readr_2.1.4 tidyr_1.3.0
[13] tibble_3.2.1 ggplot2_3.4.1 tidyverse_2.0.0 SeuratObject_4.1.3 Seurat_4.3.0
loaded via a namespace (and not attached): [1] utf8_1.2.3 spatstat.explore_3.1-0 reticulate_1.28 tidyselect_1.2.0 htmlwidgets_1.6.2
[6] grid_4.2.2 Rtsne_0.16 pROC_1.18.0 munsell_0.5.0 codetools_0.2-19
[11] ica_1.0-3 future_1.32.0 miniUI_0.1.1.1 withr_2.5.0 spatstat.random_3.1-4 [16] colorspace_2.1-0 progressr_0.13.0 knitr_1.42 rstudioapi_0.14 stats4_4.2.2
[21] ROCR_1.0-11 ggsignif_0.6.4 tensor_1.5 listenv_0.9.0 labeling_0.4.2
[26] polyclip_1.10-4 farver_2.1.1 parallelly_1.35.0 vctrs_0.6.1 generics_0.1.3
[31] ipred_0.9-14 xfun_0.38 timechange_0.2.0 randomForest_4.7-1.1 R6_2.5.1
[36] doParallel_1.0.17 ggbeeswarm_0.7.1 clue_0.3-64 bitops_1.0-7 spatstat.utils_3.0-2
[41] promises_1.2.0.1 scales_1.2.1 nnet_7.3-18 beeswarm_0.4.0 gtable_0.3.3
[46] globals_0.16.2 goftest_1.2-3 timeDate_4022.108 rlang_1.1.0 GlobalOptions_0.1.2
[51] splines_4.2.2 rstatix_0.7.2 lazyeval_0.2.2 ModelMetrics_1.2.2.2 broom_1.0.4
[56] spatstat.geom_3.1-0 checkmate_2.1.0 yaml_2.3.7 reshape2_1.4.4 abind_1.4-5
[61] backports_1.4.1 httpuv_1.6.9 Hmisc_5.0-1 caret_6.0-94 DiagrammeR_1.0.9
[66] tools_4.2.2 lava_1.7.2.1 ellipsis_0.3.2 proxy_0.4-27 BiocGenerics_0.44.0
[71] ggridges_0.5.4 Rcpp_1.0.10 plyr_1.8.8 base64enc_0.1-3 visNetwork_2.1.2
[76] ggpubr_0.6.0 rpart_4.1.19 deldir_1.0-6 pbapply_1.7-0 GetoptLong_1.0.5
[81] cowplot_1.1.1 S4Vectors_0.36.2 zoo_1.8-11 ggrepel_0.9.3 cluster_2.1.4
[86] magrittr_2.0.3 scattermore_0.8 lmtest_0.9-40 RANN_2.6.1 fitdistrplus_1.1-8
[91] matrixStats_0.63.0 hms_1.1.3 patchwork_1.1.2 mime_0.12 evaluate_0.20
[96] xtable_1.8-4 IRanges_2.32.0 gridExtra_2.3 shape_1.4.6 compiler_4.2.2
[101] KernSmooth_2.23-20 crayon_1.5.2 htmltools_0.5.5 later_1.3.0 tzdb_0.3.0
[106] ggprism_1.0.4 Formula_1.2-5 DBI_1.1.3 ComplexHeatmap_2.14.0 MASS_7.3-58.3
[111] car_3.1-1 Matrix_1.5-3 cli_3.6.1 parallel_4.2.2 gower_1.0.1
[116] igraph_1.4.1 pkgconfig_2.0.3 foreign_0.8-84 sp_1.6-0 plotly_4.10.1
[121] spatstat.sparse_3.0-1 recipes_1.0.5 paletteer_1.5.0 foreach_1.5.2 vipor_0.4.5
[126] hardhat_1.2.0 prodlim_2019.11.13 snakecase_0.11.0 digest_0.6.31 sctransform_0.3.5
[131] RcppAnnoy_0.0.20 janitor_2.2.0 spatstat.data_3.0-1 rmarkdown_2.20 leiden_0.4.3
[136] htmlTable_2.4.1 uwot_0.1.14 shiny_1.7.4 rjson_0.2.21 lifecycle_1.0.3
[141] nlme_3.1-162 jsonlite_1.8.4 carData_3.0-5 viridisLite_0.4.1 limma_3.54.2
[146] fansi_1.0.4 pillar_1.9.0 lattice_0.20-45 ggrastr_1.0.1 fastmap_1.1.1
[151] httr_1.4.5 survival_3.5-5 glue_1.6.2 fdrtool_1.2.17 png_0.1-8
[156] iterators_1.0.14 class_7.3-21 stringi_1.7.12 rematch2_2.1.2 caTools_1.18.2
[161] irlba_2.3.5.1 e1071_1.7-13 future.apply_1.10.0