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quadbio / Pando

Multiome GRN inference.
https://quadbio.github.io/Pando/
MIT License
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Error in as_igraph_vs(graph, from) : Invalid vertex names while running plot_tf_network #58

Open yojetsharma opened 6 months ago

yojetsharma commented 6 months ago

I am trying to plot the tf network graph. However it gives me that error. Not sure what is happening. 

> ctrl_multiome <- get_tf_network(ctrl_multiome, tf='ZNFS21', graph='full_graph', keep_all_edges = T)
Getting shortest paths from TF
Error in as_igraph_vs(graph, from) : Invalid vertex names
> ctrl_multiome <- get_tf_network(ctrl_multiome, tf='ZNF521', graph='full_graph', keep_all_edges = T)
Getting shortest paths from TF
Error in as_igraph_vs(graph, from) : Invalid vertex names
> ctrl_multiome <- get_tf_network(ctrl_multiome, tf='TFAP2C', graph='full_graph', keep_all_edges = T)
Getting shortest paths from TF
Error in as_igraph_vs(graph, from) : Invalid vertex names
> ctrl_multiome <- get_tf_network(ctrl_multiome, tf='HES5', graph='full_graph', keep_all_edges = T)
Getting shortest paths from TF
Error in as_igraph_vs(graph, from) : Invalid vertex names
> ctrl_multiome <- get_tf_network(ctrl_multiome, tf='DLK1', graph='full_graph', keep_all_edges = T)
Getting shortest paths from TF
Error in as_igraph_vs(graph, from) : Invalid vertex names
> sessionInfo()
R version 4.2.1 (2022-06-23)
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/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
 [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
[10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] glmnet_4.1-8                      Matrix_1.6-5                     
 [3] doParallel_1.0.17                 iterators_1.0.14                 
 [5] foreach_1.5.2                     Pando_1.1.0                      
 [7] lubridate_1.9.3                   forcats_1.0.0                    
 [9] stringr_1.5.1                     dplyr_1.1.4                      
[11] purrr_1.0.2                       readr_2.1.5                      
[13] tidyr_1.3.1                       tibble_3.2.1                     
[15] ggplot2_3.5.0                     tidyverse_2.0.0                  
[17] sctransform_0.4.0                 BSgenome.Hsapiens.UCSC.hg38_1.4.5
[19] BSgenome_1.66.3                   rtracklayer_1.58.0               
[21] Biostrings_2.66.0                 XVector_0.38.0                   
[23] EnsDb.Hsapiens.v86_2.99.0         ensembldb_2.22.0                 
[25] AnnotationFilter_1.22.0           GenomicFeatures_1.50.4           
[27] AnnotationDbi_1.60.2              Biobase_2.58.0                   
[29] GenomicRanges_1.50.2              GenomeInfoDb_1.34.9              
[31] IRanges_2.32.0                    S4Vectors_0.36.2                 
[33] BiocGenerics_0.44.0               Seurat_5.0.2                     
[35] SeuratObject_5.0.1                sp_2.1-3                         
[37] Signac_1.12.0                    

loaded via a namespace (and not attached):
  [1] rappdirs_0.3.3              scattermore_1.2            
  [3] R.methodsS3_1.8.2           bit64_4.0.5                
  [5] knitr_1.45                  R.utils_2.12.3             
  [7] irlba_2.3.5.1               DelayedArray_0.24.0        
  [9] data.table_1.15.2           rpart_4.1.16               
 [11] TFBSTools_1.36.0            KEGGREST_1.38.0            
 [13] RCurl_1.98-1.14             generics_0.1.3             
 [15] cowplot_1.1.3               RSQLite_2.3.5              
 [17] RANN_2.6.1                  future_1.33.1              
 [19] ggpointdensity_0.1.0        bit_4.0.5                  
 [21] tzdb_0.4.0                  spatstat.data_3.0-4        
 [23] xml2_1.3.6                  httpuv_1.6.14              
 [25] SummarizedExperiment_1.28.0 DirichletMultinomial_1.40.0
 [27] viridis_0.6.5               xfun_0.42                  
 [29] hms_1.1.3                   evaluate_0.23              
 [31] promises_1.2.1              fansi_1.0.6                
 [33] restfulr_0.0.15             progress_1.2.3             
 [35] caTools_1.18.2              dbplyr_2.4.0               
 [37] igraph_2.0.2                DBI_1.2.2                  
 [39] htmlwidgets_1.6.4           sparsesvd_0.2-2            
 [41] spatstat.geom_3.2-9         ellipsis_0.3.2             
 [43] RSpectra_0.16-1             backports_1.4.1            
 [45] annotate_1.76.0             biomaRt_2.54.1             
 [47] deldir_2.0-4                sparseMatrixStats_1.10.0   
 [49] MatrixGenerics_1.10.0       vctrs_0.6.5                
 [51] remotes_2.4.2.1             ROCR_1.0-11                
 [53] abind_1.4-5                 cachem_1.0.8               
 [55] withr_3.0.0                 grr_0.9.5                  
 [57] ggforce_0.4.2               progressr_0.14.0           
 [59] presto_1.0.0                checkmate_2.3.1            
 [61] GenomicAlignments_1.34.1    prettyunits_1.2.0          
 [63] goftest_1.2-3               cluster_2.1.3              
 [65] seqLogo_1.64.0              dotCall64_1.1-1            
 [67] lazyeval_0.2.2              crayon_1.5.2               
 [69] hdf5r_1.3.10                spatstat.explore_3.2-6     
 [71] pkgconfig_2.0.3             slam_0.1-50                
 [73] labeling_0.4.3              tweenr_2.0.3               
 [75] nlme_3.1-157                ProtGenerics_1.30.0        
 [77] pals_1.8                    nnet_7.3-17                
 [79] rlang_1.1.3                 globals_0.16.3             
 [81] lifecycle_1.0.4             miniUI_0.1.1.1             
 [83] filelock_1.0.3              fastDummies_1.7.3          
 [85] BiocFileCache_2.6.1         dichromat_2.0-0.1          
 [87] polyclip_1.10-6             RcppHNSW_0.6.0             
 [89] matrixStats_1.2.0           lmtest_0.9-40              
 [91] zoo_1.8-12                  base64enc_0.1-3            
 [93] ggridges_0.5.6              png_0.1-8                  
 [95] viridisLite_0.4.2           rjson_0.2.21               
 [97] bitops_1.0-7                R.oo_1.26.0                
 [99] KernSmooth_2.23-20          spam_2.10-0                
[101] blob_1.2.4                  DelayedMatrixStats_1.20.0  
[103] shape_1.4.6.1               parallelly_1.37.1          
[105] spatstat.random_3.2-3       CNEr_1.34.0                
[107] scales_1.3.0                memoise_2.0.1              
[109] magrittr_2.0.3              plyr_1.8.9                 
[111] ica_1.0-3                   zlibbioc_1.44.0            
[113] compiler_4.2.1              BiocIO_1.8.0               
[115] RColorBrewer_1.1-3          fitdistrplus_1.1-11        
[117] Rsamtools_2.14.0            cli_3.6.2                  
[119] listenv_0.9.1               patchwork_1.2.0            
[121] pbapply_1.7-2               htmlTable_2.4.2            
[123] Formula_1.2-5               MASS_7.3-57                
[125] tidyselect_1.2.0            stringi_1.8.3              
[127] glmGamPoi_1.10.2            yaml_2.3.8                 
[129] ggrepel_0.9.5               grid_4.2.1                 
[131] VariantAnnotation_1.44.1    fastmatch_1.1-4            
[133] tools_4.2.1                 timechange_0.3.0           
[135] future.apply_1.11.1         rstudioapi_0.15.0          
[137] TFMPvalue_0.0.9             foreign_0.8-82             
[139] gridExtra_2.3               farver_2.1.1               
[141] Rtsne_0.17                  ggraph_2.2.1               
[143] digest_0.6.34               BiocManager_1.30.22        
[145] FNN_1.1.4                   pracma_2.4.4               
[147] shiny_1.8.0                 qlcMatrix_0.9.7            
[149] motifmatchr_1.20.0          Rcpp_1.0.12                
[151] later_1.3.2                 RcppAnnoy_0.0.22           
[153] httr_1.4.7                  biovizBase_1.46.0          
[155] colorspace_2.1-0            XML_3.99-0.16.1            
[157] tensor_1.5                  reticulate_1.35.0          
[159] splines_4.2.1               uwot_0.1.16                
[161] RcppRoll_0.3.0              spatstat.utils_3.0-4       
[163] graphlayouts_1.1.0          mapproj_1.2.11             
[165] plotly_4.10.4               xtable_1.8-4               
[167] poweRlaw_0.80.0             jsonlite_1.8.8             
[169] tidygraph_1.3.1             R6_2.5.1                   
[171] Hmisc_5.1-1                 pillar_1.9.0               
[173] htmltools_0.5.7             mime_0.12                  
[175] glue_1.7.0                  fastmap_1.1.1              
[177] BiocParallel_1.32.6         codetools_0.2-18           
[179] maps_3.4.2                  utf8_1.2.4                 
[181] lattice_0.20-45             spatstat.sparse_3.0-3      
[183] curl_5.2.1                  leiden_0.4.3.1             
[185] gtools_3.9.5                GO.db_3.16.0               
[187] survival_3.3-1              rmarkdown_2.26             
[189] docopt_0.7.1                munsell_0.5.0              
[191] GenomeInfoDbData_1.2.9      reshape2_1.4.4             
[193] gtable_0.3.4
joschif commented 6 months ago

I'm not fully sure what the problem here is. Maybe you can check if the TFs you want are indeed in the module graph computed by Pando. It's possible that they were filtered out in the module discovery step