Extracting middle knot dots when using embedCurves from slingshot

[kmh005]

I originally created my SlingShot object in PCA space, but have been visualizing it in UMAP space via the slingshot function

crv_new<-embedCurves(crv, "UMAP")

This new crv_new plots fine in plotSmoothers and plotGeneCount, with the same lines and start and end points. However, in my 10 knots/7 lineages, I'm unable to plot the middle dots for knots. Is there a way to extract these from the ggplot2 object, similar to #226, that you could provide?

This does not plot the middle knots:

plotGeneCount(curve=crv_new, counts=counts, clusters="minor_cluster", models = sce_gam)

and following #226 allows me to embolden the lineage lines one-by-one, but not plot intermediate knots.

Thanks in advance for any tips you can provide!

SessionInfo()
R version 4.2.3 (2023-03-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.1 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

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] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] viridis_0.6.2               viridisLite_0.4.1          
 [3] pheatmap_1.0.12             scran_1.26.2               
 [5] scuttle_1.8.4               wesanderson_0.3.6          
 [7] Seurat_5.0.0                SeuratObject_5.0.0         
 [9] sp_1.6-0                    ggplot2_3.4.2              
[11] cowplot_1.1.1               dplyr_1.1.1                
[13] BiocParallel_1.32.6         tradeSeq_1.12.0            
[15] slingshot_2.6.0             TrajectoryUtils_1.6.0      
[17] SingleCellExperiment_1.20.1 SummarizedExperiment_1.28.0
[19] Biobase_2.58.0              GenomicRanges_1.50.2       
[21] GenomeInfoDb_1.34.9         IRanges_2.32.0             
[23] S4Vectors_0.36.2            BiocGenerics_0.44.0        
[25] MatrixGenerics_1.10.0       matrixStats_0.63.0         
[27] princurve_2.1.6            

loaded via a namespace (and not attached):
  [1] spam_2.9-1                systemfonts_1.0.4        
  [3] plyr_1.8.8                igraph_1.4.2             
  [5] lazyeval_0.2.2            splines_4.2.3            
  [7] RcppHNSW_0.4.1            listenv_0.9.0            
  [9] scattermore_1.2           digest_0.6.31            
 [11] htmltools_0.5.5           fansi_1.0.4              
 [13] magrittr_2.0.3            ScaledMatrix_1.6.0       
 [15] tensor_1.5                cluster_2.1.4            
 [17] ROCR_1.0-11               limma_3.54.2             
 [19] globals_0.16.2            spatstat.sparse_3.0-1    
 [21] colorspace_2.1-0          ggrepel_0.9.3            
 [23] textshaping_0.3.6         RCurl_1.98-1.12          
 [25] jsonlite_1.8.4            progressr_0.13.0         
 [27] spatstat.data_3.0-1       survival_3.5-5           
 [29] zoo_1.8-12                glue_1.6.2               
 [31] polyclip_1.10-4           gtable_0.3.3             
 [33] zlibbioc_1.44.0           XVector_0.38.0           
 [35] leiden_0.4.3              DelayedArray_0.24.0      
 [37] BiocSingular_1.14.0       future.apply_1.10.0      
 [39] abind_1.4-5               scales_1.2.1             
 [41] edgeR_3.40.2              spatstat.random_3.1-4    
 [43] miniUI_0.1.1.1            Rcpp_1.0.10              
 [45] xtable_1.8-4              dqrng_0.3.0              
 [47] reticulate_1.28           rsvd_1.0.5               
 [49] dotCall64_1.0-2           metapod_1.6.0            
 [51] htmlwidgets_1.6.2         httr_1.4.5               
 [53] RColorBrewer_1.1-3        ellipsis_0.3.2           
 [55] ica_1.0-3                 pkgconfig_2.0.3          
 [57] farver_2.1.1              uwot_0.1.14              
 [59] deldir_1.0-6              locfit_1.5-9.7           
 [61] utf8_1.2.3                tidyselect_1.2.0         
 [63] labeling_0.4.2            rlang_1.1.0              
 [65] reshape2_1.4.4            later_1.3.0              
 [67] munsell_0.5.0             tools_4.2.3              
 [69] cli_3.6.1                 generics_0.1.3           
 [71] ggridges_0.5.4            stringr_1.5.0            
 [73] fastmap_1.1.1             ragg_1.2.5               
 [75] goftest_1.2-3             fitdistrplus_1.1-8       
 [77] purrr_1.0.1               RANN_2.6.1               
 [79] sparseMatrixStats_1.10.0  pbapply_1.7-0            
 [81] future_1.32.0             nlme_3.1-162             
 [83] mime_0.12                 compiler_4.2.3           
 [85] rstudioapi_0.14           plotly_4.10.1            
 [87] png_0.1-8                 spatstat.utils_3.0-2     
 [89] statmod_1.5.0             tibble_3.2.1             
 [91] stringi_1.7.12            RSpectra_0.16-1          
 [93] bluster_1.8.0             lattice_0.21-8           
 [95] Matrix_1.6-5              vctrs_0.6.2              
 [97] pillar_1.9.0              lifecycle_1.0.3          
 [99] BiocManager_1.30.20       spatstat.geom_3.1-0      
[101] lmtest_0.9-40             BiocNeighbors_1.16.0     
[103] RcppAnnoy_0.0.20          data.table_1.14.8        
[105] bitops_1.0-7              irlba_2.3.5.1            
[107] httpuv_1.6.9              patchwork_1.1.2          
[109] R6_2.5.1                  promises_1.2.0.1         
[111] KernSmooth_2.23-20        gridExtra_2.3            
[113] parallelly_1.35.0         codetools_0.2-19         
[115] fastDummies_1.6.3         MASS_7.3-58.3            
[117] withr_2.5.0               sctransform_0.4.1        
[119] GenomeInfoDbData_1.2.9    mgcv_1.8-42              
[121] parallel_4.2.3            beachmat_2.14.2          
[123] grid_4.2.3                tidyr_1.3.0              
[125] DelayedMatrixStats_1.20.0 Rtsne_0.16               
[127] spatstat.explore_3.1-0    shiny_1.7.4