The 2.0.5 clonalCluster example code on the vignette doesn't work on dev branch

[Qile0317]

The clonalCluster example on the current 2.0.5 vignette page when ran on the current dev branch does not work. Here are the steps to reproduce it on my machine:

data("scRep_example")

scRep_example$Patient <- substr(scRep_example$orig.ident, 1,3)
scRep_example$Type <- substr(scRep_example$orig.ident, 4,4)
colorblind_vector <- hcl.colors(n=7, palette = "inferno", fixup = TRUE)

scRep_example <- clonalCluster(scRep_example, 
                               chain = "TRA", 
                               sequence = "aa", 
                               threshold = 0.85, 
                               group.by = "Patient")

This produces the following error and traceback (from the second last):

Error in eval(e, x, parent.frame()) : object 'cloneSize' not found
> traceback()
8: eval(e, x, parent.frame())
7: eval(e, x, parent.frame())
6: subset.data.frame(subset, !is.na(cloneSize))
5: subset(subset, !is.na(cloneSize)) at utils.R#557
4: .expression2List(df, split.by) at utils.R#185
3: .list.input.return(df, split.by) at utils.R#567
2: .data.wrangle(input.data, group.by, "CTgene", chain) at clonalCluster.R#67

Additionally, I am having issues with certain other tests but getting other related errors. I am unsure if this is linked to any recent PRs that unintentionally changed certain behaviour and will investigate. I have also yet to investigate whether this issue exists on a previous patch version. This is probably the best way to investigate this issue.

SessionInfo:

R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.1 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/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0

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

time zone: America/Los_Angeles
tzcode source: system (glibc)

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

other attached packages:
[1] scRepertoire_2.0.8 ggplot2_3.5.1      testthat_3.2.1.1  

loaded via a namespace (and not attached):
  [1] fs_1.6.4                    matrixStats_1.4.1          
  [3] spatstat.sparse_3.1-0       devtools_2.4.5             
  [5] lubridate_1.9.3             httr_1.4.7                 
  [7] RColorBrewer_1.1-3          profvis_0.4.0              
  [9] tools_4.4.1                 sctransform_0.4.1          
 [11] utf8_1.2.4                  R6_2.5.1                   
 [13] lazyeval_0.2.2              uwot_0.2.2                 
 [15] urlchecker_1.0.1            withr_3.0.2                
 [17] sp_2.1-4                    gridExtra_2.3              
 [19] progressr_0.14.0            quantreg_5.99              
 [21] cli_3.6.3                   Biobase_2.64.0             
 [23] spatstat.explore_3.3-3      fastDummies_1.7.4          
 [25] iNEXT_3.0.1                 labeling_0.4.3             
 [27] Seurat_5.1.0                spatstat.data_3.1-2        
 [29] ggridges_0.5.6              pbapply_1.7-2              
 [31] commonmark_1.9.2            stringdist_0.9.12          
 [33] parallelly_1.38.0           sessioninfo_1.2.2          
 [35] VGAM_1.1-12                 rstudioapi_0.17.1          
 [37] generics_0.1.3              ica_1.0-3                  
 [39] spatstat.random_3.3-2       dplyr_1.1.4                
 [41] Matrix_1.7-1                waldo_0.5.3                
 [43] fansi_1.0.6                 S4Vectors_0.42.1           
 [45] abind_1.4-8                 lifecycle_1.0.4            
 [47] SummarizedExperiment_1.34.0 SparseArray_1.4.8          
 [49] Rtsne_0.17                  grid_4.4.1                 
 [51] promises_1.3.0              crayon_1.5.3               
 [53] miniUI_0.1.1.1              lattice_0.22-6             
 [55] cowplot_1.1.3               pillar_1.9.0               
 [57] knitr_1.48                  GenomicRanges_1.56.2       
 [59] rjson_0.2.23                future.apply_1.11.3        
 [61] codetools_0.2-20            leiden_0.4.3.1             
 [63] glue_1.8.0                  spatstat.univar_3.0-1      
 [65] data.table_1.16.2           remotes_2.5.0              
 [67] vctrs_0.6.5                 png_0.1-8                  
 [69] spam_2.11-0                 gtable_0.3.6               
 [71] assertthat_0.2.1            cachem_1.1.0               
 [73] xfun_0.48                   S4Arrays_1.4.1             
 [75] mime_0.12                   tidygraph_1.3.1            
 [77] survival_3.7-0              SingleCellExperiment_1.26.0
 [79] ellipsis_0.3.2              fitdistrplus_1.2-1         
 [81] ROCR_1.0-11                 nlme_3.1-166               
 [83] usethis_3.0.0               RcppAnnoy_0.0.22           
 [85] evd_2.3-7.1                 GenomeInfoDb_1.40.1        
 [87] rprojroot_2.0.4             irlba_2.3.5.1              
 [89] KernSmooth_2.23-24          colorspace_2.1-1           
 [91] BiocGenerics_0.50.0         tidyselect_1.2.1           
 [93] processx_3.8.4              compiler_4.4.1             
 [95] vdiffr_1.0.7                SparseM_1.84-2             
 [97] xml2_1.3.6                  desc_1.4.3                 
 [99] ggdendro_0.2.0              DelayedArray_0.30.1        
[101] plotly_4.10.4               scales_1.3.0               
[103] lmtest_0.9-40               callr_3.7.6                
[105] stringr_1.5.1               digest_0.6.37              
[107] goftest_1.2-3               spatstat.utils_3.1-0       
[109] XVector_0.44.0              htmltools_0.5.8.1          
[111] pkgconfig_2.0.3             MatrixGenerics_1.16.0      
[113] fastmap_1.2.0               rlang_1.1.4                
[115] htmlwidgets_1.6.4           UCSC.utils_1.0.0           
[117] shiny_1.9.1                 farver_2.1.2               
[119] zoo_1.8-12                  jsonlite_1.8.9             
[121] magrittr_2.0.3              GenomeInfoDbData_1.2.12    
[123] dotCall64_1.2               patchwork_1.3.0            
[125] munsell_0.5.1               Rcpp_1.0.13                
[127] evmix_2.12                  viridis_0.6.5              
[129] reticulate_1.39.0           truncdist_1.0-2            
[131] stringi_1.8.4               ggalluvial_0.12.5          
[133] ggraph_2.2.1                brio_1.1.5                 
[135] zlibbioc_1.50.0             MASS_7.3-61                
[137] plyr_1.8.9                  pkgbuild_1.4.5             
[139] parallel_4.4.1              listenv_0.9.1              
[141] ggrepel_0.9.6               deldir_2.0-4               
[143] graphlayouts_1.2.0          splines_4.4.1              
[145] tensor_1.5                  hash_2.2.6.3               
[147] ps_1.8.1                    igraph_2.1.1               
[149] spatstat.geom_3.3-3         cubature_2.1.1             
[151] RcppHNSW_0.6.0              reshape2_1.4.4             
[153] stats4_4.4.1                pkgload_1.4.0              
[155] SeuratObject_5.0.2          tweenr_2.0.3               
[157] httpuv_1.6.15               MatrixModels_0.5-3         
[159] RANN_2.6.2                  tidyr_1.3.1                
[161] purrr_1.0.2                 polyclip_1.10-7            
[163] future_1.34.0               scattermore_1.2            
[165] ggforce_0.4.2               xtable_1.8-4               
[167] RSpectra_0.16-2             roxygen2_7.3.2             
[169] later_1.3.2                 viridisLite_0.4.2          
[171] gsl_2.1-8                   tibble_3.2.1               
[173] memoise_2.0.1               IRanges_2.38.1             
[175] cluster_2.1.6               timechange_0.3.0           
[177] globals_0.16.3 

[Qile0317]

Also if my hypothesis that some recent PR accidentally changed some intended behaviour of certain functions is correct, then this may or may not be related to #433. Also a high chance that these are completely unrelated though as I am unsure which branch they even used.

[ncborcherding]

There is only one place that the following phrase exists - which is in the internal function .expression2List()

subset.data.frame(subset, !is.na(cloneSize))

I think this means that the clones have not been added to the object scRep_example. Especially because the following code does not have a discrete combineExpression() call.

data("scRep_example")

scRep_example$Patient <- substr(scRep_example$orig.ident, 1,3)
scRep_example$Type <- substr(scRep_example$orig.ident, 4,4)
colorblind_vector <- hcl.colors(n=7, palette = "inferno", fixup = TRUE)

scRep_example <- clonalCluster(scRep_example, 
                               chain = "TRA", 
                               sequence = "aa", 
                               threshold = 0.85, 
                               group.by = "Patient")

[Qile0317]

@ncborcherding Ok - combined the contig list but now the TRA_clusters metadata column is almost all NAs which does not line up with the visualization showing many clusters:

data("contig_list")
combined <- combineTCR(contig_list, 
                           samples = c("P17B", "P17L", "P18B", "P18L", 
                                            "P19B","P19L", "P20B", "P20L"),
                           removeNA = FALSE, 
                           removeMulti = FALSE, 
                           filterMulti = FALSE)

data("scRep_example")
scRep_example <- combineExpression(combined, scRep_example)

scRep_example$Patient <- substr(scRep_example$orig.ident, 1,3)

#Adding type information
scRep_example$Type <- substr(scRep_example$orig.ident, 4,4)

#Define color palette 
colorblind_vector <- hcl.colors(n=7, palette = "inferno", fixup = TRUE)

scRep_example <- clonalCluster(scRep_example, 
                               chain = "TRA", 
                               sequence = "aa", 
                               threshold = 0.85, 
                               group.by = "Patient")

mean(!is.na(scRep_example$TRA_cluster))
# outputs 0.008 (only 4 non-NA rows)

Perhaps I missed some prior step in the vignette where scRep example was further modified? Just trying to get to the bottom of this so I can ensure correct behaviour for #425

Probably also will add an internal assert checker that an object has been combined.

[ncborcherding]

@Qile0317

The issue is you are using the small scRep_example Seurat object that has 500 randomly sampled cells (~350 have clones). The vignette on the website uses the full Seurat object.

If you just look at the output of combined.TCR(), you can get a better approximation:

combined <- addVariable(combined, 
                        variable.name = "Patient", 
                        variables = c("P17", "P17", "P18", "P18", 
                                      "P19","P19", "P20", "P20"))
combined <- clonalCluster(combined, 
                               chain = "TRA", 
                               sequence = "aa", 
                               threshold = 0.85, 
                               group.by = "Patient")

mean(!is.na(do.call(rbind, combined)))

[1] 0.9462471

I will work on troubleshooting #425 tomorrow/next week and see if I can help find the issue.

Nick