Issue combining single-cell experiment object and combined.BCR screpertoire object despite matching barcodes.

[monikaramos]

Hi there,
I am trying to overlay the BCR analysis results onto my single-cell object and am following this vignette: https://www.borch.dev/uploads/screpertoire/articles/attaching_sc . Unfortunately, I am encountering the following error when trying to combine my single cell experiment object and screpertoire combined.bcr object:

sce <- combineExpression(combined.BCR,
sce,
cloneCall="gene",
group.by = NULL,
proportion = TRUE)

ERROR message:
Error in [.data.frame(full_data, , combined_col_names) :
undefined columns selected

I have ensured that my barcodes match their naming convention in the sce and combined.bcr object as evidenced here:
SCE object:

combined.BCR object:

Can you please help me identify the cause of this error?

Thank you.

[ncborcherding]

@monikaramos

Could you do me a favor and please make a reproducible example for this issue so I can ID the issue?

Also with the example, please make sure to include the output of sessionInfo().

Thanks a lot,
Nick

[monikaramos]

May I please have your email to send a link to the .rds files (they're small) I am combining with the code I posted above?

Here is my sessionInfo() output:

sessionInfo()
R version 4.4.0 (2024-04-24)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.2.1

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/Los_Angeles
tzcode source: internal

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

other attached packages:
[1] RColorBrewer_1.1-3 ggthemes_5.1.0 scRepertoire_2.0.7 SeuratWrappers_0.3.5 lubridate_1.9.4
[6] forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 readr_2.1.5
[11] tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0 patchwork_1.3.0
[16] Seurat_5.2.1 SeuratObject_5.0.2 sp_2.2-0

loaded via a namespace (and not attached):
[1] cubature_2.1.1 RcppAnnoy_0.0.22 splines_4.4.0
[4] later_1.4.1 R.oo_1.27.0 polyclip_1.10-7
[7] fastDummies_1.7.5 lifecycle_1.0.4 globals_0.16.3
[10] lattice_0.22-6 MASS_7.3-64 magrittr_2.0.3
[13] plotly_4.10.4 rmarkdown_2.29 yaml_2.3.10
[16] remotes_2.5.0 httpuv_1.6.15 sctransform_0.4.1
[19] spam_2.11-1 spatstat.sparse_3.1-0 reticulate_1.40.0
[22] cowplot_1.1.3 pbapply_1.7-2 pkgload_1.4.0
[25] abind_1.4-8 zlibbioc_1.50.0 Rtsne_0.17
[28] GenomicRanges_1.56.2 R.utils_2.12.3 ggraph_2.2.1
[31] BiocGenerics_0.50.0 hash_2.2.6.3 tweenr_2.0.3
[34] evmix_2.12 GenomeInfoDbData_1.2.12 IRanges_2.38.1
[37] S4Vectors_0.42.1 ggrepel_0.9.6 irlba_2.3.5.1
[40] listenv_0.9.1 spatstat.utils_3.1-2 iNEXT_3.0.1
[43] MatrixModels_0.5-3 goftest_1.2-3 RSpectra_0.16-2
[46] spatstat.random_3.3-2 fitdistrplus_1.2-2 parallelly_1.42.0
[49] leiden_0.4.3.1 codetools_0.2-20 DelayedArray_0.30.1
[52] ggforce_0.4.2 tidyselect_1.2.1 UCSC.utils_1.0.0
[55] farver_2.1.2 viridis_0.6.5 matrixStats_1.5.0
[58] stats4_4.4.0 spatstat.explore_3.3-4 jsonlite_1.8.9
[61] tidygraph_1.3.1 progressr_0.15.1 ggalluvial_0.12.5
[64] ggridges_0.5.6 survival_3.8-3 tools_4.4.0
[67] stringdist_0.9.15 ica_1.0-3 Rcpp_1.0.14
[70] glue_1.8.0 gridExtra_2.3 SparseArray_1.4.8
[73] xfun_0.50 MatrixGenerics_1.16.0 GenomeInfoDb_1.40.1
[76] withr_3.0.2 BiocManager_1.30.25 fastmap_1.2.0
[79] fansi_1.0.6 SparseM_1.84-2 digest_0.6.37
[82] rsvd_1.0.5 timechange_0.3.0 R6_2.5.1
[85] mime_0.12 colorspace_2.1-1 scattermore_1.2
[88] tensor_1.5 spatstat.data_3.1-4 R.methodsS3_1.8.2
[91] utf8_1.2.4 generics_0.1.3 data.table_1.16.4
[94] graphlayouts_1.2.2 httr_1.4.7 htmlwidgets_1.6.4
[97] S4Arrays_1.4.1 uwot_0.2.2 pkgconfig_2.0.3
[100] gtable_0.3.6 lmtest_0.9-40 SingleCellExperiment_1.26.0
[103] XVector_0.44.0 htmltools_0.5.8.1 dotCall64_1.2
[106] scales_1.3.0 Biobase_2.64.0 png_0.1-8
[109] spatstat.univar_3.1-1 ggdendro_0.2.0 knitr_1.49
[112] rstudioapi_0.17.1 rjson_0.2.23 tzdb_0.4.0
[115] reshape2_1.4.4 nlme_3.1-167 cachem_1.1.0
[118] zoo_1.8-12 KernSmooth_2.23-26 parallel_4.4.0
[121] miniUI_0.1.1.1 vipor_0.4.7 ggrastr_1.0.2
[124] pillar_1.10.1 grid_4.4.0 vctrs_0.6.5
[127] RANN_2.6.2 VGAM_1.1-12 promises_1.3.2
[130] xtable_1.8-4 cluster_2.1.8 beeswarm_0.4.0
[133] evaluate_1.0.3 truncdist_1.0-2 cli_3.6.3
[136] compiler_4.4.0 rlang_1.1.5 crayon_1.5.3
[139] future.apply_1.11.3 labeling_0.4.3 plyr_1.8.9
[142] ggbeeswarm_0.7.2 stringi_1.8.4 viridisLite_0.4.2
[145] deldir_2.0-4 assertthat_0.2.1 gsl_2.1-8
[148] munsell_0.5.1 lazyeval_0.2.2 spatstat.geom_3.3-5
[151] quantreg_6.00 Matrix_1.7-2 RcppHNSW_0.6.0
[154] hms_1.1.3 future_1.34.0 shiny_1.10.0
[157] SummarizedExperiment_1.34.0 evd_2.3-7.1 ROCR_1.0-11
[160] memoise_2.0.1 igraph_2.1.4

[ncborcherding]

Hey @monikaramos

Thanks for the follow up - my email is ncborch @ gmail . com. I am out today with a sick kid, so I will follow up later this week. Apologies for the delay.

Nick

[ncborcherding]

Hey @monikaramos

Thank you so much for the follow up and sending the data along - it save me a tone of time.

What is going on here is that your sce file already has the columns associated with scRepertoire in the colData() - which causes the error because there are no new columns to add. These columns are associated with all NA values for some reason too.

sce <- readRDS("~/Downloads/screpertoire-selected/sce.fortroubleshoot.rds")
head(colData(sce)[,13:18], 5)

DataFrame with 5 rows and 6 columns
CTgene CTnt

Primary_Tumor_LVMMR_AAACCTGCAAGAAAGG-1 NA NA
Primary_Tumor_LVMMR_AAACCTGCAGCTGTGC-1 NA NA
Primary_Tumor_LVMMR_AAACCTGGTCGGCACT-1 NA NA
Primary_Tumor_LVMMR_AAACCTGTCAAACCAC-1 NA NA
Primary_Tumor_LVMMR_AAACCTGTCTGAGTGT-1 NA NA
CTaa CTstrict

Primary_Tumor_LVMMR_AAACCTGCAAGAAAGG-1 NA NA
Primary_Tumor_LVMMR_AAACCTGCAGCTGTGC-1 NA NA
Primary_Tumor_LVMMR_AAACCTGGTCGGCACT-1 NA NA
Primary_Tumor_LVMMR_AAACCTGTCAAACCAC-1 NA NA
Primary_Tumor_LVMMR_AAACCTGTCTGAGTGT-1 NA NA
clonalProportion clonalFrequency

Primary_Tumor_LVMMR_AAACCTGCAAGAAAGG-1 NA NA
Primary_Tumor_LVMMR_AAACCTGCAGCTGTGC-1 NA NA
Primary_Tumor_LVMMR_AAACCTGGTCGGCACT-1 NA NA
Primary_Tumor_LVMMR_AAACCTGTCAAACCAC-1 NA NA
Primary_Tumor_LVMMR_AAACCTGTCTGAGTGT-1 NA NA

If you just subtract the scRepertoire-associated columns, you should be able to use combineExpression() with no isssue:

colData(sce) <- colData(sce)[,1:12]
sce <- combineExpression(combined.BCR,
                         sce,
                         cloneCall="gene",
                         group.by = NULL,
                         proportion = TRUE)

Hope that helps and thanks again!

Nick