Clonotype calling

[pwmellors]

Hi Nick,

Thanks again for this wonderful package.

I had posted previously about this, but had a slightly different question. As I understand, if in a given cell only one variable chain is sequenced and there is drop out of the other, conservatively NA_B and A_B, for example, will be called as different clonotypes, because one cannot definitively say NA = A.

I noticed that for some of my NA containing clonotypes, that in the clonotypes.csv and filtered_contig_annotations.csv files, there is no discrete clonotype listed for the one chain.

For example, in the figure below, NA_CSARHHPRTGKTNEQYF does not exist in these files as CSARHHPRTGKTNEQYF without the alpha chain, only as CAVTSYNTDKLIF_CSARHHPRTGKTNEQYF, which is clonotype 1.

In contrast CASSIVGADTQYF alone does exist as a stand alone beta chain, and is called as a different clonotype from CAVEPLNARLMF_CASSIVGADTQYF.

Thanks so much

Code

effector_alluvial_ptx <- clonalCompare(ptx_effector,
top.clones = 10,
cloneCall = "aa",
group.by = "condition",
graph = "alluvial") +
ylim(0, 1.0)

effector_alluvial_ptx

R version 4.4.2 (2024-10-31)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.3

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/New_York
tzcode source: internal

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

other attached packages:
[1] readxl_1.4.5 scater_1.34.1 scuttle_1.16.0 SingleCellExperiment_1.28.1 SummarizedExperiment_1.36.0
[6] GenomicRanges_1.58.0 GenomeInfoDb_1.42.3 MatrixGenerics_1.18.1 matrixStats_1.5.0 scRepertoire_2.2.1
[11] rsconnect_1.3.4 ShinyCell_2.1.0 RColorBrewer_1.1-3 glue_1.8.0 R.utils_2.13.0
[16] R.oo_1.27.0 R.methodsS3_1.8.2 reticulate_1.42.0 hdf5r_1.3.12 Matrix_1.7-3
[21] data.table_1.17.0 scCustomize_3.0.1 glmGamPoi_1.18.0 org.Hs.eg.db_3.20.0 AnnotationDbi_1.68.0
[26] IRanges_2.40.1 S4Vectors_0.44.0 clusterProfiler_4.14.6 HGNChelper_0.8.15 openxlsx_4.2.8
[31] multtest_2.62.0 Biobase_2.66.0 BiocGenerics_0.52.0 BiocManager_1.30.25 gridExtra_2.3
[36] lubridate_1.9.4 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.4
[41] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0 ggplot2_3.5.1
[46] Seurat_5.2.1 SeuratObject_5.0.2 sp_2.2-0 miloR_2.2.0 edgeR_4.4.2
[51] limma_3.62.2

loaded via a namespace (and not attached):
[1] vroom_1.6.5 goftest_1.2-3 Biostrings_2.74.1 vctrs_0.6.5 ggtangle_0.0.6 spatstat.random_3.3-3
[7] digest_0.6.37 png_0.1-8 shape_1.4.6.1 ggrepel_0.9.6 deldir_2.0-4 parallelly_1.43.0
[13] MASS_7.3-65 reshape2_1.4.4 httpuv_1.6.15 qvalue_2.38.0 withr_3.0.2 ggrastr_1.0.2
[19] ggfun_0.1.8 survival_3.8-3 memoise_2.0.1 ggbeeswarm_0.7.2 janitor_2.2.1 MatrixModels_0.5-4
[25] gson_0.1.0 tidytree_0.4.6 zoo_1.8-13 GlobalOptions_0.1.2 gtools_3.9.5 pbapply_1.7-2
[31] rematch2_2.1.2 KEGGREST_1.46.0 promises_1.3.2 evmix_2.12 httr_1.4.7 globals_0.16.3
[37] hash_2.2.6.3 fitdistrplus_1.2-2 rstudioapi_0.17.1 UCSC.utils_1.2.0 miniUI_0.1.1.1 generics_0.1.3
[43] DOSE_4.0.1 ggalluvial_0.12.5 zlibbioc_1.52.0 ScaledMatrix_1.14.0 ggraph_2.2.1 polyclip_1.10-7
[49] GenomeInfoDbData_1.2.13 SparseArray_1.6.2 xtable_1.8-4 pracma_2.4.4 S4Arrays_1.6.0 hms_1.1.3
[55] irlba_2.3.5.1 colorspace_2.1-1 ROCR_1.0-11 spatstat.data_3.1-6 magrittr_2.0.3 lmtest_0.9-40
[61] snakecase_0.11.1 later_1.4.2 viridis_0.6.5 ggtree_3.14.0 lattice_0.22-7 spatstat.geom_3.3-6
[67] future.apply_1.11.3 SparseM_1.84-2 scattermore_1.2 cowplot_1.1.3 RcppAnnoy_0.0.22 pillar_1.10.2
[73] nlme_3.1-168 compiler_4.4.2 beachmat_2.22.0 RSpectra_0.16-2 stringi_1.8.7 tensor_1.5
[79] plyr_1.8.9 crayon_1.5.3 abind_1.4-8 ggdendro_0.2.0 gridGraphics_0.5-1 locfit_1.5-9.12
[85] graphlayouts_1.2.2 bit_4.6.0 fastmatch_1.1-6 codetools_0.2-20 BiocSingular_1.22.0 paletteer_1.6.0
[91] plotly_4.10.4 mime_0.13 splines_4.4.2 circlize_0.4.16 Rcpp_1.0.14 fastDummies_1.7.5
[97] quantreg_6.1 cellranger_1.1.0 blob_1.2.4 fs_1.6.5 listenv_0.9.1 evd_2.3-7.1
[103] gsl_2.1-8 ggplotify_0.1.2 statmod_1.5.0 tzdb_0.5.0 tweenr_2.0.3 pkgconfig_2.0.3
[109] tools_4.4.2 cachem_1.1.0 RSQLite_2.3.9 viridisLite_0.4.2 DBI_1.2.3 numDeriv_2016.8-1.1
[115] splitstackshape_1.4.8 fastmap_1.2.0 scales_1.3.0 ica_1.0-3 patchwork_1.3.0 ggprism_1.0.5
[121] dotCall64_1.2 RANN_2.6.2 farver_2.1.2 tidygraph_1.3.1 VGAM_1.1-13 cli_3.6.4
[127] lifecycle_1.0.4 uwot_0.2.3 BiocParallel_1.40.1 timechange_0.3.0 gtable_0.3.6 rjson_0.2.23
[133] ggridges_0.5.6 progressr_0.15.1 cubature_2.1.1 parallel_4.4.2 ape_5.8-1 jsonlite_2.0.0
[139] RcppHNSW_0.6.0 bit64_4.6.0-1 assertthat_0.2.1 Rtsne_0.17 yulab.utils_0.2.0 spatstat.utils_3.1-3
[145] BiocNeighbors_2.0.1 zip_2.3.2 GOSemSim_2.32.0 spatstat.univar_3.1-2 truncdist_1.0-2 lazyeval_0.2.2
[151] shiny_1.10.0 htmltools_0.5.8.1 enrichplot_1.26.6 GO.db_3.20.0 iNEXT_3.0.1 sctransform_0.4.1
[157] spam_2.11-1 XVector_0.46.0 treeio_1.30.0 igraph_2.1.4 R6_2.6.1 labeling_0.4.3
[163] cluster_2.1.8.1 stringdist_0.9.15 aplot_0.2.5 DelayedArray_0.32.0 tidyselect_1.2.1 vipor_0.4.7
[169] ggforce_0.4.2 future_1.34.0 rsvd_1.0.5 munsell_0.5.1 KernSmooth_2.23-26 htmlwidgets_1.6.4
[175] fgsea_1.32.4 rlang_1.1.5 spatstat.sparse_3.1-0 spatstat.explore_3.4-2 beeswarm_0.4.0

[ncborcherding]

@pwmellors

This is true and intended (in terms of treating the missing chain equally) but your issue give me an idea as a basis for an internal inference function for missing chains - use the actual sequencing run as a internal control and search for repeated pairs. This is something that I have always wanted to fix or add to the package!!

I am currently collecting things to do a minor overhaul on scRepertoire here in the next week or two. I will mark this as an enchancement and follow up as I make those changes.

Nick