This vignette shows an example of loading data from the CellRanger pipeline and doing some QC to pick barcodes. If gene expression was also collected, it is better to do joint cell calling.
Some types of multiplets / debris can be better assessed with by using the gene expression data. See
vignette('repertoire_and_expression') for details of how to merge repertoire with a
library(CellaRepertorium) library(dplyr) library(ggplot2) library(readr) library(tidyr) library(stringr)
files = list.files(system.file('extdata', package = 'CellaRepertorium'), pattern = "all_contig_annotations_.+?.csv.xz", recursive = TRUE, full.names = TRUE) # Pull out sample and population names samp_map = tibble(anno_file = files, pop = str_match(files, 'b6|balbc')[,1], sample = str_match(files, '_([0-9])\\.')[,2]) knitr::kable(samp_map)
PBMC pooled from BALB/c and C57BL/6 mice were assayed on 10X genomics V3 chemistry and a library enriched for TCR were run. For the purposes of illustrating functionality in this package, cell barcodes were subsampled 3 times for each of the BALB/c and Black6 pools to generate distinct
samples, which is reflected in the
sample column. More details are available in the scripts in the
script directory of this package.
# read in CSV all_anno = samp_map %>% rowwise() %>% mutate(anno = list(read_csv(anno_file, col_types = cols( barcode = col_character(), is_cell = col_logical(), contig_id = col_character(), high_confidence = col_logical(), length = col_double(), chain = col_character(), v_gene = col_character(), d_gene = col_character(), j_gene = col_character(), c_gene = col_character(), full_length = col_logical(), productive = col_character(), cdr3 = col_character(), cdr3_nt = col_character(), reads = col_double(), umis = col_double(), raw_clonotype_id = col_character(), raw_consensus_id = col_character() )))) all_anno = all_anno %>% unnest(cols = c(anno))
(The column types typically don’t need to be specified in such detail, but watch for issues in the
full_length columns which may be read as a character vs logical depending on your specific inputs. Either is fine, but you will want to be consistent across files.)
We read in several files of annotated “contigs” output from 10X genomics VDJ version 3.0.
cell_tbl = unique(all_anno[c("barcode","pop","sample","is_cell")]) cdb = ContigCellDB(all_anno, contig_pk = c('barcode','pop','sample','contig_id'), cell_tbl = cell_tbl, cell_pk = c('barcode','pop','sample'))
Note that initially there are 3818 contigs.
cdb = mutate_cdb(cdb, celltype = guess_celltype(chain)) cdb = filter_cdb(cdb, high_confidence)
After filtering for only high_confidence contigs there are 2731 contigs.
We read in the contig annotation file for each of the samples, and annotate the contig as a alpha-beta T cell, gamma-delta T cell, B cell or chimeric “multi” cell type based on where various
total_umi = crosstab_by_celltype(cdb) T_ab_umi = total_umi[c(cdb$cell_pk,"is_cell","T_ab")] ggplot(T_ab_umi, aes(color = factor(is_cell), x = T_ab, group = interaction(is_cell, sample, pop))) + stat_ecdf() + coord_cartesian(xlim = c(0, 10)) + ylab('Fraction of barcodes') + theme_minimal() + scale_color_discrete('10X called cell?')
10X defines a procedure to separate cells from background that fits a Gaussian mixture model to the UMI distributions for each sample. However in some cases, it may be desirable to implement a common QC threshold with a different stringency, such as:
When we consider only high confidence UMIs that unambiguous map to T cells, most “non cells” have 1 or fewer, while most putative cells have >5. However, we might want to adopt a different UMI-based cell filter, as was done below.
qual_plot = ggplot(cdb$contig_tbl, aes(x = celltype, y= umis)) + geom_violin() + geom_jitter() + facet_wrap(~sample + pop) + scale_y_log10() + xlab("Annotated cell type") qual_plot
qual_plot + aes(y = reads)
The number of UMIs and reads by sample and annotated cell type.
# At least 2 UMI mapping to high confidence T cell contigs. good_bc = total_umi %>% ungroup() %>% filter(is_cell) %>% filter(T_ab >= 2) total_cells = good_bc %>% group_by(sample, pop) %>% summarize(good_bc = n()) #> `summarise()` regrouping output by 'sample' (override with `.groups` argument) knitr::kable(total_cells)
Apply a filter on UMIs.
contigs_qc = semi_join(cdb$contig_tbl, good_bc %>% select(sample, pop, barcode)) %>% filter(full_length, productive == 'True', high_confidence, chain != 'Multi') #> Joining, by = c("pop", "sample", "barcode")
And take only high confidence, full length, productive \(\alpha-\beta\) T cell contigs.
sessionInfo() #> R version 4.0.3 (2020-10-10) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 18.04.5 LTS #> #> Matrix products: default #> BLAS: /home/biocbuild/bbs-3.12-bioc/R/lib/libRblas.so #> LAPACK: /home/biocbuild/bbs-3.12-bioc/R/lib/libRlapack.so #> #> locale: #>  LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C #>  LC_TIME=en_US.UTF-8 LC_COLLATE=C #>  LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 #>  LC_PAPER=en_US.UTF-8 LC_NAME=C #>  LC_ADDRESS=C LC_TELEPHONE=C #>  LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C #> #> attached base packages: #>  stats4 parallel stats graphics grDevices utils datasets #>  methods base #> #> other attached packages: #>  Biostrings_2.58.0 XVector_0.30.0 IRanges_2.24.0 #>  S4Vectors_0.28.0 BiocGenerics_0.36.0 ggdendro_0.1.22 #>  purrr_0.3.4 stringr_1.4.0 tidyr_1.1.2 #>  readr_1.4.0 ggplot2_3.3.2 dplyr_1.0.2 #>  CellaRepertorium_1.0.0 BiocStyle_2.18.0 #> #> loaded via a namespace (and not attached): #>  splines_4.0.3 assertthat_0.2.1 statmod_1.4.35 #>  BiocManager_1.30.10 highr_0.8 broom.mixed_0.2.6 #>  yaml_2.2.1 progress_1.2.2 pillar_1.4.6 #>  backports_1.1.10 lattice_0.20-41 glue_1.4.2 #>  digest_0.6.27 RColorBrewer_1.1-2 minqa_1.2.4 #>  colorspace_1.4-1 cowplot_1.1.0 htmltools_0.5.0 #>  Matrix_1.2-18 plyr_1.8.6 pkgconfig_2.0.3 #>  broom_0.7.2 magick_2.5.0 bookdown_0.21 #>  zlibbioc_1.36.0 scales_1.1.1 lme4_1.1-25 #>  tibble_3.0.4 generics_0.0.2 farver_2.0.3 #>  ellipsis_0.3.1 withr_2.3.0 TMB_1.7.18 #>  cli_2.1.0 magrittr_1.5 crayon_1.3.4 #>  evaluate_0.14 fansi_0.4.1 nlme_3.1-150 #>  MASS_7.3-53 forcats_0.5.0 tools_4.0.3 #>  prettyunits_1.1.1 hms_0.5.3 lifecycle_0.2.0 #>  munsell_0.5.0 compiler_4.0.3 rlang_0.4.8 #>  grid_4.0.3 nloptr_220.127.116.11 labeling_0.4.2 #>  rmarkdown_2.5 boot_1.3-25 gtable_0.3.0 #>  reshape2_1.4.4 R6_2.4.1 knitr_1.30 #>  utf8_1.1.4 stringi_1.5.3 Rcpp_1.0.5 #>  png_0.1-7 vctrs_0.3.4 tidyselect_1.1.0 #>  xfun_0.18 coda_0.19-4