The djvdj package provides a range of tools to analyze and manipulate single cell V(D)J sequencing data. These tools are straightforward and easily integrate into a standard Seurat workflow.
You can install the development version of djvdj from github with:
# install.packages("devtools")
devtools::install_github("rnabioco/djvdj")With djvdj you can import V(D)J sequencing results from Cell
Ranger
and add these data to your current
Seurat object using import_vdj().
Additional functions are provided to filter (filter_vdj()) and
manipulate (mutate_vdj()) the Seurat object based on a range of V(D)J
metrics including chains, clonotypes, and CDR3 sequences.
# Import VDJ data
paths <- c(
KI_DN3_GE = "data/tcr/KI_DN3_TCR",
KI_DN4_GE = "data/tcr/KI_DN4_TCR",
WT_DN3_GE = "data/tcr/WT_DN3_TCR",
WT_DN4_GE = "data/tcr/WT_DN4_TCR"
)
so_tcr <- import_vdj(
sobj_in = so_tcr, # Seurat object
vdj_dir = paths # Cellranger output directories
)
# Filter for paired chains
so_filt <- filter_vdj(
sobj_in = so_tcr, # Seurat object
filt = all(c("TRA", "TRB") %in% chains) # Condition for filtering
)djvdj allows you to calculate a range of population diversity and
similarity metrics implemented with the
abdiv package. The function
calc_diversity() can be used to measure diversity on a per-cluster or
per-sample basis to allow for comparison across conditions.
calc_similarity() will measure repertoire overlap between clusters or
samples to allow for direct comparisons between cells of interest.
Additional functions are also available to calculate clonotype
abundances (calc_abundance()) and relative gene usage
(calc_usage()).
so_tcr <- calc_diversity(
sobj_in = so_tcr, # Seurat object
cluster_col = "orig.ident", # meta.data column containing cell labels
method = abdiv::simpson # Diversity metric to use
)For each ‘calc’ function, djvdj also provides a corresponding ‘plot’ function to summarize the results.
# Compare the usage of different V and J genes
ggs <- plot_usage(
sobj_in = so_tcr, # Seurat object
gene_cols = c("v_gene", "j_gene"), # meta.data column(s) containing genes
cluster_col = "orig.ident", # meta.data column containing cell labels
chain = "TRB" # Chain to use for filtering genes
) %>%
imap(~ .x + ggtitle(.y))
plot_grid(plotlist = ggs)
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