import_vdj: Import V(D)J data

In rnabioco/AVIDtools: A collection of single-cell V(D)J tools

Import V(D)J data

Description

Import V(D)J data

Usage

import_vdj(
  input = NULL,
  vdj_dir = NULL,
  prefix = "",
  data_cols = NULL,
  filter_chains = TRUE,
  filter_paired = FALSE,
  define_clonotypes = NULL,
  include_mutations = FALSE,
  include_constant = FALSE,
  aggr_dir = NULL,
  quiet = FALSE,
  sep = ";"
)

Arguments

input	Object containing single cell data, if set to NULL a data.frame containing V(D)J results will be returned
vdj_dir	Directory containing the output from cellranger vdj. A vector or named vector can be given to load data from multiple runs. If a named vector is given, the cell barcodes will be prefixed with the provided names. This mimics the behavior of Seurat::Read10X().
prefix	Prefix to add to new columns
data_cols	Additional columns from filtered_contig_annotations.csv to include in object.
filter_chains	Only include chains with at least one productive and full length contig.
filter_paired	Only include clonotypes with paired chains. For TCR data each clonotype must have at least one TRA and TRB chain, for BCR data each clonotype must have at least one IGH chain and at least one IGK or IGL chain.
define_clonotypes	Define clonotype IDs based on V(D)J data. This is useful if the V(D)J datasets being loaded do not have consistent clonotype IDs, i.e., clonotype1 is not the same across samples. Possible values are: 'cdr3aa', define clonotypes based on the CDR3 amino acid sequence 'cdr3nt', define clonotypes based on the CDR3 nucleotide sequence 'cdr3_gene', define clonotypes based on the combination of the CDR3 nucleotide sequence and the V(D)J genes. When defining clonotypes, only productive full length chains will be used. Set to NULL (default) to use the clonotype IDs already present in the input data.
include_mutations	Include information about the number of insertions/deletions/mismatches for each chain. This requires the concat_ref.bam file from cellranger vdj to be present the directory provided to vdj_dir. If include_mutations is TRUE, filter_chains is also automatically set TRUE since indel data is only available for productive chains.
include_constant	If the constant region should be included in the "all" mutation count. If TRUE, the constant region will be included in the "all" mutation count and the length of the V + J + D + C regions will be used to calculate the "all_freq". If FALSE (the default), any mutations in the c region will not be counted in the "all" mutation count and only the length of the V + J + D region will be used to calculate the frequency.
aggr_dir	Path to cellranger aggr output. To include mutation information for each chain, also provide paths to the original cellranger vdj output directories using the vdj_dir argument. To correctly match cell barcodes to those in the object, gene expression data for each sample must be loaded in the same order as the samples were specified in the cellranger aggr config file. In addition, if loading mutation data, sample paths provided to the vdj_dir argument must also be in the same order as the samples were specified in the cellranger aggr config file.
quiet	If TRUE progress updates will not be displayed
sep	Separator to use for storing per cell V(D)J data

Value

Single cell object or data.frame with added V(D)J data

Examples

# Load GEX data
data_dir <- system.file("extdata/splen", package = "djvdj")

gex_dirs <- c(
  BL6 = file.path(data_dir, "BL6_GEX/filtered_feature_bc_matrix"),
  MD4 = file.path(data_dir, "MD4_GEX/filtered_feature_bc_matrix")
)

splen_so <- gex_dirs |>
  Seurat::Read10X() |>
  Seurat::CreateSeuratObject()

# Loading multiple datasets
# to ensure cell barcodes for the V(D)J data match those in the object
# load the datasets in the same order as the gene expression data
vdj_dirs <- c(
  file.path(data_dir, "BL6_BCR"),
  file.path(data_dir, "MD4_BCR")
)

res <- splen_so |>
  import_vdj(vdj_dir = vdj_dirs)

head(slot(res, "meta.data"), 1)

# Specifying cell prefixes using vector names
# cell barcode prefixes can also be specified by passing a named vector
vdj_dirs <- c(
  BL6 = file.path(data_dir, "BL6_BCR"),
  MD4 = file.path(data_dir, "MD4_BCR")
)

res <- splen_so |>
  import_vdj(vdj_dir = vdj_dirs)

head(slot(res, "meta.data"), 1)

# Only include V(D)J data for paired chains
res <- splen_so |>
  import_vdj(
    vdj_dir = vdj_dirs,
    filter_paired = TRUE
  )

head(slot(res, "meta.data"), 1)

# Defining clonotypes
# this is useful if the original clonotype IDs are not consistent across
# datasets, i.e. clonotype1 is not the same for all samples
res <- splen_so |>
  import_vdj(
    vdj_dir = vdj_dirs,
    define_clonotypes = "cdr3_gene"
  )

head(slot(res, "meta.data"), 1)

# Include mutation information for each chain
# this information will be included if the file concat_ref.bam is present
# including mutation information will cause data import to be slower
res <- splen_so |>
  import_vdj(
    vdj_dir = vdj_dirs,
    include_mutations = TRUE
  )

head(slot(res, "meta.data"), 1)

rnabioco/AVIDtools documentation built on Oct. 28, 2023, 10:23 a.m.