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Examples of files you may want to load:
filtered_contig_annotations.csv
Sample_TCRB.tsv
MiXCR
or other
toolslibrary(TCRconvertR)
tcr_file <- get_example_path("tenx.csv") # Using built-in example file
tcrs <- read.csv(tcr_file)[c("barcode", "v_gene", "j_gene", "cdr3")]
tcrs
#> barcode v_gene j_gene cdr3
#> 1 AAACCTGAGACCACGA-1 TRAV29/DV5 TRAJ12 CAVMDSSYKLIF
#> 2 AAACCTGAGACCACGA-1 TRBV20/OR9-2 TRBJ2-1 CASSGLAGGYNEQFF
#> 3 AAACCTGAGGCTCTTA-1 TRDV2 TRDJ3 CASSGVAGGTDTQYF
#> 4 AAACCTGAGGCTCTTA-1 TRGV9 TRGJ1 CAVKDSNYQLIW
new_tcrs <- convert_gene(tcrs, frm = "tenx", to = "adaptive")
#> Warning in convert_gene(tcrs, frm = "tenx", to = "adaptive"): Adaptive only
#> captures VDJ genes; C genes will be NA.
#> Converting from 10X. Using *01 as allele for all genes.
new_tcrs
#> barcode v_gene j_gene cdr3
#> 1 AAACCTGAGACCACGA-1 TCRAV29-01*01 TCRAJ12-01*01 CAVMDSSYKLIF
#> 2 AAACCTGAGACCACGA-1 TCRBV20-or09_02*01 TCRBJ02-01*01 CASSGLAGGYNEQFF
#> 3 AAACCTGAGGCTCTTA-1 TCRDV02-01*01 TCRDJ03-01*01 CASSGVAGGTDTQYF
#> 4 AAACCTGAGGCTCTTA-1 TCRGV09-01*01 TCRGJ01-01*01 CAVKDSNYQLIW
Tip: Suppress messages by setting
verbose = FALSE
. Warnings and errors will still appear.
Tip: If your Adaptive data lacks
x_resolved
/xMaxResolved
columns, create them yourself by combining thex_gene
/xGeneName
andx_allele
/xGeneAllele
columns. See the FAQs.
Supply the standard AIRR gene column names to
frm_cols
:
By default, TCRconvertR
assumes these column names based
on the input nomenclature (frm
):
frm = 'imgt'
:
c('v_gene', 'd_gene', 'j_gene', 'c_gene')
frm = 'tenx'
:
c('v_gene', 'd_gene', 'j_gene', 'c_gene')
frm = 'adaptive'
:
c('v_resolved', 'd_resolved', 'j_resolved')
frm = 'adaptivev2'
:
c('vMaxResolved', 'dMaxResolved', 'jMaxResolved')
You can override these columns using frm_cols
:
1. Load 10X data with custom column names
custom_file <- get_example_path("customcols.csv")
custom <- read.csv(custom_file)
custom
#> myVgene myDgene myJgene myCgene myCDR3 antigen
#> 1 TRAV1-2 TRBD1 TRAJ12 TRAC CAVMDSSYKLIF Flu
#> 2 TRBV6-1 TRBD2 TRBJ2-1 TRBC2 CASSGLAGGYNEQFF Flu
#> 3 TRBV6-4 TRBD2 TRBJ2-3 TRBC2 CASSGVAGGTDTQYF CMV
#> 4 TRAV1-2 TRBD1 TRAJ33 TRAC CAVKDSNYQLIW CMV
#> 5 TRBV2 TRBD1 TRBJ1-2 TRBC1 CASNQGLNYGYTF CMV
2. Specify names using frm_cols
and convert to
IMGT
custom_new <- convert_gene(
custom,
frm = "tenx",
to = "imgt",
verbose = FALSE,
frm_cols = c("myVgene", "myDgene", "myJgene", "myCgene"),
)
custom_new
#> myVgene myDgene myJgene myCgene myCDR3 antigen
#> 1 TRAV1-2*01 TRBD1*01 TRAJ12*01 TRAC*01 CAVMDSSYKLIF Flu
#> 2 TRBV6-1*01 TRBD2*01 TRBJ2-1*01 TRBC2*01 CASSGLAGGYNEQFF Flu
#> 3 TRBV6-4*01 TRBD2*01 TRBJ2-3*01 TRBC2*01 CASSGVAGGTDTQYF CMV
#> 4 TRAV1-2*01 TRBD1*01 TRAJ33*01 TRAC*01 CAVKDSNYQLIW CMV
#> 5 TRBV2*01 TRBD1*01 TRBJ1-2*01 TRBC1*01 CASNQGLNYGYTF CMV
Use species = "rhesus"
or
species = "mouse"
new_tcrs <- convert_gene(
tcrs,
frm = "tenx",
to = "imgt",
species = "rhesus", # or 'mouse'
verbose = FALSE
)
#> Warning in convert_gene(tcrs, frm = "tenx", to = "imgt", species = "rhesus", : These genes are not in IMGT for this species and will be replaced with NA:
#> TRAV29/DV5, TRBV20/OR9-2, TRGJ1
new_tcrs
#> barcode v_gene j_gene cdr3
#> 1 AAACCTGAGACCACGA-1 <NA> TRAJ12*01 CAVMDSSYKLIF
#> 2 AAACCTGAGACCACGA-1 <NA> TRBJ2-1*01 CASSGLAGGYNEQFF
#> 3 AAACCTGAGGCTCTTA-1 TRDV2*01 TRDJ3*01 CASSGVAGGTDTQYF
#> 4 AAACCTGAGGCTCTTA-1 TRGV9*01 <NA> CAVKDSNYQLIW
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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