subsampleDb

subsampleDb - Subsample repertoire

Description¶

subsampleDb will sample the same number of sequences for each gene, family or allele (specified with mode) in data. Samples or subjects can be subsampled independent by setting group.

Usage¶

subsampleDb(
data,
gene = "v_call",
mode = c("gene", "allele", "family"),
min_n = 1,
max_n = NULL,
group = NULL
)

Arguments¶

data: data.frame containing repertoire data.
gene: name of the column in data with allele calls. Default is v_call.
mode: one of c("gene", "family", "allele") defining the degree of specificity regarding allele calls when subsetting sequences. Determines how data will be split into subsets from which the same number of sequences will be subsampled. See also group.
min_n: minimum number of observations to sample from each group. A group with less observations than the minimum is excluded.
max_n: maximum number of observations to sample for all mode groups. If NULL, it will be set automatically to the size of the smallest group. If max_n is larger than the available number of sequences for any mode group, it will be automatically adjusted and the effective max_n used will be the size of the smallest mode group.
group: columns containing additional grouping variables, e.g. sample_id. These groups will be subsampled independently. If max_n is NULL, a max_n will be automatically set for each group.

Value¶

Subsampled version of the input data.

Details¶

data will be split into gene, allele or family subsets (mode) from which the same number of sequences will be subsampled. If mode=gene, for each gene in the field gene from data, a maximum of max_n sequences will be subsampled. Input sequences that have multiple gene calls (ties), can be subsampled from any of their calls, but these duplicated samplings will be removed, and the final subsampled data will contain unique rows.

Examples¶

set.seed(1)
subsampleDb(AIRRDb)

# A tibble: 140 x 26
   sequence_id                 sequence rev_comp productive v_call d_call j_call
   <chr>                       <chr>    <lgl>    <lgl>      <chr>  <chr>  <chr> 
 1 162366_1230_1356_length=44~ NNNNNNN~ NA       TRUE       IGHV1~ IGHD5~ IGHJ3~
 2 053163_2319_0521_length=44~ NNNNNNN~ NA       TRUE       IGHV1~ IGHD1~ IGHJ5~
 3 175431_1170_2788_length=45~ NNNNNNN~ NA       TRUE       IGHV1~ IGHD2~ IGHJ3~
 4 151778_3269_0501_length=44~ NNNNNNN~ NA       TRUE       IGHV1~ IGHD5~ IGHJ6~
 5 112230_0840_0227_length=45~ NNNNNNN~ NA       TRUE       IGHV1~ IGHD3~ IGHJ6~
 6 176024_3740_2255_length=44~ NNNNNNN~ NA       TRUE       IGHV1~ IGHD2~ IGHJ6~
 7 070090_2629_3436_length=44~ NNNNNNN~ NA       TRUE       IGHV1~ IGHD6~ IGHJ6~
 8 007556_2294_3485_length=44~ NNNNNNN~ NA       TRUE       IGHV1~ IGHD3~ IGHJ4~
 9 240300_1635_0534_length=45~ NNNNNNN~ NA       TRUE       IGHV1~ IGHD2~ IGHJ6~
10 038750_0415_2078_length=44~ NNNNNNN~ NA       TRUE       IGHV1~ IGHD3~ IGHJ4~
# i 130 more rows
# i 19 more variables: sequence_alignment <chr>, germline_alignment <lgl>,
#   junction <chr>, junction_aa <lgl>, v_cigar <lgl>, d_cigar <lgl>,
#   j_cigar <lgl>, vj_in_frame <lgl>, stop_codon <lgl>, v_germline_end <lgl>,
#   np1_length <dbl>, np2_length <dbl>, j_germline_end <lgl>,
#   junction_length <dbl>, mutated_invariant <lgl>, indels <lgl>,
#   d_5_trim <dbl>, d_3_trim <dbl>, j_5_trim <dbl>