Use a Fisher exact test to calculate differential abundance of each sequence in two samples and reports the log2 transformed fold change, P value and adjusted P value.
Usage
differentialAbundance(
study_table,
repertoire_ids = NULL,
abundance = "duplicate_count",
type = "junction_aa",
q = 1,
zero = 1,
parallel = FALSE
)Arguments
- study_table
A tibble consisting of antigen receptor sequences imported by the LymphoSeq2 function
readImmunoSeq().- repertoire_ids
A character vector of two repertoire_ids in study_table to be compared. If
NULL(the default), the first two repertoire_ids from study_table will be used.- abundance
The input value for the Fisher exact test. "duplicate_count" is the default value and is also the recommended value.
- type
A character vector indicating whether "junction_aa" (the default) or "junction" sequences should be used. If "junction_aa" is specified, then run
productiveSeq()first.- q
A numeric value between 0.0 and 1.0 indicating the threshold Holms adjusted P value (also known as the false discovery rate or q value) to subset the results with. Any sequences with a q value greater than this value will not be shown.
- zero
A numeric value to set all zero values to when calculating the log2 transformed fold change between samples 1 and 2. This does not apply to the p and q value calculations.
- parallel
A Boolean value
TRUE: Enable parallel processingFALSE(the default): Disable parallel processing
Value
Returns a data frame with columns corresponding to the frequency of the abundance measure in samples 1 and 2, the P value, Q value (Holms adjusted P value, also known as the false discovery rate), and log2 transformed fold change.
Examples
file_path <- system.file("extdata", "TCRB_sequencing", package = "LymphoSeq2")
study_table <- LymphoSeq2::readImmunoSeq(path = file_path, threads = 1)
study_table <- LymphoSeq2::topSeqs(study_table, top = 100)
amino_table <- LymphoSeq2::productiveSeq(study_table = study_table,
aggregate = "junction_aa")
LymphoSeq2::differentialAbundance(
study_table = amino_table,
repertoire_ids = c("TRB_Unsorted_949", "TRB_Unsorted_1320"),
type = "junction_aa", q = 0.01, zero = 0.001
)
#> # A tibble: 107 × 6
#> junction_aa TRB_Unsorted_949 TRB_Unsorted_1320 p q l2fc
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 CAIKMETPNGEQYF 29 326 1.14e- 6 1.14e- 6 -3.49
#> 2 CAISEGQGVKPQHF 0 167 1.09e- 2 1.09e- 2 -Inf
#> 3 CAISESGVLNEKLFF 13 150 1.20e- 3 1.20e- 3 -3.53
#> 4 CASDGGFRNTIYF 17 387 1.40e- 1 1.40e- 1 -4.51
#> 5 CASKPPGQGGYGYTF 0 173 1.12e- 2 1.12e- 2 -Inf
#> 6 CASNRVPEETQYF 0 127 5.75e- 2 5.75e- 2 -Inf
#> 7 CASNSKADSTDTQYF 21 1325 1.52e- 3 1.52e- 3 -5.98
#> 8 CASRDGQGSGNTIYF 48 358 6.73e-16 6.73e-16 -2.90
#> 9 CASREDRGSSPLHF 0 147 2.45e- 2 2.45e- 2 -Inf
#> 10 CASRLGPGAGDEAFF 12 619 1.26e- 1 1.26e- 1 -5.69
#> # ℹ 97 more rows