differentialAbundance: Differential abundance analysis

In shashidhar22/LymphoSeq2: Analyze high-throughput sequencing of T and B cell receptors

Differential abundance analysis

Description

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. 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 processing FALSE (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
)

shashidhar22/LymphoSeq2 documentation built on Jan. 16, 2024, 4:29 a.m.