DGU {IgGeneUsage}R Documentation

Differential gene usage in immune repertoires

Description

IgGeneUsage detects differential gene usage in immune repertoires that belong to two biological conditions.

Usage

DGU(usage.data, mcmc.warmup, mcmc.steps,
    mcmc.chains, mcmc.cores, hdi.level,
    adapt.delta, max.treedepth)

Arguments

usage.data

Data.frame with 4 columns: 'sample_id' = character identifier of each repertoire, 'condition' = character key representing each of the two biological conditions, 'gene_name' = character name of each gene to be tested for differential usage, 'gene_usage_count' = number of rearrangements belonging to a specific sample_id x condition x gene_name. Alternatively, usage.data can be a SummarizedExperiment object. See examplary data 'data(Ig_SE)' for more information.

mcmc.chains, mcmc.warmup, mcmc.steps, mcmc.cores

Number of MCMC chains (default = 4), number of cores to use (default = 1), length of MCMC chains (default = 1,500), length of adaptive part of MCMC chains (default = 500).

hdi.level

Highest density interval (HDI) (default = 0.95).

adapt.delta

MCMC setting (default = 0.95).

max.treedepth

MCMC setting (default = 12).

Details

The input to IgGeneUsage is a table with usage frequencies for each gene of a repertoire that belongs to a particular biological condition. For the analysis of differential gene usage between two biological conditions, IgGeneUsage employs a Bayesian hierarchical model for zero-inflated beta-binomial (ZIBB) regression (see vignette 'User Manual: IgGeneUsage').

Value

glm.summary

differential gene usage statistics for each gene. 1) es = effect size on differential gene usage (mean, median standard error (se), standard deviation (sd), L (low boundary of HDI), H (high boundary of HDI); 2) contrast = direction of the effect; 3) pmax = probability of differential gene usage

test.summary

differential gene usage statistics computed with the Welch's t-test (columns start with 't'), and Wilcoxon signed-rank test (columns start with 'u'). For both test report P-values, FDR-corrected P-values, Bonferroni-corrected P-values. Additionally, we report t-value and 95% CI (from the t-test) and U-value (from the Wilcoxon signed-rank test).

glm

stanfit object

ppc.data

two types of posterior predictive checks: 1) repertoire- specific, 2) gene-specific

Author(s)

Simo Kitanovski <simo.kitanovski@uni-due.de>

See Also

LOO, Ig, IGHV_Epitopes, IGHV_HCV, Ig_SE

Examples

# input data
data(Ig)
head(Ig)

# Alternative:
# use SummarizedExperiment input data
# data(Ig_SE)


# run differential gene usage (DGU)
M <- DGU(usage.data = Ig,
         mcmc.warmup = 500,
         mcmc.steps = 1500,
         mcmc.chains = 2,
         mcmc.cores = 2,
         hdi.level = 0.95,
         adapt.delta = 0.95,
         max.treedepth = 13)

[Package IgGeneUsage version 1.3.1 Index]