DGU: Differential immunoglobulin (Ig) gene usage in immune...

In snaketron/IgGeneUsage: Differential gene usage in immune repertoires

Differential immunoglobulin (Ig) gene usage in immune repertoires

Description

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

Usage

DGU(ud, 
    mcmc_warmup, 
    mcmc_steps,
    mcmc_chains, 
    mcmc_cores, 
    hdi_lvl,
    adapt_delta, 
    max_treedepth,
    paired = FALSE)

Arguments

ud	Data.frame with 4 or 5 columns: 'individual_id' = character, name of the donor (e.g. Pt1) 'condition' = character, name of biological conditions (e.g. tumor) 'gene_name' = character, Ig gene name (e.g. IGHV1-69) 'gene_usage_count' = number, frequency (=usage) of rearrangements from individual_id x condition x gene_name [optional] 'replicate' = character or number. Replicate id, if more than one repertoire (biological replicates) is available per individual ud can also be 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_lvl	Highest density interval (HDI) (default = 0.95).
adapt_delta	MCMC setting (default = 0.95).
max_treedepth	MCMC setting (default = 12).
paired	should a paired samples differential Ig gene analaysis be performed (default = FALSE)?

Details

The main input of IgGeneUsage is a table with Ig gene usage frequencies for a set of repertoires that belong to one of two biological condition. For the DGU analysis between two biological conditions, IgGeneUsage employs a Bayesian hierarchical model for zero-inflated beta-binomial (ZIBB) regression (see vignette 'User Manual: IgGeneUsage').

Value

dgu	DGU statistics for each gene: 1) es = effect size on DGU (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 DGU. This summary is only available if the input data contains at least two conditions
gu	gene usage (GU) summary of each gene in each condition
fit	stanfit object
ppc	two types of posterior predictive checks: 1) repertoire- specific, 2) condition-specific
ud	processed gene usage data used for the model

Author(s)

Simo Kitanovski <[e-mail]>

See Also

LOO, Ig, IGHV_Epitopes, IGHV_HCV, Ig_SE, d_zibb_1, d_zibb_2, d_zibb_3

Examples

# input data
data(d_zibb_2)
head(d_zibb_2)

# run differential gene usage (DGU)
M <- DGU(ud = d_zibb_2,
         mcmc_warmup = 350,
         mcmc_steps = 1500,
         mcmc_chains = 2,
         mcmc_cores = 1,
         hdi_lvl = 0.95,
         adapt_delta = 0.8,
         max_treedepth = 10,
         paired = FALSE)

# look at M elements
names(M)

# look at DGU results
head(M$dgu)

# look at posterior predictive checks (PPC)
head(M$ppc)

snaketron/IgGeneUsage documentation built on April 23, 2024, 2:22 a.m.