analyzeAssociations: Association analysis

In Genentech/midasHLA: R package for immunogenomics data handling and association analysis

Association analysis

Description

analyzeAssociations perform association analysis on a single variable level using a statistical model of choice.

Usage

analyzeAssociations(
  object,
  variables,
  placeholder = "term",
  correction = "bonferroni",
  n_correction = NULL,
  exponentiate = FALSE
)

Arguments

object	An existing fit from a model function such as lm, glm and many others.
variables	Character vector specifying variables to use in association tests.
placeholder	String specifying term in object's formula which should be substituted with variables during analysis.
correction	String specifying multiple testing correction method. See details for further information.
n_correction	Integer specifying number of comparisons to consider during multiple testing correction calculations. For Bonferroni correction it is possible to specify a number lower than the number of comparisons being made. This is useful in cases when knowledge about the biology or redundance of alleles reduces the need for correction. For other methods it must be at least equal to the number of comparisons being made; only set this (to non-default) when you know what you are doing!
exponentiate	Logical flag indicating whether or not to exponentiate the coefficient estimates. Internally this is passed to tidy. This is typical for logistic and multinomial regressions, but a bad idea if there is no log or logit link. Defaults to FALSE.

Details

correction specifies p-value adjustment method to use, common choice is Benjamini & Hochberg (1995) ("BH"). Internally this is passed to p.adjust.

Value

Tibble containing combined results for all variables. The first column "term" hold the names of variables. Further columns depends on the used model and are determined by associated tidy function. Generally they will include "estimate", "std.error", "statistic", "p.value", "conf.low", "conf.high", "p.adjusted".

Examples

midas <- prepareMiDAS(hla_calls = MiDAS_tut_HLA,
                      colData = MiDAS_tut_pheno,
                      experiment = "hla_alleles")

# analyzeAssociations expects model data to be a data.frame
midas_data <- as.data.frame(midas)

# define base model
object <- lm(disease ~ term, data = midas_data)

# test for alleles associations
analyzeAssociations(object = object,
                    variables = c("B*14:02", "DRB1*11:01"))

Genentech/midasHLA documentation built on Feb. 12, 2024, 9:38 a.m.