Adjust P-values in a dana object

Applies multiple testing correction to P-values from differential analysis results returned by the dana() function. Supports multiple adjustment methods and both coefficient and likelihood ratio test (LRT) P-values.

Usage

adjust_pval(
  dana_obj,
  padj_by = c("all", "terms"),
  padj_method = NULL,
  padj_method_LRT = NULL,
  ihw_covar = NULL,
  ihw_covar_id = NULL,
  ihw_args = list(),
  storey_args = list(),
  verbose = TRUE
)

Arguments

dana_obj

A dana class object returned by the dana() function.

padj_by

Character string. Whether P-value adjustment should be done globally across all coefficients ("all") or separately for each coefficient term ("terms").

padj_method

Character vector of one or more methods for adjusting P-values from coefficient tests. Defaults to "BH".

padj_method_LRT

Character vector of one or more methods for adjusting P-values from LRT tests. Defaults to "BH". P-values from LRT tests will always be adjusted independently for each LRT term.

ihw_covar

Data frame containing covariable(s) used for IHW::ihw(). Must contain a "feat_id" column, matching dana object "feat_id" labels.

ihw_covar_id

Character string. Column name in ihw_covar used as covariates argument in IHW::ihw().

ihw_args

Named list. Additional arguments passed to IHW::ihw(). Do not provide covariates argument, as it will be obtained from ihw_covar.

storey_args

Named list. Additional arguments passed to qvalue::qvalue().

verbose

Logical. Whether to print informative messages. Defaults to TRUE.

Value

A modified dana object with new columns in the $fit and $lrt data frames for each adjusted P-value method applied (e.g. padj_BH, padj_storey_group).

Details

Available adjustment methods include: "BH", "bonferroni", "BY", "fdr", "hochberg", "holm", "hommel", "IHW", and "storey".

See also

• dana() for fitting differential analysis models on omics datasets.

• IHW::ihw() for inverted hypothesis weighting method details.

• qvalue::qvalue() for Storey's qvalue method details.

Examples

set.seed(123)
mock_X <- matrix(rnorm(20 * 5), nrow = 20)
colnames(mock_X) <- paste0("feat_", seq_len(5))
rownames(mock_X) <- paste0("sample_", seq_len(20))

sample_data <- data.frame(
  sample_id = rownames(mock_X),
  group = factor(rep(c("A", "B"), each = 10)),
  time = factor(rep(c("T1", "T2"), times = 10)),
  subject_id = factor(rep(seq_len(10), each = 2)),
  stringsAsFactors = FALSE
)
rownames(sample_data) <- sample_data$sample_id

fit_df <- data.frame(
  feat_id = rep(colnames(mock_X), each = 2),
  Coefficient = rep(c("(Intercept)", "groupB"), 5),
  Estimate = rnorm(10),
  `Pr(>|t|)` = runif(10),
  stringsAsFactors = FALSE
)

# Mock dana object
dana_obj <- list(
  X = mock_X,
  sdata = sample_data,
  formula_rhs = ~ group,
  fit = fit_df,
  lrt = data.frame(),
  ranef = data.frame()
)
class(dana_obj) <- "dana"

# Add adjusted P-values
dana_obj <- dana_obj |>
  adjust_pval(padj_method = c("BH", "bonferroni"),
              padj_method_LRT = NULL,
              padj_by = "terms",
              verbose = FALSE)