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Run Sensitivity Analysis for Differential Abundance

Source: R/run_sensitivity_analysis.R
run_sensitivity_analysis.Rd

Performs sensitivity analysis by systematically varying statistical methods, scaling strategies, and model formulas (with optional bootstrap sampling) to assess the stability of differentially abundant features.

Usage

run_sensitivity_analysis(
  expomicset,
  base_formula,
  abundance_col = "counts",
  methods = c("limma_voom", "DESeq2", "edgeR_quasi_likelihood"),
  scaling_methods = c("none", "TMM", "quantile"),
  contrasts = NULL,
  covariates_to_remove = NULL,
  pval_col = "adj.P.Val",
  logfc_col = "logFC",
  pval_threshold = 0.05,
  logFC_threshold = log2(1),
  score_thresh = NULL,
  score_quantile = 0.9,
  stability_metric = "stability_score",
  action = "add",
  bootstrap_n = 1
)

Arguments

expomicset

A MultiAssayExperiment containing the assays to analyze.

base_formula

The base model formula used for differential analysis.

abundance_col

Character. Name of the column in the assays representing abundance. Default is "counts".

methods

Character vector of differential expression methods. Options include "limma_voom", "DESeq2", and "edgeR_quasi_likelihood".

scaling_methods

Character vector of normalization methods to try. Options include "none", "TMM", and "quantile".

contrasts

Optional list of contrasts to apply for differential testing.

covariates_to_remove

Optional character vector of covariates to remove from the base formula to generate model variants.

pval_col

Name of the column containing p-values or adjusted p-values used to define significance.

logfc_col

Name of the column containing log fold changes.

pval_threshold

Numeric threshold for significance. Default is 0.05.

logFC_threshold

Numeric threshold for absolute log fold change. Default is log2(1) (i.e., 0).

score_thresh

Optional threshold for the selected stability metric. If not provided, calculated using score_quantile.

score_quantile

Quantile used to define the threshold if score_thresh is not provided. Default is 0.9.

stability_metric

Character. Name of the column in feature_stability to use as the scoring metric. Default is "stability_score".

action

Whether to "add" results to metadata() or "get" them as a list. Default is "add".

bootstrap_n

Integer. Number of bootstrap iterations. If 0, no resampling is performed. Default is 1.

Value

If action = "add", returns a MultiAssayExperiment with results stored in metadata(expomicset)$differential_analysis$sensitivity_analysis. If action = "get", returns a list with three elements:

sensitivity_df

Data frame of all differential results across model/method combinations.

feature_stability

Data frame summarizing feature stability scores.

score_thresh

The threshold used to define stable features.

See also

run_differential_abundance, .run_se_differential_abundance

Examples

if (FALSE) { # \dontrun{
expomicset <- run_sensitivity_analysis(
  expomicset,
  base_formula = ~ sex + age + asthma,
  methods = c("limma_voom"),
  scaling_methods = c("TMM", "none"),
  covariates_to_remove = c("age", "sex"),
  bootstrap_n = 10
)
} # }