Integrate effects across omic layers and quantify heterogeneity

Uses generalized least squares (GLS) to estimate a common cross-omic effect. A generalized Cochran Q statistic tests whether the layer-specific effects are compatible with a common effect after accounting for their sampling covariance. A separate multivariate Wald-type omnibus statistic tests the joint null that all layer effects are zero; unlike the consensus effect, this test does not cancel equally strong effects occurring in opposite directions. The reported I2-like statistic is descriptive and should not be interpreted as literal between-study heterogeneity because omics layers are not studies.

Usage

integrate_effects(
  effects,
  covariance = NULL,
  min_omics = 2L,
  p_adjust = "BH",
  conf_level = 0.95
)

Arguments

effects

Data frame containing `entity`, `omic`, `effect`, and `se`.

covariance

Optional output of `bootstrap_effect_covariance()` or a named list of covariance matrices.

min_omics

Minimum omics per entity.

p_adjust

Multiple-testing method for integrated and heterogeneity p-values.

conf_level

Confidence level for the analytic GLS consensus interval.

Value

Data frame of integrated effects and heterogeneity diagnostics.