twinfer.inference.covariance_decomposition_z_scores.classify_inference_scenario

twinfer.inference.covariance_decomposition_z_scores.classify_inference_scenario(z_rho, z_rho_s, z_rho_Delta, thresholds=(1.96, 2.326, 1.96), correction='none', fdr=0.05)[source]

Inference scenario (1-4) of each pair from its three z-scores (arrays or scalars); NaN in a needed z-score gives <NA>.

Parameters:

  • z_rho (float or array) – The three z-scores of each pair.

  • z_rho_s (float or array) – The three z-scores of each pair.

  • z_rho_Delta (float or array) – The three z-scores of each pair.

  • thresholds (tuple of float) –

    (z_rho, z_rho_s, z_rho_Delta): |z| above the value counts as significant:

    z_rho not significant                                   -> 1 nothing
z_rho significant, z_rho_s not significant              -> 2 regulation
z_rho, z_rho_s and z_rho_Delta significant              -> 3 regulation + heterogeneity
z_rho and z_rho_s significant, z_rho_Delta not          -> 4 heterogeneity

  • correction ({"none", "bh"}) –

    • “none”: every threshold as above (one pre-specified pair).

    • ”bh”: z_rho is significant when its Benjamini-Hochberg q-value over ALL pairs given is <= fdr; z_rho_Delta is significant when its BH q-value over the pairs whose z_rho is significant is <= fdr (thresholds[0] and thresholds[2] are not used); z_rho_s keeps its threshold. Use for many pairs at once (real data).

  • fdr (float) – q-value cut-off of the “bh” correction.