zoom.pls_tool.vip_perm

zoom.pls_tool.vip_perm(expression: pandas.DataFrame, SBP: pandas.DataFrame, SBP_perm: pandas.DataFrame, best_comp: int, one_sided: bool, n_jobs: int) → Tuple[pandas.DataFrame, pandas.DataFrame, pandas.DataFrame][source]

Compute gene-level statistics and corresponding permutation p-values.

Parameters:

  • expression (pd.DataFrame) – AHBA gene expression matrix.

  • SBP (pd.DataFrame) – One-dimensional data frame of spatial brain phenotype.

  • SBP_perm (pd.DataFrame) – Spatial autocorrelation-preserved null model for SBP.

  • best_comp (int) – The optimal component number for current PLS-R model.

  • one_sided (bool) – If True, infer statistical significance via one-sided p-values. Else, use two-sided p-values.

  • n_jobs (int) – Number of cores used for parallel computation.

Returns:

  • gene_rep (pd.DataFrame) – Gene-level PLS statistics and permutation p-values.

  • RC_perm (pd.DataFrame) – Gene-level regression coefficients (RCs) in permutation tests.

  • VIP_perm (pd.DataFrame) – Gene-level Variable importance in projection (VIP) in permutation tests.

References

[1] Wang, Y. et al. Spatio-molecular profiles shape the human

cerebellar hierarchy along the sensorimotor-association axis. Cell Rep. 43, 113770 (2024).

[2] Mahieu, B., Qannari, E. M. & Jaillais, B. Extension and

significance testing of Variable Importance in Projection (VIP) indices in Partial Least Squares regression and Principal Components Analysis. Chemom. Intell. Lab. Syst. 242, 104986 (2023).