zoom.pls_tool.pls1_perm

zoom.pls_tool.pls1_perm(expression: pandas.DataFrame, SBP: pandas.DataFrame, SBP_perm: pandas.DataFrame, best_comp: int, n_boot: int, one_sided: bool, seed: int, 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.

  • n_boot (int) – Number of bootstrap iteration.

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

  • seed (int) – Random seed to control the resampling.

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

Returns:

  • gene_rep (pd.DataFrame) – Gene-level Z-scored PLS1 weights and permutation p-values.

  • PLS1_perm (pd.DataFrame) – Gene-level Z-scored PLS1 weights in permutation tests.