zoom.sc_tool.score_cell_zoom

zoom.sc_tool.score_cell_zoom(adata: anndata.AnnData, gene_rep: pandas.DataFrame, weight_key: str, sign_key: str, p_perm_key: str, weight_perm: pandas.DataFrame, sign_perm: pandas.DataFrame, direction: bool, gene_size: int, ctrl_match_key: str, weight_opt: str, n_genebin: int, return_ctrl_raw_score: bool, return_ctrl_norm_score: bool, n_jobs: int) → pandas.DataFrame

Calculates single-cell SBP-relevant enrichment scores.

Parameters:

• adata (ad.AnnData) – The AnnData object containing expression data. - adata.X should have underwent gene specificity score normalization. - adata.uns[‘GENE_STATS’] should be present.

• gene_rep (pd.DataFrame) – Results for PLS-R, must contain following columns: - weight_key: Column name indicating the original gene weights for scoring. - sign_key: Column name indicating the direction of genes. - p_perm_key: Column name indicating the permutation p-values.

• weight_perm (pd.DataFrame) – Gene weights in permutation tests.

• sign_perm (pd.DataFrame) – Signed gene weights in permutation tests.

• direction (bool) – If True, find gene set relevant to the positive direction of given SBP else negative direction.

• gene_size (int) – Number of genes used for scoring.

• ctrl_match_key (str) – Gene-level statistic used for matching control and SBP-relevant genes, must be present in adata.uns[‘GENE_STATS’].

• weight_opt (str) – Gene-level statistic used for re-weighting SBP-relevant genes, must be present in adata.uns[‘GENE_STATS’].

• n_genebin (int) – Number of bins for dividing genes by ctrl_match_key.

• return_ctrl_raw_score (bool) – If True, return raw scores for control gene sets.

• return_ctrl_norm_score (bool) – If True, return normalized scores for control gene sets.

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

Returns:

df_res – Results of single-cell SBP-relevant enrichment scores and other statistics. - raw_socre: Raw enrichment scores. - norm_score: Normalized enrichment scores. - p_perm: Single-cell level p-values estimated on spatial permutation tests of the same cell. - pval: Pooled p-value estimated on all permutation tests, all cells

Return type:

pd.DataFrame

References

[1]

Zhang, M. J. et al. Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data. Nat. Genet. 54, 1572-1580 (2022).

[2]

Fulcher, B. D., Arnatkeviciute, A. & Fornito, A. Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data. Nat. Commun. 12, 2669 (2021).