STAVAG.TVG_detection

STAVAG.TVG_detection(adata, coords, sps=False, threshold=0.05, num_perm=1)[source]

Detect Temporally Variable Genes (TVGs) using regression on a 1D time coordinate.

Parameters:

  • adata (AnnData) – An AnnData object containing gene expression matrix adata.X and gene names adata.var.index.

  • coords (ndarray) – 1D temporal coordinate of cells with shape (n_cells, 1).

  • sps (bool, optional) – If True and num_perm == 1, compute STAVAG priority scores by comparing observed importances with a single random baseline (original behavior). Defaults to False.

  • threshold (float, optional) –

    • If num_perm == 1: cutoff used by keep_variant_genes to select TVGs based on importance.

    • If num_perm > 1: p-value cutoff; genes with pval <= threshold are kept.

    Defaults to 0.05.

  • num_perm (int, optional) –

    Number of permutations used to build an empirical null distribution of feature importances. - If 1: keep the original single-permutation behavior. - If > 1: must be >= 100; empirical permutation p-values are computed

    for each gene.

Returns:

Dictionary containing important genes over the time axis 'T'.

  • For num_perm == 1: same structure as before (with SPS scores if sps=True), already filtered by the given threshold.

  • For num_perm > 1: the DataFrame under key ‘T’ contains columns: [‘Feature’, ‘Importance’, ‘null_mean’, ‘pval’] and is filtered by p-value (<= threshold).

Return type:

Dict[str, DataFrame]