moscot.problems.space.AlignmentProblem.compute_feature_correlation

AlignmentProblem.compute_feature_correlation(obs_key, corr_method='pearson', significance_method='fisher', annotation=None, layer=None, features=None, confidence_level=0.95, n_perms=1000, seed=None, **kwargs)

Compute correlation of push-forward or pull-back distribution with features.

Correlates a feature, e.g., counts of a gene, with probabilities of cells mapped to a set of cells such as the push-forward or pull-back distributions.

See also

• TODO: create and link an example

Parameters:

• obs_key (str) – Key in obs containing the push-forward or pull-back distribution.

• corr_method (Literal['pearson', 'spearman']) – Which type of correlation to compute, either 'pearson' or 'spearman'.

• significance_method (Literal['fisher', 'perm_test']) –

  Mode to use when calculating p-values and confidence intervals. Valid options are:

  • 'fisher' - Fisher transformation [Fisher, 1921].

  • 'perm_test' - permutation test.

• annotation (Optional[dict[str, Iterable[str]]]) –

  How to subset the data when computing the correlation:

  • None - do not subset the data.

  • str - key in obs where categorical data is stored.

  • dict - a dictionary with one key corresponding to a categorical column in obs and values to a subset of categories.

• layer (Optional[str]) – Key in layers from which to get the expression. If None, use X.

• features (Union[list[str], Literal['human', 'mouse', 'drosophila'], None]) –

  Features in AnnData to correlate with obs['{obs_key}']:

  • None - all features from var will be taken into account.

  • list - subset of var_names or obs_names.

  • 'human', 'mouse', or 'drosophila' - the features are subsetted to the transcription factors from transcription_factors().

• confidence_level (float) – Confidence level for the confidence interval calculation. Must be in interval \([0, 1]\).

• n_perms (int) – Number of permutations to use when method = 'perm_test'.

• seed (Optional[int]) – Random seed when method = 'perm_test'.

• **kwargs (Any) – The description is missing.

Return type:

DataFrame

Returns:

: Dataframe of shape (n_features, 5) containing the following columns, one for each feature:

• 'corr' - correlation between the count data and push/pull distributions.

• 'pval' - calculated p-values for double-sided test.

• 'qval' - corrected p-values using the Benjamini-Hochberg method at \(0.05\) level.

• 'ci_low' - lower bound of the confidence_level correlation confidence interval.

• 'ci_high' - upper bound of the confidence_level correlation confidence interval.