Tools

Filtering

tl.filter_adata_by_gates(adata, gates[, ...])	Filter an AnnData object to retain only markers specified in the gates DataFrame.
tl.filter_by_abs_value(adata, feature_name)	Filter cells in an AnnData object by a range of values for a specified feature.
tl.filter_by_annotation(adata, path_to_geojson)	Filter cells by annotation in a geojson file using spatial indexing.
tl.filter_by_ratio(adata, end_cycle, start_cycle)	Filter cells by the ratio of two markers in an AnnData object.
tl.filter_features_byNaNs(adata[, ...])	Filter out proteins that have a NaN proportion above the threshold, for each group in the grouping variable.

Imputation

tl.impute_gaussian(adata[, mean_shift, ...])

Impute missing values in an AnnData object using a Gaussian distribution.

Spatial

tl.spatial_autocorrelation(adata[, method, ...])	Compute spatial autocorrelation statistics (Moran's I or Geary's C) for each gene in an AnnData object.
tl.spatial_hyperparameter_search(adata[, ...])	Perform a hyperparameter search over a range of threshold values.

Statistics

tl.stats_anova(adata, grouping[, ...])	Perform one-way ANOVA for all columns of an AnnData object across all groups in a categorical column.
tl.stats_average_samples(adata, categories)	Average samples based on specified categories in adata.obs.
tl.stats_bootstrap(dataframe[, n_bootstrap, ...])	Evaluate the variability of feature-level coefficient of variation (CV) via bootstrapping.
tl.stats_ttest(adata, grouping, group1, group2)	Perform a t-test for all columns of an AnnData object between two groups.

Utils

tl.phenotype_cells(adata, phenotype[, gate, ...])