API

Datasets

datasets.Weinreb_in_vitro	Dataset from Weinreb et al. [PMID: 31974159] with in vitro hematopoiesis.
datasets.Erickson_murine_development	Dataset from [PMID: 40502176] with clonal atlas of murine development.
datasets.Liu_NSCLC_CD8	Dataset from Liu et al. [PMID: 35121991] with CD8 T cells from NSCLC.

Preprocessing

pp.clones_adata	Creates a clone-level AnnData object from a cell-level AnnData object.
pp.make_unique_clones	Prepares a clone2vec-friendly AnnData object by handling multiple clonal labels per cell.
pp.recalc_composition	Create a new annotated data matrix at the clone level with updated cell type proportions.
pp.transfer_annotation	Transfer clonal labels from a clones AnnData object to a adata AnnData object, or otherwise.
pp.transfer_expression	Summarize gene expression at the clone level using a specified strategy (sum or average).

Tools

tl.clonal_nn	Computes and adds a clone-to-clone adjacency graph to a clones object.
tl.clonocluster	Performing ClonoCluster [PMID: 36819662] algorithm for lineage-informed gene expression clustering.
tl.smooth	Performing kNN smoothing on adata.X, adata.layers[layer], adata.obs[name], or adata.obsm[name] for n_steps iterations using adata.obsp[graph_key].
tl.group_connectivity	Computes connectivities (number of observed over the number of expected edges) of groups based on the graph.

Embeddings

tl.clone2vec	Learn a clonal embedding using Skip-Gram and store the embeddings.
tl.clone2vec_Poi	Learn a clonal embedding using fastglmpca [PMID: 39110511] Python implementation and store the embeddings.
tl.project_clone2vec	Fitting of new clones to the reference clone2vec embeddings using kNN between query and reference datasets.
tl.project_clone2vec_Poi	Fitting of new clones to the reference clone2vec Poisson embeddings using kNN between query and reference datasets.
tl.find_mnn	Find anchors between batches in clones.obs[batch_key] using MNNs using clones.obsm[use_rep].
tl.align	Align clones.obsm[use_rep] using MNNs found in clones.uns[uns_key] via procrustes alignment (PA).

Associations

tl.associations	Calculate correlation between features in adata and a response variable.
tl.graph_associations	Computes the supervised principal components analysis (sPCA) of the data [10.1016/j.patcog.2010.12.015].
tl.eigenvalue_test	Performs Johnstone’s Spiked Covariance Test to identify if the embedding is random.
tl.catboost	Performs CatBoost regression or classification on the data aiming to identify associations between the features and the response variable.

Utils

utils.stack_layers	Function stacks layers of an AnnData object into a single layer in adata.X.
utils.correct_shap	Correct SHAP values by dividing by the maximum absolute value of SHAP values for each feature.
utils.connect_clones	Creates a graph of connected clones based on a grouping variable.
utils.get_connectivity_matrix	Get group connectivity matrix from adata.uns[uns_key].
utils.regress_categories	Performs expression regression on categorical variables in obs_key.
utils.impute	Impute missing values in an observation column using k-nearest neighbors.
utils.gs	Function to perform geometric sketching on an AnnData object.
utils.laplacian_eigenmaps	Compute Laplacian eigenmaps from a sparse graph stored in adata.obsp.

Plotting

pl.group_scatter	Plots the spatial distribution of a single clone on a 2D embedding.
pl.group_kde	Plots kernel density estimates (KDE) of cells from one or multiple groups on a 2D embedding.
pl.loss_history	Plot the mean loss per epoch and its change across epochs during clone2vec training.
pl.clone_size	Plot basic statistics of clone size distribution.
pl.nesting_clones	Plot ternary composition of clones labeled by two different injections.
pl.volcano	Draw a volcano using provided p-values and logFCs.
pl.shap_volcano	Draws volcano-like plot showing the result of the associations analysis with CatBoost and SHAP.
pl.barplot	Draws barplot colored by the value in each bar.
pl.heatmap	Draws pretty heatmap.
pl.predictors_comparison	Draws barplot of SHAP values for two groups.
pl.catboost_perfomance	Compares predicted by CatBoost and observed values.
pl.clones2cells	Draws two interactive sctterplots with gene expression (left) and clonal (right) embeddings.
pl.graph	Draws graph on top of the embedding.
pl.group_connectivity	Plot group connectivity on top of the embedding.
pl.pca_loadings	Plot PCA loadings for a given component.
pl.scaled_dotplot	Function plots dotplot colored by scaled expression values.
pl.scatter2vars	Plots embedding colored by two continious variables.
pl.embedding_axis	Adds two perpendicular arrows (L-shape) to the bottom-left corner of the plot to mimic standard single-cell embedding visualizations (Seurat/Scanpy).
pl.small_cbar	Adds small colorbars to the right of the specified axes.
pl.fancy_legend	Adds a legend to the plot with cluster labels aligned to the cluster centers.

Seurat

seurat.read	Reads .rds-file or .qsave-file with Seurat object and returns AnnData or MuData object.
seurat.integrate_data	Performs single-cell data integration via Seurat's CCA or RPCA approach.