API

Metrics

Import as:

import scib_rapids
scib_rapids.ilisi_knn(...)

isolated_labels(X, labels, batch[, rescale, ...])	Isolated label score.
nmi_ari_cluster_labels_kmeans(X, labels)	Compute NMI and ARI between k-means clusters and labels.
nmi_ari_cluster_labels_leiden(X, labels[, ...])	Compute NMI and ARI between leiden clusters and labels.
pcr_comparison(X_pre, X_post, covariate[, scale])	Principal component regression (PCR) comparison.
silhouette_label(X, labels[, rescale, ...])	Average silhouette width (ASW).
silhouette_batch(X, labels, batch[, ...])	Average silhouette width (ASW) with respect to batch ids within each label.
bras(X, labels, batch[, chunk_size, metric, ...])	Batch removal adapted silhouette (BRAS).
ilisi_knn(X, batches[, perplexity, scale])	Compute the integration LISI (iLISI).
clisi_knn(X, labels[, perplexity, scale])	Compute the cell-type LISI (cLISI).
kbet(X, batches[, alpha])	Compute kBET.
kbet_per_label(X, batches, labels[, alpha, ...])	Compute kBET score per cell type label.
graph_connectivity(X, labels)	Quantify the connectivity of the subgraph per cell type label.

Utils

utils.cdist(x, y[, metric])	CuPy implementation of pairwise distance computation.
utils.pdist_squareform(X)	CuPy implementation of pairwise euclidean distance matrix.
utils.silhouette_samples(X, labels[, ...])	Compute the Silhouette Coefficient for each observation using CuPy.
utils.KMeans([n_clusters, init, n_init, ...])	CuPy/RAPIDS implementation of KMeans clustering.
utils.pca(X[, n_components, return_svd])	Principal component analysis (PCA) using CuPy.
utils.principal_component_regression(X, ...)	Principal component regression (PCR) using CuPy.
utils.one_hot(y[, n_classes])	One-hot encode an array.
utils.compute_simpson_index(knn_dists, ...)	Compute the Simpson index for each cell using a fused CUDA kernel.
utils.convert_knn_graph_to_idx(X)	Convert a kNN graph to indices and distances.
utils.check_square(X)	Check if a matrix is square.
utils.diffusion_nn(X, k[, n_comps])	Diffusion-based neighbors.

Nearest neighbors

nearest_neighbors.pynndescent(X, n_neighbors)	Run pynndescent approximate nearest neighbor search.
nearest_neighbors.NeighborsResults(indices, ...)	Nearest neighbors results data store.