Tools: tl#

Any transformation of the data matrix that is not preprocessing. In contrast to a preprocessing function, a tool usually adds an easily interpretable annotation to the data matrix, which can then be visualized with a corresponding plotting function.

Embeddings#

tl.spectral(adata[, n_comps, features, ...])

Perform dimension reduction using Laplacian Eigenmaps.

tl.multi_spectral(adatas[, n_comps, ...])

Compute Laplacian Eigenmaps simultaneously on multiple modalities, with linear space and time complexity.

tl.umap(adata[, n_comps, use_dims, use_rep, ...])

type adata:

AnnData | AnnDataSet | ndarray

Clustering#

tl.leiden(adata[, resolution, ...])

Cluster cells into subgroups [Traag18].

tl.kmeans(adata, n_clusters[, n_iterations, ...])

Cluster cells into subgroups using the K-means algorithm, a classical algorithm in data mining.

tl.dbscan(adata[, eps, min_samples, ...])

Cluster cells into subgroups using the DBSCAN algorithm.

tl.hdbscan(adata[, min_cluster_size, ...])

Cluster cells into subgroups using the HDBSCAN algorithm.

Peak calling#

tl.macs3(adata, *[, groupby, qvalue, ...])

Call peaks using MACS3.

tl.merge_peaks(peaks, chrom_sizes[, half_width])

Merge peaks from different groups.

Differential analysis#

tl.marker_regions(data, groupby[, pvalue])

A quick-and-dirty way to get marker regions.

tl.diff_test(data, cell_group1, cell_group2)

Identify differentially accessible regions.

Motif analysis#

tl.motif_enrichment(motifs, regions, ...[, ...])

Identify enriched transcription factor motifs.

Network analysis (beta)#

tl.init_network_from_annotation(regions, ...)

Build CRE-gene network from gene annotations.

tl.add_cor_scores(network, *[, gene_mat, ...])

Compute correlation scores for any two connected nodes in the network.

tl.add_regr_scores(network, *[, peak_mat, ...])

Perform regression analysis for nodes and their parents in the network.

tl.add_tf_binding(network, *, motifs, ...[, ...])

Add TF motif binding information.

tl.link_tf_to_gene(network)

Contruct a genetic network by linking TFs to target genes.

tl.prune_network(network[, node_filter, ...])

Prune the network.

Utilities#

tl.aggregate_X(adata[, groupby, normalize, file])

Aggregate values in adata.X in a row-wise fashion.

tl.aggregate_cells(adata[, use_rep, ...])

Aggregate cells into pseudo-cells.