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#

Compute densities on embeddings.

tl.embedding_density

Calculate the density of cells in an embedding (per condition).

Clustering and trajectory inference#

tl.leiden

Cluster cells into subgroups [Traag et al., 2019].

tl.dendrogram

Compute a hierarchical clustering for the given groupby categories.

tl.dpt

Infer progression of cells through geodesic distance along the graph [Haghverdi et al., 2016, Wolf et al., 2019].

tl.paga

Map out the coarse-grained connectivity structures of complex manifolds [Wolf et al., 2019].

Data integration#

tl.ingest

Map labels and embeddings from reference data to new data.

Marker genes#

tl.rank_genes_groups

Rank genes for characterizing groups.

tl.filter_rank_genes_groups

Filter out genes based on two criteria.

tl.marker_gene_overlap

Calculate an overlap score between data-derived marker genes and provided markers.

Gene scores, Cell cycle#

tl.score_genes

Score a set of genes [Tirosh et al., 2016].

tl.score_genes_cell_cycle

Score cell cycle genes [Satija et al., 2015].

Output types#

Functions with several outputs return a …Result if passed out=True, and take a …Out mapping as out to write each output to a place of choice.

tl.CellCycleResult

Outputs of score_genes_cell_cycle().

tl.CellCycleOut

Where a mapping out of score_genes_cell_cycle() writes each output, see CellCycleResult.

tl.DptResult

Outputs of dpt().

tl.DptOut

Where a mapping out of dpt() writes each output, see DptResult.