Helpers

Helpers#

The cytetype package also exports three helper functions.

Plot the supporting marker genes#

marker_dotplot reads the results that CyteType.run() stored in the AnnData object and draws a Scanpy dot plot of the genes that support each label, with the clusters grouped by the categories in the results. It needs Scanpy, which is not a dependency of cytetype.

from cytetype import marker_dotplot

marker_dotplot(adata, group_key="leiden")

cytetype.marker_dotplot(adata, group_key, results_prefix='cytetype', n_top_markers=3, gene_symbols=None, **kwargs)#

Dotplot of top marker genes grouped by CyteType cluster categories.

Reads stored CyteType results from adata.uns and builds a category-grouped marker dict suitable for sc.pl.dotplot.

Parameters:

adata (AnnData) – AnnData object that has been annotated by CyteType (results stored in adata.uns).
group_key (str) – The original cluster key used during annotation (e.g. "leiden").
results_prefix (str, default: 'cytetype') – Prefix used when the results were stored. Must match the results_prefix passed to CyteType.run().
n_top_markers (int, default: 3) – Number of top supporting genes to display per cluster.
gene_symbols (str | None, default: None) – Column in adata.var containing gene symbols. Forwarded to sc.pl.dotplot via the gene_symbols parameter; marker genes are matched to its values instead of var_names.
**kwargs (Any) – Additional keyword arguments forwarded to sc.pl.dotplot (e.g. cmap, use_raw).

Return type:

Any

Returns:

The return value of sc.pl.dotplot.

Notes

Rows follow the order of the cluster categories. Labels without a category (e.g. "Unknown") are placed last. Genes come from the structured output’s keySupportingGenes, falling back to supportingMarkers for jobs without it. They are matched case-insensitively to the names sc.pl.dotplot looks up (the server reports upper-case symbols, e.g. CD8A for mouse Cd8a). Genes that are not found are skipped with a warning, and categories left without genes are dropped.

Find marker genes in backed AnnData#

rank_genes_groups_backed computes marker genes for an AnnData object opened in backed mode, such as with anndata.read_h5ad(path, backed="r"). It reads the cells in chunks of cell_batch_size instead of loading the whole matrix, compares each group with all other cells using Welch’s t-test, and writes Scanpy-compatible results to adata.uns[key_added]. Pass the same key to CyteType as rank_key. Large datasets shows where it fits in a workflow.

cytetype.rank_genes_groups_backed(adata, groupby, *, use_raw=False, layer=None, n_genes=None, key_added='rank_genes_groups', cell_batch_size=5000, corr_method='benjamini-hochberg', pts=False, copy=False)#

Memory-efficient rank_genes_groups for backed/on-disk AnnData objects.

Drop-in replacement for sc.tl.rank_genes_groups that works on backed _CSRDataset matrices by streaming cell chunks instead of loading the full matrix. Uses Welch’s t-test with one-vs-rest comparison.

Writes scanpy-compatible output to adata.uns[key_added].

Return type:: AnnData | None

Subsample cells per cluster#

subsample_by_group keeps at most max_cells_per_group randomly chosen cells from each group and returns them as a new in-memory AnnData object. Groups with fewer cells are kept whole, and random_state makes the selection reproducible.

cytetype.subsample_by_group(adata, group_key, max_cells_per_group=1000, random_state=0)#

Subsample cells from an AnnData object, capping each group to a maximum count.

Groups smaller than max_cells_per_group are kept intact.