snapatac2.pl.motif_enrichment#

snapatac2.pl.motif_enrichment(enrichment, min_log_fc=1, max_fdr=0.01, **kwargs)[source]#

Plot motif enrichment scores across groups.

Use this function to summarize motif enrichment tables returned for multiple groups as a clustered heatmap.

Anti-Patterns#

  • Do NOT pass a single enrichment table. Pass a mapping from group names to Polars DataFrames with matching motif rows.

  • Do NOT rename required columns. Each table must contain id, log2(fold change), adjusted p-value, and p-value.

type enrichment:

dict of (str, polars.DataFrame)

param enrichment:

Mapping from group names to motif enrichment result tables. Tables must have aligned rows and include required motif statistics columns.

type enrichment:

dict of (str, polars.DataFrame)

type min_log_fc:

float

param min_log_fc:

Keep motifs with at least one absolute log2 fold-change greater than or equal to this value.

type min_log_fc:

float

type max_fdr:

float

param max_fdr:

Keep motifs with at least one adjusted p-value less than or equal to this value.

type max_fdr:

float

type **kwargs:

param **kwargs:

Additional rendering options passed to snapatac2.pl.heatmap and snapatac2.pl.render_plot, such as show, interactive, out_file, and clustering options accepted by heatmap.

returns:

Returns a Plotly figure when show=False and out_file=None; otherwise renders or saves the plot and returns None.

rtype:

plotly.graph_objects.Figure or None

Examples

>>> import polars as pl
>>> import snapatac2 as snap
>>> table = pl.DataFrame({
...     "id": ["MA0001.1", "MA0002.1"],
...     "log2(fold change)": [1.5, -0.8],
...     "adjusted p-value": [0.001, 0.2],
...     "p-value": [1e-5, 0.05],
... })
>>> enrichment = {"cluster_1": table, "cluster_2": table}
>>> fig = snap.pl.motif_enrichment(enrichment, show=False)
>>> fig.update_layout(title="Motif enrichment")
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