pegasus.volcano

pegasus.volcano(data, cluster_id, de_key='de_res', de_test='mwu', qval_threshold=0.05, log2fc_threshold=1.0, top_n=20, panel_size=(6, 4), return_fig=False, dpi=300.0)[source]

Generate Volcano plots (-log10 p value vs. log2 fold change) for visualizing DE results.

Parameters

  • data (MultimodalData, UnimodalData, or anndata.AnnData object.) – Single cell expression data.

  • cluster_id (str) –

    Cluster ID for the cluster we want to show DE results. There are two cases:

    • If condition is None in pg.de_analysis: Just specify one cluster label in the cluster attribute used in pg.de_analysis.

    • If condition is not None in pg.de_analysis: Specify cluster ID in this format: “cluster_label:cond_level”, where cluster_label is the cluster label, and cond_level is the condition ID. And this shows result of cells within the cluster under the specific condition.

  • de_key (str, optional, default: de_res) – The varm keyword for DE results. data.varm[de_key] should store the full DE result table.

  • de_test (str, optional, default: mwu) – Which DE test results to show. Use MWU test result by default.

  • qval_threshold (float, optional, default: 0.05.) – Selected FDR rate. A horizontal line indicating this rate will be shown in the figure.

  • log2fc_threshold (float, optional, default: 1.0) – Log2 fold change threshold to highlight biologically interesting genes. Two vertical lines representing negative and positive log2 fold change will be shown.

  • top_n (int, optional, default: 20) – Number of top DE genes to show names. Genes are ranked by Log2 fold change.

  • panel_size (Tuple[float, float], optional, default: (6, 4)) – The size (width, height) in inches of figure.

  • return_fig (bool, optional, default: False) – Return a Figure object if True; return None otherwise.

  • dpi (float, optional, default: 300.0) – The resolution in dots per inch.

Returns

A matplotlib.figure.Figure object containing the dot plot if return_fig == True

Return type

Figure object

Examples

>>> pg.volcano(data, cluster_id = '1', dpi=200)