pegasus.find_markers

pegasus.find_markers(data, label_attr, de_key='de_res', n_jobs=-1, min_gain=1.0, random_state=0, remove_ribo=False)[source]

Find markers using gradient boosting method.

Parameters

  • data (anndata.AnnData) – Annotated data matrix with rows for cells and columns for genes.

  • label_attr (str) – Cluster labels used for finding markers. Must exist in data.obs.

  • de_key (str, optional, default: "de_res") – Keyword of DE analysis result stored in data.varm.

  • n_jobs (int, optional, default: -1) – Number of threads to used. -1 refers to using all physical CPU cores.

  • min_gain (float, optional, default: 1.0) – Only report genes with a feature importance score (in gain) of at least min_gain.

  • random_state (int, optional, default: 0) – Random seed set for reproducing results.

  • remove_ribo (bool, optional, default: False) – If True, remove ribosomal genes with either RPL or RPS as prefixes.

Returns

markers – A Python dictionary containing marker information in structure dict[cluster_id]['up' or 'down'][dataframe].

Return type

Dict[str, Dict[str, List[str]]]

Examples

>>> marker_dict = pg.find_markers(adata, label_attr = 'leiden_labels')