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 indata.obs
.de_key (
str
, optional, default:"de_res"
) – Keyword of DE analysis result stored indata.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 leastmin_gain
.random_state (
int
, optional, default:0
) – Random seed set for reproducing results.remove_ribo (
bool
, optional, default:False
) – IfTrue
, 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')