pegasus.infer_cell_types¶
-
pegasus.
infer_cell_types
(data, markers, de_test='mwu', de_alpha=0.05, de_key='de_res', threshold=0.5, ignore_nonde=False, output_file=None)[source]¶ Infer putative cell types for each cluster using legacy markers.
- Parameters
data (
MultimodalData
,UnimodalData
, oranndata.AnnData
.) – Data structure of count matrix and DE analysis results.markers (
str
orDict
) –- If
str
, it is a string representing a comma-separated list; each element in the list either refers to a JSON file containing legacy markers, or predefined markers
'human_immune'
for human immune cells;'mouse_immune'
for mouse immune cells;'human_brain'
for human brain cells;'mouse_brain'
for mouse brain cells;'human_lung'
for human lung cells.
- If
If
Dict
, it refers to a Python dictionary describing the markers.
de_test (
str
, optional, default:"mwu"
) – pegasus determines cell types using DE test results. This argument indicates which DE test result to use, can be either't'
,'fisher'
or'mwu'
. By default, it uses'mwu'
.de_alpha (
float
, optional, default:0.05
) – False discovery rate for controling family-wide error.de_key (
str
, optional, default:"de_res"
) – The keyword indata.varm
that stores DE analysis results.threshold (
float
, optional, defaut:0.5
) – Only report putative cell types with a score larger than or equal tothreshold
.ignore_nonde (
bool
, optional, default:False
) – Do not consider non DE genes as weak negative markers.output_file (
str
, optional, default:None
) – File name of output cluster annotation. IfNone
, do not write to any file.
- Returns
Python dictionary with cluster ID’s being keys, and their corresponding cell type lists sortec by scores being values.
- Return type
Dict[str, List["CellType"]]
Examples
>>> cell_type_dict = pg.infer_cell_types(adata, markers = 'human_immune,human_brain')