pegasus.calc_kBET¶
- pegasus.calc_kBET(data, attr, rep='pca', K=25, alpha=0.05, n_jobs=- 1, random_state=0, temp_folder=None, use_cache=True)[source]¶
Calculate the kBET metric of the data regarding a specific sample attribute and embedding.
The kBET metric is defined in [Büttner18], which measures if cells from different samples mix well in their local neighborhood.
- Parameters
data (
pegasusio.MultimodalData
) – Annotated data matrix with rows for cells and columns for genes.attr (
str
) – The sample attribute to consider. Must exist indata.obs
.rep (
str
, optional, default:"pca"
) – The embedding representation to be used. The key'X_' + rep
must exist indata.obsm
. By default, use PCA coordinates.K (
int
, optional, default:25
) – Number of nearest neighbors, using L2 metric.alpha (
float
, optional, default:0.05
) – Acceptance rate threshold. A cell is accepted if its kBET p-value is greater than or equal toalpha
.n_jobs (
int
, optional, default:-1
) – Number of threads used. If-1
, use all physical CPU cores.random_state (
int
, optional, default:0
) – Random seed set for reproducing results.temp_folder (
str
, optional, default:None
) – Temporary folder for joblib execution.use_cache (
bool
, optional, default:True
) – If use cache results for kNN.
- Return type
Tuple
[float
,float
,float
]- Returns
stat_mean (
float
) – Mean kBET chi-square statistic over all cells.pvalue_mean (
float
) – Mean kBET p-value over all cells.accept_rate (
float
) – kBET Acceptance rate of the sample.
Examples
>>> pg.calc_kBET(data, attr = 'Channel')
>>> pg.calc_kBET(data, attr = 'Channel', rep = 'umap')