pegasus.demultiplex¶
- pegasus.demultiplex(rna_data, hashing_data, min_signal=10.0, alpha=0.0, alpha_noise=1.0, tol=1e-06, n_threads=1)[source]¶
Demultiplexing cell/nucleus-hashing data, using the estimated antibody background probability calculated in
demuxEM.estimate_background_probs
.- Parameters
rna_data (
UnimodalData
) – Data matrix for gene expression matrix.hashing_data (
UnimodalData
) – Data matrix for HTO count matrix.min_signal (
float
, optional, default:10.0
) – Any cell/nucleus with less thanmin_signal
hashtags from the signal will be marked asunknown
.alpha (
float
, optional, default:0.0
) – The Dirichlet prior concentration parameter (alpha) on samples. An alpha value < 1.0 will make the prior sparse.alpha_noise (
float
, optional, default:1.0
) – The Dirichlet prior concenration parameter on the background noise.tol (
float
, optional, default:1e-6
) – Threshold used for the EM convergence.n_threads (
int
, optional, default:1
) – Number of threads to use. Must be a positive integer.
- Returns
None
Update
data.obs
–data.obs["demux_type"]
: Demultiplexed types of the cells. Eithersinglet
,doublet
, orunknown
.data.obs["assignment"]
: Assigned samples of origin for each cell barcode.data.obs["assignment.dedup"]
: Only exist if one sample name can correspond to multiple feature barcodes. In this case, each feature barcode is assigned a unique sample name.
Examples
>>> demultiplex(rna_data, hashing_data)