scvi.model.base.ArchesMixin#
Methods table#
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Online update of a reference model with scArches algorithm [Lotfollahi et al., 2021]. |
|
Prepare data for query integration. |
Methods#
load_query_data
- classmethod ArchesMixin.load_query_data(adata, reference_model, inplace_subset_query_vars=False, use_gpu=None, unfrozen=False, freeze_dropout=False, freeze_expression=True, freeze_decoder_first_layer=True, freeze_batchnorm_encoder=True, freeze_batchnorm_decoder=False, freeze_classifier=True)[source]#
Online update of a reference model with scArches algorithm [Lotfollahi et al., 2021].
- Parameters:
adata (
AnnData
) – AnnData organized in the same way as data used to train model. It is not necessary to run setup_anndata, as AnnData is validated against theregistry
.reference_model (
Union
[str
,BaseModelClass
]) – Either an already instantiated model of the same class, or a path to saved outputs for reference model.inplace_subset_query_vars (
bool
(default:False
)) – Whether to subset and rearrange query vars inplace based on vars used to train reference model.use_gpu (
Union
[str
,int
,bool
,None
] (default:None
)) – Load model on default GPU if available (if None or True), or index of GPU to use (if int), or name of GPU (if str), or use CPU (if False).unfrozen (
bool
(default:False
)) – Override all other freeze options for a fully unfrozen modelfreeze_dropout (
bool
(default:False
)) – Whether to freeze dropout during trainingfreeze_expression (
bool
(default:True
)) – Freeze neurons corersponding to expression in first layerfreeze_decoder_first_layer (
bool
(default:True
)) – Freeze neurons corersponding to first layer in decoderfreeze_batchnorm_encoder (
bool
(default:True
)) – Whether to freeze batchnorm weight and bias during training for encoderfreeze_batchnorm_decoder (
bool
(default:False
)) – Whether to freeze batchnorm weight and bias during training for decoderfreeze_classifier (
bool
(default:True
)) – Whether to freeze classifier completely. Only applies to SCANVI.
prepare_query_anndata
- static ArchesMixin.prepare_query_anndata(adata, reference_model, return_reference_var_names=False, inplace=True)[source]#
Prepare data for query integration.
This function will return a new AnnData object with padded zeros for missing features, as well as correctly sorted features.
- Parameters:
adata (
AnnData
) – AnnData organized in the same way as data used to train model. It is not necessary to run setup_anndata, as AnnData is validated against theregistry
.reference_model (
Union
[str
,BaseModelClass
]) – Either an already instantiated model of the same class, or a path to saved outputs for reference model.return_reference_var_names (
bool
(default:False
)) – Only load and return reference var names if True.inplace (
bool
(default:True
)) – Whether to subset and rearrange query vars inplace or return new AnnData.
- Return type:
- Returns:
Query adata ready to use in load_query_data unless return_reference_var_names in which case a pd.Index of reference var names is returned.