scanpy.external.pp.dca#
- scanpy.external.pp.dca(adata, mode='denoise', *, ae_type='nb-conddisp', normalize_per_cell=True, scale=True, log1p=True, hidden_size=(64, 32, 64), hidden_dropout=0.0, batchnorm=True, activation='relu', init='glorot_uniform', network_kwds=mappingproxy({}), epochs=300, reduce_lr=10, early_stop=15, batch_size=32, optimizer='RMSprop', random_state=0, threads=None, learning_rate=None, verbose=False, training_kwds=mappingproxy({}), return_model=False, return_info=False, copy=False)[source]#
Deep count autoencoder [Eraslan et al., 2019].
Fits a count autoencoder to the raw count data given in the anndata object in order to denoise the data and to capture hidden representation of cells in low dimensions. Type of the autoencoder and return values are determined by the parameters.
Note
More information and bug reports here.
- Parameters:
- adata
AnnData
An anndata file with
.raw
attribute representing raw counts.- mode
Literal
['denoise'
,'latent'
] (default:'denoise'
) denoise
overwritesadata.X
with denoised expression values. Inlatent
mode DCA addsadata.obsm['X_dca']
to given adata object. This matrix represent latent representation of cells via DCA.- ae_type
Literal
['zinb-conddisp'
,'zinb'
,'nb-conddisp'
,'nb'
] (default:'nb-conddisp'
) Type of the autoencoder. Return values and the architecture is determined by the type e.g.
nb
does not provide dropout probabilities. Types that end with “-conddisp”, assumes that dispersion is mean dependant.- normalize_per_cell
bool
(default:True
) If true, library size normalization is performed using the
sc.pp.normalize_per_cell
function in Scanpy and saved into adata object. Mean layer is re-introduces library size differences by scaling the mean value of each cell in the output layer. See the manuscript for more details.- scale
bool
(default:True
) If true, the input of the autoencoder is centered using
sc.pp.scale
function of Scanpy. Note that the output is kept as raw counts as loss functions are designed for the count data.- log1p
bool
(default:True
) If true, the input of the autoencoder is log transformed with a pseudocount of one using
sc.pp.log1p
function of Scanpy.- hidden_size
Sequence
[int
] (default:(64, 32, 64)
) Width of hidden layers.
- hidden_dropout
float
|Sequence
[float
] (default:0.0
) Probability of weight dropout in the autoencoder (per layer if list or tuple).
- batchnorm
bool
(default:True
) If true, batch normalization is performed.
- activation
str
(default:'relu'
) Activation function of hidden layers.
- init
str
(default:'glorot_uniform'
) Initialization method used to initialize weights.
- network_kwds
Mapping
[str
,Any
] (default:mappingproxy({})
) Additional keyword arguments for the autoencoder.
- epochs
int
(default:300
) Number of total epochs in training.
- reduce_lr
int
(default:10
) Reduces learning rate if validation loss does not improve in given number of epochs.
- early_stop
int
(default:15
) Stops training if validation loss does not improve in given number of epochs.
- batch_size
int
(default:32
) Number of samples in the batch used for SGD.
- optimizer
str
(default:'RMSprop'
) Type of optimization method used for training.
- random_state
Union
[int
,RandomState
,None
] (default:0
) Seed for python, numpy and tensorflow.
- threads
int
|None
(default:None
) Number of threads to use in training. All cores are used by default.
- learning_rate
float
|None
(default:None
) Learning rate to use in the training.
- verbose
bool
(default:False
) If true, prints additional information about training and architecture.
- training_kwds
Mapping
[str
,Any
] (default:mappingproxy({})
) Additional keyword arguments for the training process.
- return_model
bool
(default:False
) If true, trained autoencoder object is returned. See “Returns”.
- return_info
bool
(default:False
) If true, all additional parameters of DCA are stored in
adata.obsm
such as dropout probabilities (obsm[‘X_dca_dropout’]) and estimated dispersion values (obsm[‘X_dca_dispersion’]), in case that autoencoder is of type zinb or zinb-conddisp.- copy
bool
(default:False
) If true, a copy of anndata is returned.
- adata
- Return type:
- Returns:
If
copy
is true andreturn_model
is false, AnnData object is returned.In “denoise” mode,
adata.X
is overwritten with the denoised values. In “latent” mode, latent low dimensional representation of cells are stored inadata.obsm['X_dca']
andadata.X
is not modified. Note that these values are not corrected for library size effects.If
return_info
is true, all estimated distribution parameters are stored in AnnData like this:.obsm["X_dca_dropout"]
The mixture coefficient (pi) of the zero component in ZINB, i.e. dropout probability (if
ae_type
iszinb
orzinb-conddisp
)..obsm["X_dca_dispersion"]
The dispersion parameter of NB.
.uns["dca_loss_history"]
The loss history of the training. See
.history
attribute of Keras History class for mode details.
Finally, the raw counts are stored in
.raw
attribute of AnnData object.If
return_model
is given, trained model is returned. When bothcopy
andreturn_model
are true, a tuple of anndata and model is returned in that order.