scvi.model.MULTIVI#
- class scvi.model.MULTIVI(adata, n_genes, n_regions, modality_weights='equal', modality_penalty='Jeffreys', n_hidden=None, n_latent=None, n_layers_encoder=2, n_layers_decoder=2, dropout_rate=0.1, region_factors=True, gene_likelihood='zinb', dispersion='gene', use_batch_norm='none', use_layer_norm='both', latent_distribution='normal', deeply_inject_covariates=False, encode_covariates=False, fully_paired=False, protein_dispersion='protein', **model_kwargs)[source]#
Integration of multi-modal and single-modality data [Ashuach et al., 2021].
MultiVI is used to integrate multiomic datasets with single-modality (expression or accessibility) datasets.
- Parameters:
adata (AnnData) – AnnData object that has been registered via
setup_anndata()
.n_genes (int) – The number of gene expression features (genes).
n_regions (int) – The number of accessibility features (genomic regions).
modality_weights (Literal['equal', 'cell', 'universal']) – Weighting scheme across modalities. One of the following: *
"equal"
: Equal weight in each modality *"universal"
: Learn weights across modalities w_m. *"cell"
: Learn weights across modalities and cells. w_{m,c}modality_penalty (Literal['Jeffreys', 'MMD', 'None']) – Training Penalty across modalities. One of the following: *
"Jeffreys"
: Jeffreys penalty to align modalities *"MMD"
: MMD penalty to align modalities *"None"
: No penaltyn_hidden (Optional[int]) – Number of nodes per hidden layer. If
None
, defaults to square root of number of regions.n_latent (Optional[int]) – Dimensionality of the latent space. If
None
, defaults to square root ofn_hidden
.n_layers_encoder (int) – Number of hidden layers used for encoder NNs.
n_layers_decoder (int) – Number of hidden layers used for decoder NNs.
dropout_rate (float) – Dropout rate for neural networks.
model_depth – Model sequencing depth / library size.
region_factors (bool) – Include region-specific factors in the model.
gene_dispersion – One of the following *
'gene'
- genes_dispersion parameter of NB is constant per gene across cells *'gene-batch'
- genes_dispersion can differ between different batches *'gene-label'
- genes_dispersion can differ between different labelsprotein_dispersion (Literal['protein', 'protein-batch', 'protein-label']) – One of the following *
'protein'
- protein_dispersion parameter is constant per protein across cells *'protein-batch'
- protein_dispersion can differ between different batches NOT TESTED *'protein-label'
- protein_dispersion can differ between different labels NOT TESTEDlatent_distribution (Literal['normal', 'ln']) – One of *
'normal'
- Normal distribution *'ln'
- Logistic normal distribution (Normal(0, I) transformed by softmax)deeply_inject_covariates (bool) – Whether to deeply inject covariates into all layers of the decoder. If False, covariates will only be included in the input layer.
fully_paired (bool) – allows the simplification of the model if the data is fully paired. Currently ignored.
**model_kwargs – Keyword args for
MULTIVAE
gene_likelihood (Literal['zinb', 'nb', 'poisson']) –
dispersion (Literal['gene', 'gene-batch', 'gene-label', 'gene-cell']) –
use_batch_norm (Literal['encoder', 'decoder', 'none', 'both']) –
use_layer_norm (Literal['encoder', 'decoder', 'none', 'both']) –
encode_covariates (bool) –
Examples
>>> adata_rna = anndata.read_h5ad(path_to_rna_anndata) >>> adata_atac = scvi.data.read_10x_atac(path_to_atac_anndata) >>> adata_multi = scvi.data.read_10x_multiome(path_to_multiomic_anndata) >>> adata_mvi = scvi.data.organize_multiome_anndatas(adata_multi, adata_rna, adata_atac) >>> scvi.model.MULTIVI.setup_anndata(adata_mvi, batch_key="modality") >>> vae = scvi.model.MULTIVI(adata_mvi) >>> vae.train()
Notes
- The model assumes that the features are organized so that all expression features are
consecutive, followed by all accessibility features. For example, if the data has 100 genes and 250 genomic regions, the model assumes that the first 100 features are genes, and the next 250 are the regions.
- The main batch annotation, specified in
setup_anndata
, should correspond to the modality each cell originated from. This allows the model to focus mixing efforts, using an adversarial component, on mixing the modalities. Other covariates can be specified using the
categorical_covariate_keys
argument.
- The main batch annotation, specified in
Attributes table#
Data attached to model instance. |
|
Manager instance associated with self.adata. |
|
The current device that the module's params are on. |
|
Returns computed metrics during training. |
|
Whether the model has been trained. |
|
Observations that are in test set. |
|
Observations that are in train set. |
|
Observations that are in validation set. |
Methods table#
|
Converts a legacy saved model (<v0.15.0) to the updated save format. |
|
. |
|
. |
|
Impute the full accessibility matrix. |
|
Retrieves the |
|
Return the ELBO for the data. |
|
Returns the object in AnnData associated with the key in the data registry. |
|
Return the latent representation for each cell. |
|
Return library size factors. |
|
Return the marginal LL for the data. |
|
Returns the normalized (decoded) gene expression. |
|
Returns the foreground probability for proteins. |
|
Return the reconstruction error for the data. |
Return region-specific factors. |
|
|
Instantiate a model from the saved output. |
|
Online update of a reference model with scArches algorithm [Lotfollahi et al., 2021]. |
|
Return the full registry saved with the model. |
|
Prepare data for query integration. |
|
Registers an |
|
Save the state of the model. |
|
Sets up the |
|
Move model to device. |
|
Trains the model using amortized variational inference. |
|
Print summary of the setup for the initial AnnData or a given AnnData object. |
|
Print args used to setup a saved model. |
Attributes#
adata
adata_manager
device
history
is_trained
test_indices
train_indices
validation_indices
Methods#
convert_legacy_save
- classmethod MULTIVI.convert_legacy_save(dir_path, output_dir_path, overwrite=False, prefix=None)[source]#
Converts a legacy saved model (<v0.15.0) to the updated save format.
differential_accessibility
- MULTIVI.differential_accessibility(adata=None, groupby=None, group1=None, group2=None, idx1=None, idx2=None, mode='change', delta=0.05, batch_size=None, all_stats=True, batch_correction=False, batchid1=None, batchid2=None, fdr_target=0.05, silent=False, two_sided=True, **kwargs)[source]#
.
A unified method for differential accessibility analysis.
Implements
'vanilla'
DE [Lopez et al., 2018] and'change'
mode DE [Boyeau et al., 2019].adata
AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.
- groupby
The key of the observations grouping to consider.
- group1
Subset of groups, e.g. [
'g1'
,'g2'
,'g3'
], to which comparison shall be restricted, or all groups ingroupby
(default).- group2
If
None
, compare each group ingroup1
to the union of the rest of the groups ingroupby
. If a group identifier, compare with respect to this group.- idx1
idx1
andidx2
can be used as an alternative to the AnnData keys. Custom identifier forgroup1
that can be of three sorts: (1) a boolean mask, (2) indices, or (3) a string. If it is a string, then it will query indices that verifies conditions onadata.obs
, as described inpandas.DataFrame.query()
Ifidx1
is notNone
, this option overridesgroup1
andgroup2
.- idx2
Custom identifier for
group2
that has the same properties asidx1
. By default, includes all cells not specified inidx1
.- mode
Method for differential expression. See user guide for full explanation.
- delta
specific case of region inducing differential expression. In this case, we suppose that \(R \setminus [-\delta, \delta]\) does not induce differential expression (change model default case).
- batch_size
Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.- all_stats
Concatenate count statistics (e.g., mean expression group 1) to DE results.
- batch_correction
Whether to correct for batch effects in DE inference.
- batchid1
Subset of categories from
batch_key
registered insetup_anndata
, e.g. ['batch1'
,'batch2'
,'batch3'
], forgroup1
. Only used ifbatch_correction
isTrue
, and by default all categories are used.- batchid2
Same as
batchid1
for group2.batchid2
must either have null intersection withbatchid1
, or be exactly equal tobatchid1
. When the two sets are exactly equal, cells are compared by decoding on the same batch. When sets have null intersection, cells fromgroup1
andgroup2
are decoded on each group ingroup1
andgroup2
, respectively.- fdr_target
Tag features as DE based on posterior expected false discovery rate.
- silent
If True, disables the progress bar. Default: False.
- two_sided
Whether to perform a two-sided test, or a one-sided test.
- **kwargs
Keyword args for
scvi.model.base.DifferentialComputation.get_bayes_factors()
Differential accessibility DataFrame with the following columns: prob_da
the probability of the region being differentially accessible
- is_da_fdr
whether the region passes a multiple hypothesis correction procedure with the target_fdr threshold
- bayes_factor
Bayes Factor indicating the level of significance of the analysis
- effect_size
the effect size, computed as (accessibility in population 2) - (accessibility in population 1)
- emp_effect
the empirical effect, based on observed detection rates instead of the estimated accessibility scores from the PeakVI model
- est_prob1
the estimated probability of accessibility in population 1
- est_prob2
the estimated probability of accessibility in population 2
- emp_prob1
the empirical (observed) probability of accessibility in population 1
- emp_prob2
the empirical (observed) probability of accessibility in population 2
differential_expression
- MULTIVI.differential_expression(adata=None, groupby=None, group1=None, group2=None, idx1=None, idx2=None, mode='change', delta=0.25, batch_size=None, all_stats=True, batch_correction=False, batchid1=None, batchid2=None, fdr_target=0.05, silent=False, **kwargs)[source]#
.
A unified method for differential expression analysis. Implements
"vanilla"
DE [Lopez et al., 2018] and"change"
mode DE [Boyeau et al., 2019].adata
AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.
- groupby
The key of the observations grouping to consider.
- group1
Subset of groups, e.g. [
'g1'
,'g2'
,'g3'
], to which comparison shall be restricted, or all groups ingroupby
(default).- group2
If
None
, compare each group ingroup1
to the union of the rest of the groups ingroupby
. If a group identifier, compare with respect to this group.- idx1
idx1
andidx2
can be used as an alternative to the AnnData keys. Custom identifier forgroup1
that can be of three sorts: (1) a boolean mask, (2) indices, or (3) a string. If it is a string, then it will query indices that verifies conditions onadata.obs
, as described inpandas.DataFrame.query()
Ifidx1
is notNone
, this option overridesgroup1
andgroup2
.- idx2
Custom identifier for
group2
that has the same properties asidx1
. By default, includes all cells not specified inidx1
.- mode
Method for differential expression. See user guide for full explanation.
- delta
specific case of region inducing differential expression. In this case, we suppose that \(R \setminus [-\delta, \delta]\) does not induce differential expression (change model default case).
- batch_size
Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.- all_stats
Concatenate count statistics (e.g., mean expression group 1) to DE results.
- batch_correction
Whether to correct for batch effects in DE inference.
- batchid1
Subset of categories from
batch_key
registered insetup_anndata
, e.g. ['batch1'
,'batch2'
,'batch3'
], forgroup1
. Only used ifbatch_correction
isTrue
, and by default all categories are used.- batchid2
Same as
batchid1
for group2.batchid2
must either have null intersection withbatchid1
, or be exactly equal tobatchid1
. When the two sets are exactly equal, cells are compared by decoding on the same batch. When sets have null intersection, cells fromgroup1
andgroup2
are decoded on each group ingroup1
andgroup2
, respectively.- fdr_target
Tag features as DE based on posterior expected false discovery rate.
- silent
If True, disables the progress bar. Default: False.
- **kwargs
Keyword args for
scvi.model.base.DifferentialComputation.get_bayes_factors()
Differential expression DataFrame.
get_accessibility_estimates
- MULTIVI.get_accessibility_estimates(adata=None, indices=None, n_samples_overall=None, region_list=None, transform_batch=None, use_z_mean=True, threshold=None, normalize_cells=False, normalize_regions=False, batch_size=128, return_numpy=False)[source]#
Impute the full accessibility matrix.
Returns a matrix of accessibility probabilities for each cell and genomic region in the input (for return matrix A, A[i,j] is the probability that region j is accessible in cell i).
- Parameters:
adata (Optional[AnnData]) – AnnData object that has been registered with scvi. If
None
, defaults to the AnnData object used to initialize the model.indices (Sequence[int]) – Indices of cells in adata to use. If
None
, all cells are used.n_samples_overall (Optional[int]) – Number of samples to return in total
region_list (Optional[Sequence[str]]) – Regions to use. if
None
, all regions are used.transform_batch (Optional[Union[int, str]]) –
Batch to condition on. If transform_batch is:
None, then real observed batch is used
int, then batch transform_batch is used
use_z_mean (bool) – If True (default), use the distribution mean. Otherwise, sample from the distribution.
threshold (Optional[float]) – If provided, values below the threshold are replaced with 0 and a sparse matrix is returned instead. This is recommended for very large matrices. Must be between 0 and 1.
normalize_cells (bool) – Whether to reintroduce library size factors to scale the normalized probabilities. This makes the estimates closer to the input, but removes the library size correction. False by default.
normalize_regions (bool) – Whether to reintroduce region factors to scale the normalized probabilities. This makes the estimates closer to the input, but removes the region-level bias correction. False by default.
batch_size (int) – Minibatch size for data loading into model
return_numpy (bool) –
- Return type:
get_anndata_manager
- MULTIVI.get_anndata_manager(adata, required=False)[source]#
Retrieves the
AnnDataManager
for a given AnnData object specific to this model instance.Requires
self.id
has been set. Checks for anAnnDataManager
specific to this model instance.
get_elbo
- MULTIVI.get_elbo(adata=None, indices=None, batch_size=None)[source]#
Return the ELBO for the data.
The ELBO is a lower bound on the log likelihood of the data used for optimization of VAEs. Note, this is not the negative ELBO, higher is better.
- Parameters:
adata (Optional[AnnData]) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Optional[Sequence[int]]) – Indices of cells in adata to use. If
None
, all cells are used.batch_size (Optional[int]) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.
- Return type:
get_from_registry
- MULTIVI.get_from_registry(adata, registry_key)[source]#
Returns the object in AnnData associated with the key in the data registry.
AnnData object should be registered with the model prior to calling this function via the
self._validate_anndata
method.
get_latent_representation
- MULTIVI.get_latent_representation(adata=None, modality='joint', indices=None, give_mean=True, batch_size=None)[source]#
Return the latent representation for each cell.
- Parameters:
adata (Optional[AnnData]) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.modality (Literal['joint', 'expression', 'accessibility']) – Return modality specific or joint latent representation.
indices (Optional[Sequence[int]]) – Indices of cells in adata to use. If
None
, all cells are used.give_mean (bool) – Give mean of distribution or sample from it.
batch_size (Optional[int]) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.
- Returns:
-latent_representation (
ndarray
) Low-dimensional representation for each cell- Return type:
get_library_size_factors
- MULTIVI.get_library_size_factors(adata=None, indices=None, batch_size=128)[source]#
Return library size factors.
- Parameters:
adata (Optional[AnnData]) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Sequence[int]) – Indices of cells in adata to use. If
None
, all cells are used.batch_size (int) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.
- Returns:
Library size factor for expression and accessibility
- Return type:
get_marginal_ll
- MULTIVI.get_marginal_ll(adata=None, indices=None, n_mc_samples=1000, batch_size=None)[source]#
Return the marginal LL for the data.
The computation here is a biased estimator of the marginal log likelihood of the data. Note, this is not the negative log likelihood, higher is better.
- Parameters:
adata (Optional[AnnData]) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Optional[Sequence[int]]) – Indices of cells in adata to use. If
None
, all cells are used.n_mc_samples (int) – Number of Monte Carlo samples to use for marginal LL estimation.
batch_size (Optional[int]) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.
- Return type:
get_normalized_expression
- MULTIVI.get_normalized_expression(adata=None, indices=None, n_samples_overall=None, transform_batch=None, gene_list=None, use_z_mean=True, n_samples=1, batch_size=None, return_mean=True, return_numpy=False)[source]#
Returns the normalized (decoded) gene expression.
This is denoted as \(\rho_n\) in the scVI paper.
- Parameters:
adata (Optional[AnnData]) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Optional[Sequence[int]]) – Indices of cells in adata to use. If
None
, all cells are used.transform_batch (Optional[Sequence[Union[int, float, str]]]) –
Batch to condition on. If transform_batch is:
None, then real observed batch is used.
int, then batch transform_batch is used.
gene_list (Optional[Sequence[str]]) – Return frequencies of expression for a subset of genes. This can save memory when working with large datasets and few genes are of interest.
library_size – Scale the expression frequencies to a common library size. This allows gene expression levels to be interpreted on a common scale of relevant magnitude. If set to
"latent"
, use the latent library size.use_z_mean (bool) – If True, use the mean of the latent distribution, otherwise sample from it
n_samples (int) – Number of posterior samples to use for estimation.
batch_size (Optional[int]) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.return_mean (bool) – Whether to return the mean of the samples.
return_numpy (bool) –
- Returns:
If
n_samples
> 1 andreturn_mean
is False, then the shape is(samples, cells, genes)
. Otherwise, shape is(cells, genes)
. In this case, return type isDataFrame
unlessreturn_numpy
is True.- Return type:
get_protein_foreground_probability
- MULTIVI.get_protein_foreground_probability(adata=None, indices=None, transform_batch=None, protein_list=None, n_samples=1, batch_size=None, use_z_mean=True, return_mean=True, return_numpy=None)[source]#
Returns the foreground probability for proteins.
This is denoted as \((1 - \pi_{nt})\) in the totalVI paper.
- Parameters:
adata (Optional[AnnData]) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Optional[Sequence[int]]) – Indices of cells in adata to use. If
None
, all cells are used.transform_batch (Optional[Sequence[Union[int, float, str]]]) –
Batch to condition on. If transform_batch is:
None
- real observed batch is usedint
- batch transform_batch is usedList[int]
- average over batches in list
protein_list (Optional[Sequence[str]]) – Return protein expression for a subset of genes. This can save memory when working with large datasets and few genes are of interest.
n_samples (int) – Number of posterior samples to use for estimation.
batch_size (Optional[int]) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.return_mean (bool) – Whether to return the mean of the samples.
return_numpy (Optional[bool]) – Return a
ndarray
instead of aDataFrame
. DataFrame includes gene names as columns. If eithern_samples=1
orreturn_mean=True
, defaults toFalse
. Otherwise, it defaults toTrue
.use_z_mean (bool) –
- Returns:
foreground_probability - probability foreground for each protein
If
n_samples
> 1 andreturn_mean
is False, then the shape is(samples, cells, genes)
. Otherwise, shape is(cells, genes)
. In this case, return type isDataFrame
unlessreturn_numpy
is True.
get_reconstruction_error
- MULTIVI.get_reconstruction_error(adata=None, indices=None, batch_size=None)[source]#
Return the reconstruction error for the data.
This is typically written as \(p(x \mid z)\), the likelihood term given one posterior sample. Note, this is not the negative likelihood, higher is better.
- Parameters:
adata (Optional[AnnData]) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Optional[Sequence[int]]) – Indices of cells in adata to use. If
None
, all cells are used.batch_size (Optional[int]) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.
- Return type:
get_region_factors
load
- classmethod MULTIVI.load(dir_path, adata=None, use_gpu=None, prefix=None, backup_url=None)[source]#
Instantiate a model from the saved output.
- Parameters:
dir_path (str) – Path to saved outputs.
adata (Optional[Union[AnnData, MuData]]) – 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 the saved
scvi
setup dictionary. If None, will check for and load anndata saved with the model.use_gpu (Optional[Union[str, int, bool]]) – 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).
backup_url (Optional[str]) – URL to retrieve saved outputs from if not present on disk.
- Returns:
Model with loaded state dictionaries.
Examples
>>> model = ModelClass.load(save_path, adata) # use the name of the model class used to save >>> model.get_....
load_query_data
- classmethod MULTIVI.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 the
registry
.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) – Whether to subset and rearrange query vars inplace based on vars used to train reference model.
use_gpu (Optional[Union[str, int, bool]]) – 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) – Override all other freeze options for a fully unfrozen model
freeze_dropout (bool) – Whether to freeze dropout during training
freeze_expression (bool) – Freeze neurons corersponding to expression in first layer
freeze_decoder_first_layer (bool) – Freeze neurons corersponding to first layer in decoder
freeze_batchnorm_encoder (bool) – Whether to freeze batchnorm weight and bias during training for encoder
freeze_batchnorm_decoder (bool) – Whether to freeze batchnorm weight and bias during training for decoder
freeze_classifier (bool) – Whether to freeze classifier completely. Only applies to
SCANVI
.
load_registry
- static MULTIVI.load_registry(dir_path, prefix=None)[source]#
Return the full registry saved with the model.
prepare_query_anndata
- static MULTIVI.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 the
registry
.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) – Only load and return reference var names if True.
inplace (bool) – Whether to subset and rearrange query vars inplace or return new AnnData.
- Returns:
Query adata ready to use in
load_query_data
unlessreturn_reference_var_names
in which case a pd.Index of reference var names is returned.- Return type:
register_manager
- classmethod MULTIVI.register_manager(adata_manager)[source]#
Registers an
AnnDataManager
instance with this model class.Stores the
AnnDataManager
reference in a class-specific manager store. Intended for use in thesetup_anndata()
class method followed up by retrieval of theAnnDataManager
via the_get_most_recent_anndata_manager()
method in the model init method.Notes
Subsequent calls to this method with an
AnnDataManager
instance referring to the same underlying AnnData object will overwrite the reference to previousAnnDataManager
.- Parameters:
adata_manager (AnnDataManager) –
save
- MULTIVI.save(dir_path, prefix=None, overwrite=False, save_anndata=False, **anndata_write_kwargs)[source]#
Save the state of the model.
Neither the trainer optimizer state nor the trainer history are saved. Model files are not expected to be reproducibly saved and loaded across versions until we reach version 1.0.
- Parameters:
dir_path (str) – Path to a directory.
prefix (Optional[str]) – Prefix to prepend to saved file names.
overwrite (bool) – Overwrite existing data or not. If
False
and directory already exists atdir_path
, error will be raised.save_anndata (bool) – If True, also saves the anndata
anndata_write_kwargs – Kwargs for
write()
setup_anndata
- classmethod MULTIVI.setup_anndata(adata, layer=None, batch_key=None, size_factor_key=None, categorical_covariate_keys=None, continuous_covariate_keys=None, protein_expression_obsm_key=None, protein_names_uns_key=None, **kwargs)[source]#
Sets up the
AnnData
object for this model.A mapping will be created between data fields used by this model to their respective locations in adata. None of the data in adata are modified. Only adds fields to adata.
- Parameters:
adata (AnnData) – AnnData object. Rows represent cells, columns represent features.
layer (Optional[str]) – if not
None
, uses this as the key inadata.layers
for raw count data.batch_key (Optional[str]) – key in
adata.obs
for batch information. Categories will automatically be converted into integer categories and saved toadata.obs['_scvi_batch']
. IfNone
, assigns the same batch to all the data.size_factor_key (Optional[str]) – key in
adata.obs
for size factor information. Instead of using library size as a size factor, the provided size factor column will be used as offset in the mean of the likelihood. Assumed to be on linear scale.categorical_covariate_keys (Optional[List[str]]) – keys in
adata.obs
that correspond to categorical data. These covariates can be added in addition to the batch covariate and are also treated as nuisance factors (i.e., the model tries to minimize their effects on the latent space). Thus, these should not be used for biologically-relevant factors that you do _not_ want to correct for.continuous_covariate_keys (Optional[List[str]]) – keys in
adata.obs
that correspond to continuous data. These covariates can be added in addition to the batch covariate and are also treated as nuisance factors (i.e., the model tries to minimize their effects on the latent space). Thus, these should not be used for biologically-relevant factors that you do _not_ want to correct for.protein_expression_obsm_key (Optional[str]) – key in
adata.obsm
for protein expression data.protein_names_uns_key (Optional[str]) – key in
adata.uns
for protein names. If None, will use the column names ofadata.obsm[protein_expression_obsm_key]
if it is a DataFrame, else will assign sequential names to proteins.
to_device
- MULTIVI.to_device(device)[source]#
Move model to device.
- Parameters:
device (Union[str, int]) – Device to move model to. Options: ‘cpu’ for CPU, integer GPU index (eg. 0), or ‘cuda:X’ where X is the GPU index (eg. ‘cuda:0’). See torch.device for more info.
Examples
>>> adata = scvi.data.synthetic_iid() >>> model = scvi.model.SCVI(adata) >>> model.to_device('cpu') # moves model to CPU >>> model.to_device('cuda:0') # moves model to GPU 0 >>> model.to_device(0) # also moves model to GPU 0
train
- MULTIVI.train(max_epochs=500, lr=0.0001, use_gpu=None, train_size=0.9, validation_size=None, batch_size=128, weight_decay=0.001, eps=1e-08, early_stopping=True, save_best=True, check_val_every_n_epoch=None, n_steps_kl_warmup=None, n_epochs_kl_warmup=50, adversarial_mixing=True, plan_kwargs=None, **kwargs)[source]#
Trains the model using amortized variational inference.
- Parameters:
max_epochs (int) – Number of passes through the dataset.
lr (float) – Learning rate for optimization.
use_gpu (Optional[Union[str, int, bool]]) – Use 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).
train_size (float) – Size of training set in the range [0.0, 1.0].
validation_size (Optional[float]) – Size of the test set. If
None
, defaults to 1 -train_size
. Iftrain_size + validation_size < 1
, the remaining cells belong to a test set.batch_size (int) – Minibatch size to use during training.
weight_decay (float) – weight decay regularization term for optimization
eps (float) – Optimizer eps
early_stopping (bool) – Whether to perform early stopping with respect to the validation set.
save_best (bool) – Save the best model state with respect to the validation loss, or use the final state in the training procedure
check_val_every_n_epoch (Optional[int]) – Check val every n train epochs. By default, val is not checked, unless
early_stopping
isTrue
. If so, val is checked every epoch.n_steps_kl_warmup (Optional[int]) – Number of training steps (minibatches) to scale weight on KL divergences from 0 to 1. Only activated when
n_epochs_kl_warmup
is set to None. IfNone
, defaults tofloor(0.75 * adata.n_obs)
.n_epochs_kl_warmup (Optional[int]) – Number of epochs to scale weight on KL divergences from 0 to 1. Overrides
n_steps_kl_warmup
when both are notNone
.adversarial_mixing (bool) – Whether to use adversarial training to penalize the model for umbalanced mixing of modalities.
plan_kwargs (Optional[dict]) – Keyword args for
TrainingPlan
. Keyword arguments passed totrain()
will overwrite values present inplan_kwargs
, when appropriate.**kwargs – Other keyword args for
Trainer
.
view_anndata_setup
- MULTIVI.view_anndata_setup(adata=None, hide_state_registries=False)[source]#
Print summary of the setup for the initial AnnData or a given AnnData object.
view_setup_args