scvi.model.base.RNASeqMixin#

class scvi.model.base.RNASeqMixin[source]#

General purpose methods for RNA-seq analysis.

Methods table#

differential_expression([adata, groupby, ...])

A unified method for differential expression analysis.

get_feature_correlation_matrix([adata, ...])

Generate gene-gene correlation matrix using scvi uncertainty and expression.

get_latent_library_size([adata, indices, ...])

Returns the latent library size for each cell.

get_likelihood_parameters([adata, indices, ...])

Estimates for the parameters of the likelihood \(p(x \mid z)\)

get_normalized_expression([adata, indices, ...])

Returns the normalized (decoded) gene expression.

posterior_predictive_sample([adata, ...])

Generate observation samples from the posterior predictive distribution.

Methods#

differential_expression#

RNASeqMixin.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 [Lopez18] and “change” mode DE [Boyeau19].

Parameters:
adata : AnnData | NoneOptional[AnnData] (default: None)

AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

groupby : str | NoneOptional[str] (default: None)

The key of the observations grouping to consider.

group1 : Iterable[str] | NoneOptional[Iterable[str]] (default: None)

Subset of groups, e.g. [‘g1’, ‘g2’, ‘g3’], to which comparison shall be restricted, or all groups in groupby (default).

group2 : str | NoneOptional[str] (default: None)

If None, compare each group in group1 to the union of the rest of the groups in groupby. If a group identifier, compare with respect to this group.

idx1 : Sequence[int] | Sequence[bool] | str | NoneUnion[Sequence[int], Sequence[bool], str, None] (default: None)

idx1 and idx2 can be used as an alternative to the AnnData keys. Custom identifier for group1 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 on adata.obs, as described in pandas.DataFrame.query() If idx1 is not None, this option overrides group1 and group2.

idx2 : Sequence[int] | Sequence[bool] | str | NoneUnion[Sequence[int], Sequence[bool], str, None] (default: None)

Custom identifier for group2 that has the same properties as idx1. By default, includes all cells not specified in idx1.

mode : {‘vanilla’, ‘change’}Literal[‘vanilla’, ‘change’] (default: 'change')

Method for differential expression. See user guide for full explanation.

delta : float (default: 0.25)

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 : int | NoneOptional[int] (default: None)

Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.

all_stats : bool (default: True)

Concatenate count statistics (e.g., mean expression group 1) to DE results.

batch_correction : bool (default: False)

Whether to correct for batch effects in DE inference.

batchid1 : Iterable[str] | NoneOptional[Iterable[str]] (default: None)

Subset of categories from batch_key registered in setup_anndata, e.g. [‘batch1’, ‘batch2’, ‘batch3’], for group1. Only used if batch_correction is True, and by default all categories are used.

batchid2 : Iterable[str] | NoneOptional[Iterable[str]] (default: None)

Same as batchid1 for group2. batchid2 must either have null intersection with batchid1, or be exactly equal to batchid1. When the two sets are exactly equal, cells are compared by decoding on the same batch. When sets have null intersection, cells from group1 and group2 are decoded on each group in group1 and group2, respectively.

fdr_target : float (default: 0.05)

Tag features as DE based on posterior expected false discovery rate.

silent : bool (default: False)

If True, disables the progress bar. Default: False.

**kwargs

Keyword args for scvi.model.base.DifferentialComputation.get_bayes_factors()

Return type:

DataFrame

Returns:

Differential expression DataFrame.

get_feature_correlation_matrix#

RNASeqMixin.get_feature_correlation_matrix(adata=None, indices=None, n_samples=10, batch_size=64, rna_size_factor=1000, transform_batch=None, correlation_type='spearman')[source]#

Generate gene-gene correlation matrix using scvi uncertainty and expression.

Parameters:
adata : AnnData | NoneOptional[AnnData] (default: None)

AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

indices : Sequence[int] | NoneOptional[Sequence[int]] (default: None)

Indices of cells in adata to use. If None, all cells are used.

n_samples : int (default: 10)

Number of posterior samples to use for estimation.

batch_size : int (default: 64)

Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.

rna_size_factor : int (default: 1000)

size factor for RNA prior to sampling gamma distribution.

transform_batch : Sequence[Union[int, float, str]] | NoneOptional[Sequence[Union[int, float, str]]] (default: None)

Batches to condition on. If transform_batch is:

  • None, then real observed batch is used.

  • int, then batch transform_batch is used.

  • list of int, then values are averaged over provided batches.

correlation_type : {‘spearman’, ‘pearson’}Literal[‘spearman’, ‘pearson’] (default: 'spearman')

One of “pearson”, “spearman”.

Return type:

DataFrame

Returns:

Gene-gene correlation matrix

get_latent_library_size#

RNASeqMixin.get_latent_library_size(adata=None, indices=None, give_mean=True, batch_size=None)[source]#

Returns the latent library size for each cell.

This is denoted as \(\ell_n\) in the scVI paper.

Parameters:
adata : AnnData | NoneOptional[AnnData] (default: None)

AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

indices : Sequence[int] | NoneOptional[Sequence[int]] (default: None)

Indices of cells in adata to use. If None, all cells are used.

give_mean : bool (default: True)

Return the mean or a sample from the posterior distribution.

batch_size : int | NoneOptional[int] (default: None)

Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.

Return type:

ndarray

get_likelihood_parameters#

RNASeqMixin.get_likelihood_parameters(adata=None, indices=None, n_samples=1, give_mean=False, batch_size=None)[source]#

Estimates for the parameters of the likelihood \(p(x \mid z)\)

Parameters:
adata : AnnData | NoneOptional[AnnData] (default: None)

AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

indices : Sequence[int] | NoneOptional[Sequence[int]] (default: None)

Indices of cells in adata to use. If None, all cells are used.

n_samples : int | NoneOptional[int] (default: 1)

Number of posterior samples to use for estimation.

give_mean : bool | NoneOptional[bool] (default: False)

Return expected value of parameters or a samples

batch_size : int | NoneOptional[int] (default: None)

Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.

Return type:

{str: ndarray}Dict[str, ndarray]

get_normalized_expression#

RNASeqMixin.get_normalized_expression(adata=None, indices=None, transform_batch=None, gene_list=None, library_size=1, n_samples=1, n_samples_overall=None, batch_size=None, return_mean=True, return_numpy=None)[source]#

Returns the normalized (decoded) gene expression.

This is denoted as \(\rho_n\) in the scVI paper.

Parameters:
adata : AnnData | NoneOptional[AnnData] (default: None)

AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

indices : Sequence[int] | NoneOptional[Sequence[int]] (default: None)

Indices of cells in adata to use. If None, all cells are used.

transform_batch : Sequence[Union[int, float, str]] | NoneOptional[Sequence[Union[int, float, str]]] (default: None)

Batch to condition on. If transform_batch is:

  • None, then real observed batch is used.

  • int, then batch transform_batch is used.

gene_list : Sequence[str] | NoneOptional[Sequence[str]] (default: None)

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 : float | {‘latent’}Union[float, Literal[‘latent’]] (default: 1)

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 libary size.

n_samples : int (default: 1)

Number of posterior samples to use for estimation.

batch_size : int | NoneOptional[int] (default: None)

Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.

return_mean : bool (default: True)

Whether to return the mean of the samples.

return_numpy : bool | NoneOptional[bool] (default: None)

Return a ndarray instead of a DataFrame. DataFrame includes gene names as columns. If either n_samples=1 or return_mean=True, defaults to False. Otherwise, it defaults to True.

Return type:

ndarray | DataFrameUnion[ndarray, DataFrame]

Returns:

If n_samples > 1 and return_mean is False, then the shape is (samples, cells, genes). Otherwise, shape is (cells, genes). In this case, return type is DataFrame unless return_numpy is True.

posterior_predictive_sample#

RNASeqMixin.posterior_predictive_sample(adata=None, indices=None, n_samples=1, gene_list=None, batch_size=None)[source]#

Generate observation samples from the posterior predictive distribution.

The posterior predictive distribution is written as \(p(\hat{x} \mid x)\).

Parameters:
adata : AnnData | NoneOptional[AnnData] (default: None)

AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

indices : Sequence[int] | NoneOptional[Sequence[int]] (default: None)

Indices of cells in adata to use. If None, all cells are used.

n_samples : int (default: 1)

Number of samples for each cell.

gene_list : Sequence[str] | NoneOptional[Sequence[str]] (default: None)

Names of genes of interest.

batch_size : int | NoneOptional[int] (default: None)

Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.

Return type:

ndarray

Returns:

x_new : torch.Tensor tensor with shape (n_cells, n_genes, n_samples)