scvi.model.base.RNASeqMixin#
Methods table#
|
. |
|
Generate gene-gene correlation matrix using scvi uncertainty and expression. |
|
Returns the latent library size for each cell. |
|
Estimates for the parameters of the likelihood \(p(x \mid z)\). |
|
Returns the normalized (decoded) gene expression. |
|
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 [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_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 (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_samples (int) – Number of posterior samples to use for estimation.
batch_size (int) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.rna_size_factor (int) – size factor for RNA prior to sampling gamma distribution.
transform_batch (Optional[Sequence[Union[int, float, str]]]) –
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 (Literal['spearman', 'pearson']) – One of “pearson”, “spearman”.
- Returns:
Gene-gene correlation matrix
- Return type:
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 (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.give_mean (bool) – Return the mean or a sample from the posterior distribution.
batch_size (Optional[int]) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.
- Return type:
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 (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_samples (Optional[int]) – Number of posterior samples to use for estimation.
give_mean (Optional[bool]) – Return expected value of parameters or a samples
batch_size (Optional[int]) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.
- Return type:
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 (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 (Union[float, Literal['latent']]) – 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.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
.n_samples_overall (int) –
- 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:
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 (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_samples (int) – Number of samples for each cell.
gene_list (Optional[Sequence[str]]) – Names of genes of interest.
batch_size (Optional[int]) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.
- Returns:
x_new :
torch.Tensor
tensor with shape (n_cells, n_genes, n_samples)- Return type: