delnx.tl.NBFitResultΒΆ

class delnx.tl.NBFitResult(beta, overdispersions, mu, size_factors, deviances, design_matrix, design_column_names, feature_names, layer=None, condition_key=None, ql_dispersions=None, df0_prior=0.0, dispersion_trend=None, converged=<factory>)[source]ΒΆ

Result container for nb_fit.

betaΒΆ

Fitted coefficients, shape (n_genes, n_coefficients).

Type:

np.ndarray

overdispersionsΒΆ

MLE dispersion estimates per gene, shape (n_genes,).

Type:

np.ndarray

muΒΆ

Fitted mean values, shape (n_samples, n_genes).

Type:

np.ndarray

size_factorsΒΆ

Size factors per sample, shape (n_samples,).

Type:

np.ndarray

deviancesΒΆ

Deviance per gene, shape (n_genes,).

Type:

np.ndarray

design_matrixΒΆ

Design matrix used, shape (n_samples, n_coefficients).

Type:

np.ndarray

design_column_namesΒΆ

Column names for the design matrix (e.g., [β€œintercept”, β€œcondB”]).

Type:

list[str]

feature_namesΒΆ

Feature/gene names.

Type:

pd.Index

layerΒΆ

Layer used for count data.

Type:

str | None

condition_keyΒΆ

Condition key used for design.

Type:

str | None

ql_dispersionsΒΆ

Quasi-likelihood dispersions (if shrinkage applied).

Type:

np.ndarray | None

df0_priorΒΆ

Prior degrees of freedom from QL shrinkage.

Type:

float

dispersion_trendΒΆ

Fitted dispersion trend.

Type:

np.ndarray | None

convergedΒΆ

Convergence status per gene.

Type:

np.ndarray

Attributes tableΒΆ

Methods tableΒΆ

AttributesΒΆ

NBFitResult.condition_key: str | None = NoneΒΆ
NBFitResult.df0_prior: float = 0.0ΒΆ
NBFitResult.dispersion_trend: ndarray | None = NoneΒΆ
NBFitResult.layer: str | None = NoneΒΆ
NBFitResult.ql_dispersions: ndarray | None = NoneΒΆ
NBFitResult.beta: ndarrayΒΆ
NBFitResult.overdispersions: ndarrayΒΆ
NBFitResult.mu: ndarrayΒΆ
NBFitResult.size_factors: ndarrayΒΆ
NBFitResult.deviances: ndarrayΒΆ
NBFitResult.design_matrix: ndarrayΒΆ
NBFitResult.design_column_names: list[str]ΒΆ
NBFitResult.feature_names: IndexΒΆ
NBFitResult.converged: ndarrayΒΆ

MethodsΒΆ