pydeseq2.grid_search
Functions
|
Find best dispersion parameter. |
|
Find best LFC parameter. |
|
Find best LFC parameter. |
|
Return the negative log-likelihood of a negative binomial. |
- grid_fit_alpha(counts, design_matrix, mu, alpha_hat, min_disp, max_disp, prior_disp_var=None, cr_reg=True, prior_reg=False, grid_length=100)
Find best dispersion parameter.
Performs 1D grid search to maximize negative binomial log-likelihood.
- Parameters:
counts (
ndarray
) – Raw counts for a given gene.design_matrix (
ndarray
) – Design matrix.mu (
ndarray
) – Mean estimation for the NB model.alpha_hat (
float
) – Initial dispersion estimate.min_disp (
float
) – Lower threshold for dispersion parameters.max_disp (
float
) – Upper threshold for dispersion parameters.prior_disp_var (
float
) – Prior dispersion variance.cr_reg (
bool
) – Whether to use Cox-Reid regularization. (default:True
).prior_reg (
bool
) – Whether to use prior log-residual regularization. (default:False
).grid_length (
int
) – Number of grid points. (default:100
).
- Returns:
Logarithm of the fitted dispersion parameter.
- Return type:
- grid_fit_beta(counts, size_factors, design_matrix, disp, min_mu=0.5, grid_length=60, min_beta=-30, max_beta=30)
Find best LFC parameter.
Perform 2D grid search to maximize negative binomial GLM log-likelihood w.r.t. LFCs.
- Parameters:
counts (
ndarray
) – Raw counts for a given gene.size_factors (
ndarray
) – DESeq2 normalization factors.design_matrix (
ndarray
) – Design matrix.disp (
float
) – Gene-wise dispersion prior.min_mu (
float
) – Lower threshold for dispersion parameters.grid_length (
int
) – Number of grid points. (default:100
).min_beta (
float
) – Lower-bound on LFC. (default:30
).max_beta (
float
) – Upper-bound on LFC. (default:30
).
- Returns:
Fitted LFC parameter.
- Return type:
ndarray
- grid_fit_shrink_beta(counts, offset, design_matrix, size, prior_no_shrink_scale, prior_scale, scale_cnst, grid_length=60, min_beta=-30, max_beta=30)
Find best LFC parameter.
Performs 2D grid search to maximize MAP negative binomial GLM log-likelihood w.r.t. LFCs, with apeGLM prior.
- Parameters:
counts (
ndarray
) – Raw counts for a given gene.offset (
ndarray
) – Natural logarithm of size factor.design_matrix (
ndarray
) – Design matrix.size (
ndarray
) – Size parameter of NB family (inverse of dispersion).prior_no_shrink_scale (
float
) – Prior variance for the intercept.prior_scale (
float
) – Prior variance for the LFC coefficient.scale_cnst (
float
) – Scaling factor for the optimization.grid_length (
int
) – Number of grid points. (default:100
).min_beta (
int
) – Lower-bound on LFC. (default:30
).max_beta (
int
) – Upper-bound on LFC. (default:30
).
- Returns:
Fitted MAP LFC parameter.
- Return type:
ndarray
- vec_nb_nll(counts, mu, alpha)
Return the negative log-likelihood of a negative binomial.
Vectorized version.
- Parameters:
counts (
ndarray
) – Observations.mu (
ndarray
) – Mean of the distribution.alpha (
ndarray
) – Dispersion of the distribution, s.t. the variance is \(\mu + \alpha \mu^2\).
- Returns:
Negative log likelihood of the observations counts following \(NB(\mu, \alpha)\).
- Return type:
ndarray