splicejac.tools.estimate_jacobian
tools for jacobian inference
Module Contents
Functions
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Check that the selected number of genes (n_top_genes) does not exceed the number of observables |
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Rescale count of each gene to a zero mean |
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Run a single cluster-wise regression using all cells in each cluster |
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Run cluster-wise Jacobian regression multiple times with a randomly-selected subset of cells |
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Compute average Jacobian matrix from long_regression() results |
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Run cluster-wise Jacobian inference |
- splicejac.tools.estimate_jacobian.initial_check(adata, frac, n_top_genes)
Check that the selected number of genes (n_top_genes) does not exceed the number of observables
The maximum value admitted for the n_top_genes variable to ensure a unique solution is given by the number of cells in the smaller cluster times the fraction of cells per cluster used during each iteration of inference (frac)
- adata: ~anndata.AnnData
count matrix
- frac: float
fraction of cell selected from each cluster for bootstrapping inference
- n_top_genes: int
number of top genes to keep when running spliceJAC
None
- splicejac.tools.estimate_jacobian.rescale_counts(mat)
Rescale count of each gene to a zero mean
- mat: ~numpy.ndarray
count matrix
- rescaled: ~numpy.ndarray
rescaled count matrix
- splicejac.tools.estimate_jacobian.quick_regression(adata, first_moment=True, method='Ridge', alpha=1, beta=1.0, rescale=True)
Run a single cluster-wise regression using all cells in each cluster Results are saved in adata.uns[‘all_cells’]
- adata: ~anndata.AnnData
count matrix
- first_moment: Bool (default: True)
if True, use first moment of U and S to run regression
- method: str (default: Ridge)
regression method, choose between Linear, Ridge or Lasso
- alpha: float (default: 1)
regularization coefficient for Ridge and Lasso
- beta: float (default: 1)
mRNA splicing rate constant
- rescale: Bool (default: True)
if True, center counts on zero (default= True). rescale=True enforces fit_int=False
None
- splicejac.tools.estimate_jacobian.long_regression(adata, first_moment=True, method='Ridge', alpha=1, beta=1.0, rescale=True, nsim=10, frac=0.9)
Run cluster-wise Jacobian regression multiple times with a randomly-selected subset of cells results are saved in adata.uns[‘jacobian_lists’]
- adata: ~anndata.AnnData
count matrix
- first_moment: Bool (default: True)
if True, use first moment of U and S to run regression
- method: str (default: Ridge)
regression method, choose between Linear, Ridge or Lasso
- alpha: float (default: 1)
regularization coefficient for Ridge and Lasso
- beta: float (default: 1)
mRNA splicing rate constant
- rescale: Bool (default: True)
if True, center counts on zero (default= True). rescale=True enforces fit_int=False
- nsim: int (default: 10)
number of independent regressions per cluster
- frac: float (default: 0.9)
fraction of cells to randomly select (bound in [0,1])
None
- splicejac.tools.estimate_jacobian.compute_avg_jac(adata, eps=0.9)
Compute average Jacobian matrix from long_regression() results The percentage of smallest Jacobian elements (by absolute value) are set to zero based on the parameter epsilon
- adata: ~anndata.AnnData
count matrix
- eps: float (default= 0.9)
fraction of weakest Jacobian elements that are set to zero (by absolute value)
None
- splicejac.tools.estimate_jacobian.estimate_jacobian(adata, first_moment=True, method='Ridge', alpha=1, beta=1.0, rescale=True, nsim=10, frac=0.9, filter_and_norm=True, min_shared_counts=20, n_top_genes=20, eps=0.9, seed=100)
Run cluster-wise Jacobian inference
- adata: ~anndata.AnnData
count matrix
- first_moment: Bool (default: True)
if True, use first moment of U and S to run regression
- method: str (default: Ridge)
regression method, choose between Linear, Ridge or Lasso
- alpha: float (default: 1)
regularization coefficient for Ridge and Lasso
- beta: float (default: 1)
mRNA splicing rate constant
- rescale: Bool (default: True)
if True, center counts on zero (default= True). rescale=True enforces fit_int=False
- nsim: int (default: 10)
number of independent regressions per cluster
- frac: float (default: 0.9)
fraction of cells to randomly select (bound in [0,1])
- filter_and_norm: Bool (default: True)
if True, apply scvelo filter_and_normalize function to the count matrix
- min_shared_count: int (default: 20)
minimum number of shared count for the scvelo filter_and_normalize function
- n_top_genes: int (default: 20)
number of top genes for the scvelo filter_and_normalize function
- eps: float (default= 0.9)
fraction of weakest Jacobian elements that are set to zero (by absolute value)
- seed: int (default: 100)
seed for numpy random number generator (for reproducibility)
None