splicejac.tools.aux_functions
auxiliary functions for spliceJAC inference and analysis
Module Contents
Functions
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Run regression to infer spliced-unspliced interaction coefficients |
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Estimate degradation rate coefficient vector |
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Construct a Jacobian matrix given the gene-gene interactions and degradation rates |
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Set up a axes name list with unspliced and spliced genes |
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Construct an instability score for each gene in each cluster by looking at eigenvector components in the cluster |
- splicejac.tools.aux_functions.parameter_regression(U_data, S_data, method='Ridge', alpha=1, fit_int=True)
Run regression to infer spliced-unspliced interaction coefficients
- U_data: ~numpy.ndarray
count matrix of unspliced counts
- S_data: ~numpy.ndarray
count matrix of spliced counts
- method: str (default: Ridge)
regression method, choose between Linear, Ridge or Lasso
- alpha: float (default: 1)
regularization coefficient for Ridge and Lasso
- fit_int: Bool (default: True)
if True, set the fit_intercept parameter to True
- mat: ~numpy.ndarray
gene-gene interaction matrix
- interc: ~numpy.ndarray
intercept vector
- degr: ~numpy.ndarray
degradation coefficient vector
- splicejac.tools.aux_functions.estimate_degr(adata, first_moment=True)
Estimate degradation rate coefficient vector
- adata: ~anndata.AnnData
count matrix
- first_moment: Bool (default: True)
if True, use first moment of U and S to run regression
- degr: ~numpy.ndarray
degradation coefficient vector
- splicejac.tools.aux_functions.construct_jac(mat, degr, b=1)
Construct a Jacobian matrix given the gene-gene interactions and degradation rates
- mat: ~numpy.ndarray
matrix of gene-gene interactions
- degr: ~numpy.ndarray
degradation coefficient vector
- b: float (default: 1)
splicing rate constant
J: Jacobian matrix
- splicejac.tools.aux_functions.set_gene_axes(adata)
Set up a axes name list with unspliced and spliced genes
- adata: ~anndata.AnnData
count matrix
None
- splicejac.tools.aux_functions.instability_scores(adata)
Construct an instability score for each gene in each cluster by looking at eigenvector components in the cluster unstable manifold. Results are saved in adata.uns[‘inst_scores’]
- adata: ~anndata.AnnData
count matrix
None