splicejac.tools
__init__ file for the tools library
Submodules
Package Contents
Functions
|
Run cluster-wise Jacobian inference |
|
Compute the gene instability scores for all transitions identified with PAGA |
|
Compute the gene instability scores for transition from cluster1 to cluster2 |
functions to quantify grn similarity across cell states |
|
|
Compare methods for gene-gene interaction parameter regression |
test the inference sensitivity to cell subsampling |
|
|
Computes various statistics on the cell state specific GRNs. The statistics are added to adata.uns['GRN_statistics'] |
|
Export the GRN of a cell state to a csv file |
|
Export gene transition scores for all transitions in a csv file |
- splicejac.tools.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
- splicejac.tools.trans_from_PAGA(adata, dir_method='top_eig', eig_number=5, top_DEG=5, top_TG=5, first_moment=True)
Compute the gene instability scores for all transitions identified with PAGA PAGA transitions must be stored as a dataframe in adata.uns[‘PAGA_paths’] results are stored in adata.uns[‘transitions’]
- adata: ~anndata.AnnData
count matrix
- dir_method: str (default: ‘top_eig’)
method to select the unstable directions, choose between ‘top_eig’ and ‘positive’. ‘top_eig’ uses the largest eigenvalues irrespective of sign; ‘positive’ strictly uses positive eigenvalues
- eig_number: int (default: 5)
number of largest eigenvalues to consider, required for dir_method=’top_eig’
- top_DEG: int (default: 5)
number of top DEG to select
- top_TG: int (default: 5)
number of top TG to select
- first_moment: Bool (default: True)
if True, use first moments of unspliced/spliced counts
None
- splicejac.tools.transition_genes(adata, cluster1, cluster2, dir_method='top_eig', eig_number=5, top_DEG=5, top_TG=5, first_moment=True)
Compute the gene instability scores for transition from cluster1 to cluster2 Results are stored in adata.uns[‘transitions’]
- adata: ~anndata.AnnData
count matrix
- cluster1: str
starting cell state
- cluster2: str
final cell states
- dir_method: str (default: ‘top_eig’)
method to select the unstable directions, choose between ‘top_eig’ and ‘positive’. ‘top_eig’ uses the largest eigenvalues irrespective of sign; ‘positive’ strictly uses positive eigenvalues
- eig_number: int (default: 5)
number of largest eigenvalues to consider, required for dir_method=’top_eig’
- top_DEG: int (default: 5)
number of top DEG to select
- top_TG: int (default: 5)
number of top TG to select
- first_moment: Bool (default: True)
if True, use first moments of unspliced/spliced counts
None
- splicejac.tools.grn_comparison(adata)
Compute AUROC/AUPRC scores for all pairs of state-specific gene regulatory networks Results are stored in adata.uns[‘comparison_scores’]
- adata: ~anndata.AnnData
count matrix
None
- splicejac.tools.regr_method_sens(adata, alpha_ridge=np.array([0.01, 0.1, 1, 10, 100]), alpha_lasso=np.array([0.0001, 0.001, 0.01, 0.1, 1]))
Compare methods for gene-gene interaction parameter regression Results are stored in adata.uns[‘method_sens’] and adata.uns[‘sens_coeff’]
- adata: ~anndata.AnnData
count matrix
- alpha_ridge: ~numpy.ndarray (default: numpy.array([0.01, 0.1, 1, 10, 100]))
array of shrinkage coefficients to test for Ridge regression
- alpha_lasso: ~numpy.ndarray (default: numpy.array([0.0001, 0.001, 0.01, 0.1, 1]))
array of shrinkage coefficients to test for Lasso regression
None
- splicejac.tools.subsampling_sens(adata, frac=np.arange(0.1, 0.91, 0.1), seed=100, nsim=10)
Test the inference of gene-gene interaction matrix as a function of fraction of selected cells Results are stored in adata.uns[‘sens_summary’]
- adata: ~anndata.AnnData
count matrix
- frac: ~numpy.ndarray (default: numpy.arange(0.1, 0.91, 0.1))
fraction of cells to randomly select
- seed: int (default=100)
seed for random cell selection for reproducibility
- nsim: int (default: 10)
number of independent simulations
None
- splicejac.tools.grn_statistics(adata, weight_quantile=0.5, k=None, normalized=True, weight=None, endpoints=False, seed=None)
Computes various statistics on the cell state specific GRNs. The statistics are added to adata.uns[‘GRN_statistics’]
For a more detailed discussion of several of the parameters, please see the betweenness_centrality from Networkx (https://networkx.org/documentation/networkx-1.10/reference/generated/networkx.algorithms.centrality.betweenness_centrality.html) Results are stored in adata.uns[‘GRN_statistics’], adata.uns[‘cluster_variation’] and adata.uns[‘cluster_average’]
- adata: ~anndata.AnnData
count matrix
- weight_quantile: float (default: 0.5)
cutoff for weak gene-gene interactions between 0 and 1
- k: int or None (default=`None`)
number of nodes considered to compute betweenness centrality. k=None implies that all edges are used
- normalized: Bool (default: True)
if True, betweenness centrality values are normalized
- weight: str (default: None)
If None, all edge weights are considered equal
- endpoints: Bool (default: False)
If True, include the endpoints in the shortest path counts during betweenness centrality calculation
- seed: int (default: None)
seed for betweenness centrality calculation
None
- splicejac.tools.export_grn(adata, cluster, filename=None)
Export the GRN of a cell state to a csv file
If no filename is provided, a local folder results/exported_results/ will be created (if not existing) and the GRN file will be saved with default name “grn” + cluster + “.csv”
- adata: ~anndata.AnnData
count matrix
- cluster: str
cell state
- filename: str (default: None)
the name of export file, including path and .csv extension
None
- splicejac.tools.export_transition_scores(adata, filename=None)
Export gene transition scores for all transitions in a csv file
If no filename is provided, a local folder results/exported_results/ will be created (if not existing) and the GRN file will be saved with default name “grn” + cluster + “.csv”
- adata: ~anndata.AnnData
count matrix
- filename: str (defaul: None)
the name of export file, including path and .csv extension
None