splicejac.tools.grn_comparison
functions to quantify grn similarity across cell states
Module Contents
Functions
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Compute AUROC/AUPRC related metrics between matrices m1 and m2 disregarding the interaction signs |
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Compute AUROC/AUPRC related metrics between matrices m1 and m2 when considering the interaction signs |
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Compute AUROC/AUPRC scores for all pairs of state-specific gene regulatory networks |
- splicejac.tools.grn_comparison.edge_detection_score(m1, m2)
Compute AUROC/AUPRC related metrics between matrices m1 and m2 disregarding the interaction signs
- m1: ~numpy.ndarray
observation matrix
- m2: ~numpy.ndarray
ground truth matrix
fpr: false positive rate tpr: true positive rate auroc: area under the receiver characteristic curve precision: precision recall: recall auprc: area under the precision recall curve
- splicejac.tools.grn_comparison.sign_detection_score(m1, m2)
Compute AUROC/AUPRC related metrics between matrices m1 and m2 when considering the interaction signs
m1: observation matrix m2: ground truth matrix
- fpr: float
false positive rate
- tpr: float
true positive rate
- auroc: float
area under the receiver characteristic curve
- precision: float
precision
- recall: float
recall
- auprc: float
area under the precision recall curve
- splicejac.tools.grn_comparison.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