Comments (1)
Thank you for your enquiry.
To compute the prediction values, the function evaluate_target_prediction_strict
calls classification_evaluation_continuous_pred
using the response and prediction vectors as inputs.
Inside classification_evaluation_continuous_pred
you will find all the functions that are called to compute the AUPR and correlation scores. In this way, you should be able to reconstruct all the steps and inputs used to prioritize EFNA1 and CDH1.
from nichenetr.
Related Issues (20)
- Error in `generate_info_tables`
- Warning message in `predict_ligand_activities`
- `generate_prioritization_tables` warnings and documentation
- Error when passing the recorrect_umi argument in get_lfc_celltype HOT 1
- Error in WhichCells.Seurat(object = object, idents = ident.2) : Cannot find the following identities in the object: Adjacent HOT 1
- Error in `Idents<-`: ! 'value' must be a factor or vector HOT 3
- Different results running. the same. code in different version. of HOT 6
- Protein complex HOT 1
- Function generate_info_tables return an error HOT 3
- Low AUPR values in analyses HOT 4
- RankActiveLigands Error
- Can NicheNet be used for analyzing three groups? HOT 1
- Parallelization error when optimizing parameters for NicheNet HOT 4
- How is the Ligand-Target-Matrix generated? HOT 2
- Receiver cells in differential analysis HOT 1
- when sender cell type is only one, HOT 1
- Only activating interactions or also repressing? HOT 1
- Naming convention in prioritization table
- Change error in `alias_to_symbol_seurat()` to warning
- discrepancy in results output HOT 2
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from nichenetr.