Comments (1)
Simplest, effective, loss function is lambda_g1 = 1
and all other parameters equal to zero. This is what we end up using mostly (although historically we were also adding a lambda_g2 = 1
term). All other parameters, are sensible choices that can help mapping under various circumstances. To figure out what is the best loss function for you, always use the AUC score from the test transcriptome (that's the actual validation).
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Related Issues (20)
- Can we have an API as batch size for the integration method? HOT 3
- No attribute 'pp_adatas' in tangram HOT 2
- Possible to use multiple single cell datasets in Tangram? HOT 3
- Cannot find correspondence of the input data HOT 1
- question about training genes HOT 1
- Some questions about best practices HOT 1
- Attribute error : module 'tangram' has no attribute 'map_cells_to_space' HOT 2
- scRNAseq cells < spatial cells, curious about how mapping works HOT 3
- Unexpected behaviour HOT 1
- Question about acceptable AUC, improving AUC HOT 1
- potential overfitting HOT 5
- Interpretation of tangram_ct_pred HOT 2
- Option "enforce gene lowercase" HOT 1
- error when sq.im.segment
- Tangram Deconvolution HOT 1
- Using Integrated single cell data for alignment
- AttributeError: module 'tangram' has no attribute 'pp_adatas'
- Sparsity_sc and sparsity_sp = 0
- Please explain `project_cell_annotations`
- Projecting spatial annotations to single cell HOT 2
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