Comments (1)
Hejin0701
commented on June 19, 2024
Hi Joy,
Yes. You should always train the model for new datasets. For your analysis, when you get a new single cell data and Visium data, you should first identify the marker genes. Then just follow the same steps in our tutorial but replace the single cell and ST data with your data (arranged in Anndata format). After that, use the map_cell_to_space function to train the model to get the mapping result.
Let me know whether that helps.
Best,
Hejin
from tangram.
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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