ingest confidence thresholding

What kind of feature would you like to request?

Additional function parameters / changed functionality / changed defaults?

Please describe your wishes

First thank you for the amazing tool you guys developed

I am currently trying to use the ingest function to map cluster identities from a single cell dataset to a spatial data set.
the single cell is composed from a subset of cell types while the spatial has all of the cells. The single cell has many genes while the spatial has a few.
I parse out the common genes from from both datasets and use those to run the ingest function which works quiet well!

common_genes = sc_data.var_names.intersection(spatial_data.var_names)
sc_data = sc_data[:, common_genes]
spatial_data = spatial_data[:, common_genes]
sc.tl.pca(sc_data, svd_solver='arpack')
sc.pp.neighbors(sc_data, use_rep='X_pca')
sc.tl.umap(sc_data)
sc.tl.pca(spatial_data, svd_solver='arpack')
sc.tl.ingest(spatial_data, sc_data, obs='unified_clusters')

The issue I am running into is that the ingest function forces an identity onto cells even though the confidence of that identity is probably very low.

I am getting around that, in part, by subsetting the spatial dataset, but it would be terrific if I could use a confidence parameter to specify which cells would get an identity at all.

I was playing around with the ingest scripts and was thinking of something like this

    def map_labels(self, labels: str, method: str, confidence_threshold: float = 0.5):
 
        if method == 'knn':
            self.neighbors()
            cat_array: pd.Series = self._adata_ref.obs[labels].astype("category")
            
            confident_labels = []
            for inds in self._indices:
                mode_label = cat_array.iloc[inds].mode()[0]
                mode_count = (cat_array.iloc[inds] == mode_label).sum()
                
                confidence = mode_count / len(inds)
                
                if confidence >= confidence_threshold:
                    confident_labels.append(mode_label)
                else:
                    confident_labels.append('unassigned')
            
            self._adata_new.obs[labels] = pd.Categorical(values=confident_labels, categories=cat_array.cat.categories)
        else:
            raise NotImplementedError("Ingest supports knn labeling for now.")

perhaps I am misunderstanding the tool, or unaware of another tool which exists for my purpose, and would love input and help

Start by reading the ingest function's label-mapping path and the proposed map_labels implementation in the issue. Clarify how confidence should be calculated and represented for unassigned cells, then verify that the threshold behavior is covered for the supported knn method.