Thresholding the guided cell-type map

After cell-type signatures are provided, the tissue image can be classified. The classification of each pixel is based on the Pearson correlation metric (although an experimental adversarial autoencoder based classification method can be applied).

We found that a minimum correlation threshold (min_r) of 0.3 worked well for guided mode based on single cell RNAseq cell-type signatures, and 0.6 worked well for de novo mode.

Below we show how the cell-type map changes using correlation thresholds of 0.15,0.3,0.45 using the scRNAseq signatures

scrna_uniq_labels = [scrna_cl_metadata_dic[i][0] for i in scrna_uniq_clusters]
scrna_colors = [scrna_cl_metadata_dic[i][1] for i in scrna_uniq_clusters]

analysis.map_celltypes(scrna_centroids)

analysis.filter_celltypemaps(min_norm=filter_method, filter_params=filter_params, min_r=0.15, output_mask=output_mask) # post-filter cell-
plt.figure(figsize=[5, 5])
ds.plot_celltypes_map(rotate=1, colors=scrna_colors, set_alpha=False)

analysis.filter_celltypemaps(min_norm=filter_method, filter_params=filter_params, min_r=0.3, output_mask=output_mask) # post-filter cell-
plt.figure(figsize=[5, 5])
ds.plot_celltypes_map(rotate=1, colors=scrna_colors, set_alpha=False)

analysis.filter_celltypemaps(min_norm=filter_method, filter_params=filter_params, min_r=0.45, output_mask=output_mask) # post-filter cell-
plt.figure(figsize=[5, 5])
ds.plot_celltypes_map(rotate=1, colors=scrna_colors, set_alpha=False)