While we believe that the guided mode of SSAM to be able to generate good cell-type maps rapidly, the de novo mode provide much more accurate results.
The steps of the de novo analysis are briefly discussed below, with links to more detailed discussion:
- setting cell-type map correlation threshold
- visualisation of cell-type signatures: heatmap, tSNE, UMAP
Once the local maxima have been selected and filtered, we can perform clustering analysis. SSAM supports a number of clustering methods. Here we use the Louvain algorithm using 22 principle components, a resolution of 0.15.
analysis.cluster_vectors( min_cluster_size=0, pca_dims=22, resolution=0.15, metric='correlation')
SSAM provides diagnostic plots which can be used to evaluate the quality of clusters, and facilitates the annotation of clusters.
SSAM supports cluster visualisation via heatmaps, and 2D embedding (t-SNE and UMAP). Here we give an example of the t-SNE plot:
plt.figure(figsize=[5, 5]) ds.plot_tsne(pca_dims=22, metric="correlation", s=5, run_tsne=True) plt.savefig('images/tsne.png')
Once the clusters have been evaluated for quality, we can generate the
de novo cell-type map. This involves classifying all the pixels in
the tissue image based on a correlation
threshold. For the de novo application
0.6
was found to perform well:
analysis.map_celltypes() filter_params = { "block_size": 151, "method": "mean", "mode": "constant", "offset": 0.2 } analysis.filter_celltypemaps(min_norm="local", filter_params=filter_params, min_r=0.6, fill_blobs=True, min_blob_area=50, output_mask=output_mask)
plt.figure(figsize=[5, 5]) ds.plot_celltypes_map(rotate=1, set_alpha=False) plt.axis('off') plt.savefig('images/de_novo.png')
We can now use our celltype map to infer a map of tissue domains.