Diagnostic plots¶
After unsupervised clustering of gene expression vectors, some clusters may need to be manually merged or discarded. SSAM supports merging of clusters based on correlation of gene expression profile, however in many cases manual inspection is needed to rule out any non-trivial issues.
To guide this process, SSAM generates a cluster-wise ‘diagnostic plot’, which consists of four panels: 1) location of the clustered vectors on the tissue image, 2) the pixels classified to belong the cluster signature (the cluster centroid), 3) the mean expression profile of the clustered vectors, and 4) the t-SNE or UMAP embedding.
In the three datasets analyzed the clusters to be merged or removed often showed a discordance between the location of sampled vectors used to determine the cluster (panel 1) and the pixels classified to belong to that cluster (panel 2). In case of overclustering, i.e. when a cell-type signature is split over 2 clusters, the map typically does not classify the full shape of the cells but instead only fragments (panel 2), and having almost the same marker gene expression of another cluster (panel 3). Such clusters can be merged.
For dubious clusters that should be removed, we observed that vectors usually originate from outside the tissue region or from image artifacts (panel 1), or that the gene expression does not show any clear expression of marker genes or similarity to expected gene expression profiles (panel 3).
The remaining clusters are then annotated by comparing cluster marker genes to known cell-type markers. Note that in many cases, the identity of clusters can be easily assigned by comparing the centroids of the clusters to the known cell-type signatures, e.g., from single cell RNA sequencing.
To support rapid annotation of cell types to clusters, SSAM additionally shows the highest correlating known cell-type signature should this data be available in panel 3.
Example 1: a large cluster that can be easily annotated¶
Local maxima (panel 1), correspond to the same area (panel 2), and matches known gene expression patterns of Vip Arhgap36 Hmcn1 cell types from scRNAseq experiments with high correlation (panel 3)
Example 2: a large cluster that cannot be easily annotated¶
Local maxima (panel 1), correspond to the same area (panel 2). The gene expression profile has a good correlation to L2/3 IT VISp Adamts2 cell types, but are lacking the very high expression of Pde1a. In this particular case, one would need to check other clusters matching this cell type and perhaps merge them, or perhaps this indicates low efficiency of the Pde1a probe in the experiment.
Example 3: a small cluster that is good¶
Despite only 2 local maxima (panel 1), the classified pixels correspond to the same area (panel 2), and matches known gene expression patterns (panel 3). This presents a very rare, SSt Chodl cell type.
Example 4: a small cluster that is questionable¶
Sampled local maxima (panel 1) to no correspond to the classified pixels (panel 2), and doesnt clearly match known gene expression patterns (panel 3)
