Clusters were arranged by transcriptomic similarity based on hierarchical clustering. First, the average expression level of each gene was calculated for each cluster. Genes were then sorted based on variance and the top 2000 genes were used to calculate a correlation-based distance matrix, Dxy=1-(cor(x,y))/2, between each cluster average. A cluster tree was generated by performing hierarchical clustering on this distance matrix (using “hclust” with default parameters), and then reordered to show inhibitory clusters first, followed by excitatory clusters and glia, with larger clusters first, while respecting the tree structure. Note that this measure of cluster similarity is complementary to the co-clustering similarity described above. For example, two clusters with high transcriptomic similarity but a few distinct marker genes may have low co-clustering similarity.
Cluster matching
- Threshold score to pick marker genes - correlated expression - reciprocal best match
- Cluster annotation with Tasic et al clusters
- Select best markers of paired clusters
Estimating nuclear proportions
- Intronic read ratio
- Top 3 gene expression ratio
- Compare with variances - propagation of error
- Individual gene estimates
- Comparison with Halpern 2015