Genes

Genome-wide distributions of expression fidelity for astrocytes (A), oligodendrocytes (O), microglia (M), and neurons (N) over all analyzed samples are shown. The horizontal line denotes the expression fidelity of the query gene for each cell type. The dashed horizontal line (only visible when analyzing genes over ‘All’ regions) denotes the threshold above which all fidelity scores had Confidence = 100 (see panel B).

This table provides information on the data used to produce the distributions in panel (A):

• Datasets: the total number of human regional CNS datasets that contributed to the expression fidelity estimates for each cell type.
• Samples: the total number of human CNS samples that contributed to the expression fidelity estimates for each cell type.
• Fidelity: a z-score that quantifies the extent to which the expression of the query gene was correlated with the inferred abundance of each cell type over all analyzed samples.
• Rank: the genome-wide rank of the expression fidelity z-score (currently the database contains information for ~18,500 genes).
• Confidence: the percentage of genes with expression fidelity scores higher than the query gene that have the highest expression fidelity for that cell type.
• Mean cor: the aggregate Pearson correlation coefficient between the expression pattern of the query gene and the inferred abundance of each cell type over all analyzed samples.

Mean expression percentile ranks of the query gene in all analyzed datasets. Shapes and colors of points denote technology platforms and CNS regions, respectively.

Modeling results for expression levels of the query gene as a function of variation in the inferred abundance of individual cell types. Simple linear regression is used to predict gene expression levels as a function of estimated neuron, astrocyte, oligodendrocyte, or microglia abundance in all human CNS regional datasets containing the query gene. Adjusted r² values and t-values are shown for each cell type (labeled by color, with shapes denoting technology platforms).

Modeling results for expression levels of the query gene as a function of variation in the inferred abundance of all cell types. Multiple linear regression is used to predict gene expression levels as a function of estimated neuron, astrocyte, oligodendrocyte, and microglia abundance in all human CNS regional datasets containing the query gene. Adjusted r² values and F-statistics are shown (with colors and shapes denoting CNS regions and technology platforms, respectively).

The top 12 genes (ordered by columns) ranked by their aggregate correlation to the query gene’s expression levels over all analyzed samples.