Skip to main content
Fig. 3 | Genome Biology

Fig. 3

From: Exploiting single-cell expression to characterize co-expression replicability

Fig. 3

Comparative network analysis shows higher functional connectivity, semantic similarity, and convergent co-expression of UMI-based aggregates. a For each batch network, functional connectivity was benchmarked against 108 GO slim categories then networks were randomly selected and aggregated ten times. Networks built from raw UMI data are shown in black and count-per-million (CPM) standardized data are shown in red. As in our meta-analysis of single-cell networks, performance rises with aggregation, indicating an overlap in functional signal among networks. CPM networks have significantly lower functional connectivity than UMI networks (mean UMI = 0.54 +/– 0.002, mean CPM + 0.54 +/– 0.001, p <0.05 Wilcoxon rank sum, n = 8). Inset: Boxplot of synaptic gene performance for UMI and CPM networks. Though mean GO slim performance is modest, connectivity of this functionally relevant gene set is high (mean UMI = 0.73 +/– 0.004, mean CPM = 0.69 +/– 0005). b Semantic similarity of top 1 % network connections assessed by the number of shared GO functions. The red line indicates mean semantic similarity of all genes. Lower semantic similarity is observed for CPM, removal of unwanted variation (RUV), and binary expression aggregates compared to UMI-based networks. c Plot shows pairwise comparisons of top 1 % network connections based on the Jaccard index. UMI-based networks are more similar to one another than to CPM, RUV, and binary expression networks. d Standard deviation of aggregate co-expression values, red line marks the amount of variance expected by chance. All aggregates are more variable than random, indicating the presence of replicable co-expression among individual networks

Back to article page