Assessment of dispersion metrics for estimating single-cell transcriptional variability
Fig 6
Change in VMR has a negative correlation with mean and fraction of expressing cells and varies between platforms.
In a Drop-seq neuron dataset, change in VMR inversely correlates with (A) the fraction of expressing cells and (B) change in mean expression. (C) Neurod6, a DEG, is the gene in this dataset with the largest absolute change in VMR between E11.5 and E17.5, whereas Stmn2, a non-DEG, also had a large absolute change in VMR between timepoints. In the 10x Genomics cardiomyocyte dataset comparing control and matHG conditions at E9.5, associations between VMR and UMI counts across cells behave similarly (D, E). Panel (F) shows Hbb-bt, a DEG with the largest absolute change in VMR at E9.5 between control and matHG. Igfbp5 had the 10th largest absolute change in VMR between conditions, and is not a DEG. A negative correlation between VMR changes and UMI distribution is again evident in the 10x Genomics dataset comparing WT and T21 cardiomyocytes (G, H). Panel (I) shows Thbs1, a gene with the largest absolute change in VMR between WT and T21 conditions. Col2a1 had the 10th largest absolute change in VMR between conditions, and had the largest fraction of expressing cells among the top 10 genes with largest change in VMR. Neither Thbs1 nor Col2a1 are DEGs.