Transcriptomic entropy benchmarks stem cell-derived cardiomyocyte maturation against endogenous tissue at single cell level
Fig 2
Gene filtration facilitates cross-study comparisons of entropy.
A. Correction of mismapped mitochondrial reads. The use of a genomic mapping algorithm (such as zUMIs) or a “full” reference containing pseudogenes can lead to erroneous mapping of mitochondrial genes to pseudogenes, in turn inflating entropy score. We included a correction step that facilitates usage of data from a range of mapping pipelines. B. Proportion of ribosomal protein coding genes in mouse in vivo datasets, grouped by library preparation method. Given the clear protocol-dependence of these genes, we eliminated them from analysis. C. Entropy at different gene subsamplings for two studies with different sensitivities. Data from e18 and p22 from the maturation reference and p21 from Murphy et al. are shown. D. Spearman correlation between entropy and timepoint for different gene subsamplings (using median of entropy for each timepoint and study). E. Normalized variance of entropy for different gene subsamplings (using median of entropy for each timepoint). We normalized by scaling entropy at every subsampling to [0, 1].