Fig. 1

Overview of TREG motivation and methodology. a Illustration of the relationship between the expression of a TREG and the total RNA expression of a nucleus. TREG expression can be quantified with puncta (white dots) in a nucleus (blue area), where the nucleus is identified with DAPI. b Illustration of the distribution of expression rank, which is the rank of the expression of a given gene among all genes, computed individually for each cell/nucleus, depending on the measurement technology used: sc or snRNA-seq. Two theoretical genes are shown: gene 1 with high rank invariance and gene 2 with low rank invariance across cells/nuclei. c Rank invariance workflow to identify a TREG (the “Rank invariance calculation” section), with a gene expression matrix with genes on the rows and cells/nuclei on the columns. (i) Filter for low-expressed genes (the “Expression and proportion zero filtering” section). Onward working with one cell type at a time; (ii) compute expression rank of each cell/nucleus for each gene (example distribution in b); (iii) calculate the mean gene expression across all cells/nuclei for one cell type and then its rank expression; (iv) per gene, find the difference of the rank expression against the mean rank expression for each cell/nucleus in a given cell type; (v) calculate the mean of the absolute expression rank differences for each gene; (vi) rank the mean absolute expression rank differences; (vii) repeat steps ii–vi for each cell type; (viii) per gene, compute the sum of the previous ranks across all cell types and then rank these sums across genes such that the highest rank is given to the gene with the smallest sum. This is the final rank invariance value