Fig. 1

Integration and expression reconstruction of single-cell sequencing data. A DISCERN transfers the style of a high-quality (hq) dataset to a related low-quality (lq) dataset, enabling gene expression reconstruction that results in improved clustering, cell type identification, marker gene detection, and mechanistic insights into cell function. The hq and lq datasets have to be related but not identical, containing for example several overlapping cell types but also exclusive cell types of cell activity states for one or the other dataset. B t-SNE visualization of the pancreas dataset before reconstruction (original) and after transferring the style of the smartseq2 dataset using DISCERN (p-smartseq2). The upper row shows the dataset of origin before and after reconstruction colored by batch and the lower row colored by cell type annotation (details of 13 cell types in supplements). C and D Average gene expression (over all the cells of a given type) of the pancreas indrop and smartseq2 datasets before (first column and panel) and after smartseq2 to indrop (second column and panel) and after indrop to smartseq2 reconstruction (third column and panel). C Gene correlation by cell type shown in colored heatmap. D Each colored point represents a single gene colored by the cell type. The mean Pearson correlation with one standard deviation over all cell types is shown in the figure title