Fig 1: InTraSeq generates high-quality single-cell RNA and protein profiles in the same cellsA. InTraSeq workflow.B. Experiment design of the benchmarking analysis.C. Distribution of summary statistics of the gene expression assay comparing the Live, InTraSeq RNA-only and InTraSeq RNA+ADT samples. To exclude impacts of the differences in the sequencing depth and the number of cells captured, only 7,000 cells with largest total number of reads were included in the comparison, and the samples were down-sampled to match the median number of reads per cell in the sample with lowest coverage (Methods).D. UMAP plots depicting the cell types identified in the Live, InTraSeq RNA-only and InTraSeq RNA+ADT samples. UMAP coordinates were computed based on RNA expression data after integration. Colors indicate the cell types.E. RNA expression of cluster marker genes in each sample. Size of dots reflects percent of cells with non-zero UMIs, and the color represents average expression of genes that were z-normalized across cell types in each sample.F. Correlation between average RNA and protein expression across cell types in the InTraSeq RNA+ADT data. Size and border color of dots reflect the statistical significance of the Pearson correlation coefficient. Fill color of the dots shows the protein types.G-J. Average RNA (x-axis) vs. protein (y-axis) expression across cell types for TCF-7 (G), NFAT1 (H), p-MAPK-ERK1/2-Thr202/204 (I) and p-S6-Ser235/236 (J). Pearson correlation coe cients and corresponding unadjusted p-values (using the Algorithm AS 8949) were reported for each RNA-protein pair.