Background: Stemness is defined as the potential of cells for self-renewal and differentiation. Many transcriptome-based methods for stemness evaluation have been proposed. However, all these stemness indexes showed low correlations with differentiation time and the limitation to identify the high-stemness cells across datasets. Methods: Here, we constructed a stemness index for single-cell samples (StemSC) based on relative expression orderings (REO) of gene pairs. Firstly, we identified the

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... emness-related genes by selecting the genes significantly related to differentiation time in all five datasets. Then, we used 13 RNA-seq datasets from both the bulk and single-cell ESC samples to construct the reference REOs. Finally, the StemSC value of a given sample was calculated as the percentage of gene pairs with the same REOs as the ESC samples.Results: We validated the StemSC by its higher correlations with differentiation time in eight normal datasets and its higher correlations with tumor dedifferentiation in three colorectal cancer datasets and four glioma datasets. By using the StemSC, we can recognize the tissue-specific stem genes and automatically construct the cell differentiation trajectories. StemSC also could provide the same threshold to identify high-stemness cells across datasets. Results showed that the tumor cells with high-stemness had fewer interactions with anti-tumor immune cells. Besides, the immunotherapy-treated patients with high-stemness had worse survival than those with low-stemness. Conclusions: All above results showed StemSC is a better stemness index to calculate the stemness across datasets, which can help researchers explore the effect of stemness on other biological processes.