With rapid advances in single-cell profiling technologies, larger-scale investigations that require comparisons of multiple single-cell datasets can lead to novel findings. Specifically, quantifying cell-type-specific responses to different conditions across single-cell datasets could be useful in understanding how the difference in conditions is induced at a cellular level. In this study, we present a computational pipeline that quantifies cell-type-specific differences and identifies genes responsible for the differences. We quantify differences observed in a low-dimensional uniform manifold approximation and projection for dimension reduction space as a proxy for the difference present in the high-dimensional space and use SHapley Additive exPlanations to quantify genes driving the differences. In this study, we applied our algorithm to the Iris flower dataset, single-cell RNA sequencing dataset, and mass cytometry dataset and demonstrate that it can robustly quantify cell-type-specific differences and it can also identify genes that are responsible for the differences.

中文翻译：

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使用统一流形近似和投影进行降维和 Shapley 加法解释来量化单细胞数据集的细胞类型特异性差异。

随着单细胞分析技术的快速进步，需要比较多个单细胞数据集的更大规模的研究可以带来新的发现。具体来说，量化单细胞数据集中不同条件下的细胞类型特异性反应可能有助于理解如何在细胞水平上诱导条件差异。在这项研究中，我们提出了一个计算管道，可以量化细胞类型特异性差异并识别导致差异的基因。我们量化在低维均匀流形近似和降维空间投影中观察到的差异作为高维空间中存在的差异的代理，并使用 SHapley Additive exPlanations 来量化驱动差异的基因。在这项研究中，