Single-cell RNA-seq technologies have been successfully employed over the past decade to generate many high resolution cell atlases. These have proved invaluable in recent efforts aimed at understanding the cell type specificity of host genes involved in SARS-CoV-2 infections. While single-cell atlases are based on well-sampled highly-expressed genes, many of the genes of interest for understanding SARS-CoV-2 can be expressed at very low levels. Common assumptions underlying standard single-cell analyses don't hold when examining low-expressed genes, with the result that standard workflows can produce misleading results.

中文翻译：

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通过log（x + 1）*或log（1 + x）*对单细胞RNA-seq计数进行归一化

在过去的十年中，单细胞RNA-seq技术已成功用于产生许多高分辨率细胞图谱。在旨在了解与SARS-CoV-2感染有关的宿主基因的细胞类型特异性的最新努力中，这些已被证明是无价的。虽然单细胞图谱基于采样良好的高表达基因，但许多用于理解SARS-CoV-2的感兴趣的基因都可以非常低的水平表达。当检查低表达基因时，标准单细胞分析的基本假设不成立，结果标准工作流程可能会产生误导性的结果。