Greehey Children's Cancer Research Institute, UT Health San Antonio, San Antonio, United States.
Department of Population Health Sciences, UT Health San Antonio, San Antonio, United States.
Elife. 2022 Feb 25;11:e71994. doi: 10.7554/eLife.71994.
Quantifying the activity of gene expression signatures is common in analyses of single-cell RNA sequencing data. Methods originally developed for bulk samples are often used for this purpose without accounting for contextual differences between bulk and single-cell data. More broadly, few attempts have been made to benchmark these methods. Here, we benchmark five such methods, including single sample gene set enrichment analysis (ssGSEA), Gene Set Variation Analysis (GSVA), AUCell, Single Cell Signature Explorer (SCSE), and a new method we developed, Jointly Assessing Signature Mean and Inferring Enrichment (JASMINE). Using cancer as an example, we show cancer cells consistently express more genes than normal cells. This imbalance leads to bias in performance by bulk-sample-based ssGSEA in gold standard tests and down sampling experiments. In contrast, single-cell-based methods are less susceptible. Our results suggest caution should be exercised when using bulk-sample-based methods in single-cell data analyses, and cellular contexts should be taken into consideration when designing benchmarking strategies.
量化基因表达谱的活性在单细胞 RNA 测序数据分析中很常见。为此目的，通常使用最初为批量样本开发的方法，而不考虑批量和单细胞数据之间的上下文差异。更广泛地说，很少有人试图对这些方法进行基准测试。在这里，我们基准测试了五种这样的方法，包括单样本基因集富集分析（ssGSEA）、基因集变异分析（GSVA）、AUCell、单细胞特征探索器（SCSE）和我们开发的一种新方法，联合评估特征均值并推断富集（JASMINE）。我们以癌症为例，表明癌细胞通常比正常细胞表达更多的基因。这种不平衡导致基于批量样本的 ssGSEA 在黄金标准测试和下采样实验中的性能偏差。相比之下，基于单细胞的方法则不太容易受到影响。我们的结果表明，在单细胞数据分析中使用基于批量样本的方法时应谨慎行事，并且在设计基准测试策略时应考虑细胞环境。
