在单细胞 RNA 测序 (scRNA-seq) 研究中，细胞类型及其标记基因通常通过聚类和差异表达基因 (DEG) 分析来识别。常见的做法是使用方差和偏差等替代标准选择基因，然后使用选定的基因对它们进行聚类，并在假设已知细胞类型的情况下通过 DEG 分析检测标记。替代标准可能会遗漏重要基因或选择不重要基因，而DEG分析则存在选择偏差问题。我们提出了 Festem，一种直接选择下游聚类细胞类型标记的统计方法。 Festem 区分具有簇信息的细胞间异质分布的标记基因。模拟和 scRNA-seq 应用表明 Festem 可以高精度、灵敏地选择标记，并能够识别其他方法经常遗漏的细胞类型。在大型肝内胆管癌数据集中，我们确定了不同的 CD8 T 细胞类型和潜在的预后标记基因。版权所有 © 2024 作者。由爱思唯尔公司出版。保留所有权利。

In single-cell RNA sequencing (scRNA-seq) studies, cell types and their marker genes are often identified by clustering and differentially expressed gene (DEG) analysis. A common practice is to select genes using surrogate criteria such as variance and deviance, then cluster them using selected genes and detect markers by DEG analysis assuming known cell types. The surrogate criteria can miss important genes or select unimportant genes, while DEG analysis has the selection-bias problem. We present Festem, a statistical method for the direct selection of cell-type markers for downstream clustering. Festem distinguishes marker genes with heterogeneous distribution across cells that are cluster informative. Simulation and scRNA-seq applications demonstrate that Festem can sensitively select markers with high precision and enables the identification of cell types often missed by other methods. In a large intrahepatic cholangiocarcinoma dataset, we identify diverse CD8+ T cell types and potential prognostic marker genes.Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.