TWO-SIGMA-G: a new competitive gene set testing framework for scRNA-seq data accounting for inter-gene and cell-cell correlation.
TWO-SIGMA-G: a new competitive gene set testing framework for scRNA-seq data accounting for inter-gene and cell-cell correlation.
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TWO-SIGMA-G：一种新的竞争性基因集测试框架，用于说明基因间和细胞间相关性的 scRNA-seq 数据。
DOI：
10.1093/bib/bbac084
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发表时间：
2022
影响因子：
9.5
通讯作者：
Wu,Di
中科院分区：
文献类型：
--
作者：
VanBuren,Eric;Hu,Ming;Cheng,Liang;Wrobel,John;Wilhelmsen,Kirk;Su,Lishan;Li,Yun;Wu,Di
我们提出了2 - sigma - g，一个竞争性的基因集测试scRNA-seq数据。TWO-SIGMA- g使用基于我们之前发表的TWO-SIGMA的混合效应回归模型来测试基因水平上的差异表达。这种基于回归的模型在基因水平上提供了灵活性和严谨性(1)处理复杂的实验设计，(2)考虑生物重复之间的相关性，(3)适应scRNA-seq数据的分布，以提高统计推断。此外，2 - sigma -g采用了一种新颖的方法来调节设置水平上的基因间相关性（IGC），以控制设置水平的假阳性率。仿真结果表明，与其他方法相比，在IGC存在的情况下，TWO-SIGMA-G算法保留了i型误差，并提高了功率。对两个数据集的应用确定了异种移植小鼠中与hiv相关的干扰素通路和与人类阿尔茨海默病进展相关的通路。
We propose TWO-SIGMA-G, a competitive gene set test for scRNA-seq data. TWO-SIGMA-G uses a mixed-effects regression model based on our previously published TWO-SIGMA to test for differential expression at the gene-level. This regression-based model provides flexibility and rigor at the gene-level in (1) handling complex experimental designs, (2) accounting for the correlation between biological replicates and (3) accommodating the distribution of scRNA-seq data to improve statistical inference. Moreover, TWO-SIGMA-G uses a novel approach to adjust for inter-gene-correlation (IGC) at the set-level to control the set-level false positive rate. Simulations demonstrate that TWO-SIGMA-G preserves type-I error and increases power in the presence of IGC compared with other methods. Application to two datasets identified HIV-associated interferon pathways in xenograft mice and pathways associated with Alzheimer’s disease progression in humans.