Tree-based differential testing using inferential uncertainty for RNA-seq

doi:10.1101/gr.279981.124

2025-10-01

DOI：10.1101/gr.279981.124

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摘要

En 中文

鉴定差异表达转录本在转录组学中是一个至关重要且具有挑战性的问题。某些转录本丰度估计存在显著不确定性，若忽略此不确定性，可能导致假阳性结果的夸大；若纳入此不确定性，则可能降低统计功效。在此，我们介绍一种数据驱动的差异检验方法，该方法在保持统计功效的同时最大化生物学分辨率。给定一组RNA-seq样本，TreeTerminus将转录本排列成层次树结构，该结构编码不同层次的分辨率以解释转录组的丰度，且不确定性通常随着从树末端向上移动而递减。我们引入mehenDi，该方法利用TreeTerminus的树结构进行差异检验。mehenDi输出的节点（称为选定节点）以数据驱动的方式确定，旨在最大化可从数据中提取的信号，同时控制转录本丰度估计的相关不确定性。所识别的选定节点可包含转录本和内部节点，且任意两个节点之间不存在祖先/后代关系。我们通过模拟和实验数据集评估了该方法，并将其性能与其他基于树的差异方法以及考虑不确定性的差异转录本/基因表达方法进行比较。我们的方法能够检测到显示出强烈差异表达信号的内部节点，这些节点在单独分析转录本时会被忽略。

Keyword:

FALSE DISCOVERY RATE

QUANTIFICATION

EXPRESSION

期刊

Genome Research

IF:

5.5

论文数:

5.6K

被引数:

4.3W

机构

University System of Maryland

学者数:

6.5W

论文数: 5.6W

被引数: 113

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