Optimizing murine sample sizes for RNA-seq studies revealed from large-scale comparative analysis

In order to determine the N, the usual approach is to perform a power calculation, which involves understanding the variability between samples and the expected effect size. Here, we focused on bulk RNA-seq experiments, which have become ubiquitous in biology, but which have many unknown or difficult to estimate parameters, and so the required analyses to determine the minimum N is typically lacking. We therefore performed two N=30 profiling studies between wild-type mice and mice in which one copy of a gene had been deleted, to determine how many mice would be required to minimize false positives and to maximize true discoveries found in the N of 30 experiment Dchs1+/- and Fat+/- heterozygous mice were generated using Regenerons (Tarrytown, NY) VelociGene technology (ID #20561), and Wild-type littermates were used as controls. All experiments were performed on 100% C57BL/6NTac background. RNA-Seq was performed on RNA purified from heterozygous and homozygous Dchs1 or Fat4 heterozygous mice tissues (heart, kidney, liver, and lung).为确定样本量N，常规方法是开展功效分析（power calculation），该分析需明确样本间的变异程度与预期效应量。本研究聚焦于已在生物学领域广泛普及的批量RNA测序（bulk RNA-seq）实验，但这类实验存在诸多未知或难以估算的参数，因此通常缺乏用于确定最小样本量N的必要分析流程。为此，我们针对野生型小鼠与单等位基因敲除小鼠开展了两组样本量为30的转录组谱分析研究，以明确在该样本量下，需多少只小鼠才能最大限度减少假阳性结果并最大化真实发现率。本研究中的Dchs1+/-与Fat+/-杂合子小鼠均通过再生元公司（Regeneron，位于纽约州塔里敦）的VelociGene技术（编号ID #20561）构建获得，野生型同窝小鼠作为对照。所有实验均采用100% C57BL/6NTac遗传背景小鼠。我们对Dchs1或Fat4杂合子及纯合子小鼠的心脏、肾脏、肝脏与肺脏组织提取的RNA进行了RNA测序（RNA-Seq）。