Computational tools for RNA sequencing power analysis and data integration

RNA测序提供了一个了解细胞运作的窗口，可以量化几乎所有表达的基因的水平。不同细胞之间基因表达的差异以及对不同环境的反应可以提供有关生化途径和细胞如何运作的重要信息。然而，能够有效地研究差异基因表达依赖于拥有准确和适当的计算工具来解释数据。这项拨款申请解决了基因表达数据分析的两个重要方面--(I)可以检测到基因表达变化的大小，即实验的力量有多大；(Ii)不同实验中的基因反应有多相似，这些能否被准确确定？在发现基金内进行的研究将通过使用重采样技术来评估RNA测序实验的特殊能力，包括整个实验水平和每个基因(目标1)，从而解决这个问题。这将为任何高通量表达实验提供重要数据，因为将估计所有基因(差异表达或非差异表达)的可检测效应的大小。其次，我们正在使用一种基于统计对比的方法来比较不同实验的数据(目标2)。我们不是比较不同实验产生的差异表达基因列表，而是识别(I)在两个实验中具有相似基因表达变化的基因，以及在两个实验之间差异表达的基因。事后看来，这种方法似乎清晰有效，但在类似方法的应用文献中却缺乏实例。最后，我们将通过探索靶向细胞系中特定生化途径的影响来验证我们的方法(目标3)，扩展McConkey实验室之前的工作。这项工作将为更可靠和深入地探索RNA测序表达数据集创造工具。重要的是，这将使研究人员能够更好地量化可检测到的基因表达变化的大小(力量分析)，而不是只关注被识别为差异表达的东西(显著性检验)。其次，更好的跨多个实验进行比较的工具将对使用高通量表达研究的研究人员大有裨益，他们可以快速识别可能对给定扰动具有高度特异性的基因，而不是代谢或应激反应基因等“共同反应基因”。

RNA sequencing provides a window into the operation of the cell and can quantify levels of almost all expressed genes. Differences in gene expression between different cells and in response to different environments can provide important information on biochemical pathways and how the cell is functioning. Being able to effectively investigate differential gene expression however relies on having accurate and appropriate computational tools to interpret the data. This grant application addresses two important aspects of data analysis of gene expression - (i) what size of change in gene expression can be detected, i.e. what is the power of the experiment; and (ii) how similar are the gene responses across different experiments, and can these be determined accurately? The research conducted within the discovery grant will address this by using resampling techniques to estimate post hoc power of RNA sequencing experiments, both the experiment-wide level and for each gene (Aim 1). This will provide important data for any high-throughput expression experiment, as the magnitude of detectable effect will be estimated for all genes (differentially expressed or not). Secondly, we are using a method based on statistical contrasts to compare data across experiments (Aim 2). Instead of comparing lists of differentially expressed genes produced by separate experiments, we identify (i) genes that have similar changes in gene expression in both experiments, as well as genes that are differentially expressed between experiments. In hindsight this method seems clear and effective, yet there is a lack of examples in the literature of applications of similar methodology. Lastly, we will verify our methods by exploring the effects of targeting specific biochemical pathways in cell lines (Aim 3), extending previous work in the McConkey lab. This work will create tools for more reliable and in-depth exploration of RNA sequencing expression data sets. Importantly, it will permit researchers to better quantify the magnitude of changes in gene expression that can be detected (power analysis) instead of focusing exclusively on what is identified as differentially expressed (significance testing). Secondly, better tools to compare across multiple experiments will be hugely beneficial to researchers using high-throughput expression studies, to quickly identify genes that may have a high degree of specificity to a given perturbation, versus `common responders' such as metabolic or stress response genes.