Computational tools for RNA sequencing power analysis and data integration

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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.