Transcriptomics is widely used to assess the state of biological systems. There are many tools for the different steps, such as normalization, differential expression, and enrichment. While numerous studies have examined the impact of method choices on differential expression results, little attention has been paid to their effects on further downstream functional analysis, which typically provides the basis for interpretation and follow-up experiments. To address this, we introduce FLOP, a comprehensive nextflow-based workflow combining methods to perform end-to-end analyses of transcriptomics data. We illustrate FLOP on datasets ranging from end-stage heart failure patients to cancer cell lines. We discovered effects not noticeable at the gene-level, and observed that not filtering the data had the highest impact on the correlation between pipelines in the gene set space. Moreover, we performed three benchmarks to evaluate the 12 pipelines included in FLOP, and confirmed that filtering is essential in scenarios of expected moderate-to-low biological signal. Overall, our results underscore the impact of carefully evaluating the consequences of the choice of preprocessing methods on downstream enrichment analyses. We envision FLOP as a valuable tool to measure the robustness of functional analyses, ultimately leading to more reliable and conclusive biological findings.

中文翻译：

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评估转录组学数据分析流程对下游功能富集结果的影响


转录组学广泛用于评估生物系统的状态。有许多工具可用于不同的步骤，例如标准化、差异表达和富集。虽然许多研究已经检验了方法选择对差异表达结果的影响，但很少关注它们对进一步下游功能分析的影响，而下游功能分析通常为解释和后续实验提供基础。为了解决这个问题，我们引入了 FLOP，这是一种基于 n​​extflow 的综合工作流程，结合了对转录组数据进行端到端分析的方法。我们在从末期心力衰竭患者到癌细胞系的数据集上说明了 FLOP。我们发现在基因水平上的影响并不明显，并观察到不过滤数据对基因集空间中管道之间的相关性影响最大。此外，我们还进行了三个基准测试来评估 FLOP 中包含的 12 个管道，并确认过滤在预期的中低生物信号场景中至关重要。总的来说，我们的结果强调了仔细评估预处理方法选择对下游富集分析的影响。我们设想 FLOP 是衡量功能分析稳健性的一个有价值的工具，最终带来更可靠和结论性的生物学发现。