Background RNAseq is nowadays the method of choice for transcriptome analysis. In the last decades, a high number of statistical methods, and associated bioinformatics tools, for RNAseq analysis were developed. More recently, statistical studies realised neutral comparison studies using benchmark datasets, shedding light on the most appropriate approaches for RNAseq data analysis. Results DiCoExpress is a script-based tool implemented in R that includes methods chosen based on their performance in neutral comparisons studies. DiCoExpress uses pre-existing R packages including FactoMineR, edgeR and coseq, to perform quality control, differential, and co-expression analysis of RNAseq data. Users can perform the full analysis, providing a mapped read expression data file and a file containing the information on the experimental design. Following the quality control step, the user can move on to the differential expression analysis performed using generalized linear models thanks to the automated contrast writing function. A co-expression analysis is implemented using the coseq package. Lists of differentially expressed genes and identified co-expression clusters are automatically analyzed for enrichment of annotations provided by the user. We used DiCoExpress to analyze a publicly available RNAseq dataset on the transcriptional response of Brassica napus L. to silicon treatment in plant roots and mature leaves. This dataset, including two biological factors and three replicates for each condition, allowed us to demonstrate in a tutorial all the features of DiCoExpress. Conclusions DiCoExpress is an R script-based tool allowing users to perform a full RNAseq analysis from quality controls to co-expression analysis through differential analysis based on contrasts inside generalized linear models. DiCoExpress focuses on the statistical modelling of gene expression according to the experimental design and facilitates the data analysis leading the biological interpretation of the results.

中文翻译：

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DiCoExpress：一种通过基于 GLM 模型内部对比的差异分析来处理从质量控制到共表达分析的多因素 RNAseq 实验的工具。

背景 RNAseq 是当今转录组分析的首选方法。在过去的几十年中，开发了用于 RNAseq 分析的大量统计方法和相关的生物信息学工具。最近，统计研究使用基准数据集实现了中性比较研究，揭示了 RNAseq 数据分析的最合适方法。结果 DiCoExpress 是在 R 中实现的基于脚本的工具，其中包括根据它们在中性比较研究中的表现选择的方法。DiCoExpress 使用预先存在的 R 包，包括 FactoMineR、edgeR 和 coseq，对 RNAseq 数据进行质量控制、差异和共表达分析。用户可以执行完整的分析，提供映射的读取表达数据文件和包含实验设计信息的文件。在质量控制步骤之后，由于自动对比写入功能，用户可以继续使用广义线性模型执行差异表达分析。使用 coseq 包实现共表达分析。自动分析差异表达基因列表和已识别的共表达簇，以丰富用户提供的注释。我们使用 DiCoExpress 分析了一个公开可用的 RNAseq 数据集，该数据集关于欧洲油菜对植物根和成熟叶中硅处理的转录反应。该数据集包括两个生物因素和每个条件的三个重复，使我们能够在教程中演示 DiCoExpress 的所有功能。结论 DiCoExpress 是一个基于 R 脚本的工具，允许用户通过基于广义线性模型内部对比的差异分析来执行从质量控制到共表达分析的完整 RNAseq 分析。DiCoExpress 专注于根据实验设计对基因表达进行统计建模，并促进数据分析，从而对结果进行生物学解释。