Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany.
Bioinformatics. 2022 Jan 27;38(4):1181-1182. doi: 10.1093/bioinformatics/btab748.
First-line data quality assessment and exploratory data analysis are integral parts of any data analysis workflow. In high-throughput quantitative omics experiments (e.g. transcriptomics, proteomics and metabolomics), after initial processing, the data are typically presented as a matrix of numbers (feature IDs × samples). Efficient and standardized data quality metrics calculation and visualization are key to track the within-experiment quality of these rectangular data types and to guarantee for high-quality datasets and subsequent biological question-driven inference.
We present MatrixQCvis, which provides interactive visualization of data quality metrics at the per-sample and per-feature level using R's shiny framework. It provides efficient and standardized ways to analyze data quality of quantitative omics data types that come in a matrix-like format (features IDs × samples). MatrixQCvis builds upon the Bioconductor SummarizedExperiment S4 class and thus facilitates the integration into existing workflows.
MatrixQCVis is implemented in R. It is available via Bioconductor and released under the GPL v3.0 license.
Supplementary data are available at Bioinformatics online.
一线数据质量评估和探索性数据分析是任何数据分析工作流程的组成部分。在高通量定量组学实验（例如转录组学、蛋白质组学和代谢组学）中，经过初步处理后，数据通常以数字矩阵的形式呈现（特征 ID × 样本）。高效和标准化的数据质量指标计算和可视化是跟踪这些矩形数据类型的实验内质量的关键，可确保高质量数据集和随后的基于生物学问题的推理。
我们提出了 MatrixQCvis，它使用 R 的 shiny 框架在样本和特征级别上提供数据质量指标的交互式可视化。它提供了高效和标准化的方法来分析以矩阵形式呈现的定量组学数据类型（特征 ID × 样本）的数据质量。MatrixQCvis 建立在 Bioconductor SummarizedExperiment S4 类的基础上，因此便于集成到现有工作流程中。
MatrixQCVis 是用 R 实现的。它可以通过 Bioconductor 使用，并根据 GPL v3.0 许可证发布。
补充数据可在 Bioinformatics 在线获得。
