Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria.
Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.
Methods Mol Biol. 2020;2120:223-232. doi: 10.1007/978-1-0716-0327-7_16.
Since the performance of in silico approaches for estimating immune-cell fractions from bulk RNA-seq data can vary, it is often advisable to compare results of several methods. Given numerous dependencies and differences in input and output format of the various computational methods, comparative analyses can become quite complex. This motivated us to develop immunedeconv, an R package providing uniform and user-friendly access to seven state-of-the-art computational methods for deconvolution of cell-type fractions from bulk RNA-seq data. Here, we show how immunedeconv can be installed and applied to a typical dataset. First, we give an example for obtaining cell-type fractions using quanTIseq. Second, we show how dimensionless scores produced by MCP-counter can be used for cross-sample comparisons. For each of these examples, we provide R code illustrating how immunedeconv results can be summarized graphically.
由于从批量 RNA-seq 数据估算免疫细胞分数的计算方法的性能可能有所不同,因此比较几种方法的结果通常是明智的。鉴于各种计算方法在输入和输出格式上存在众多依赖性和差异,比较分析可能会变得非常复杂。这促使我们开发了 immunedeconv,这是一个 R 包,为从批量 RNA-seq 数据中反卷积细胞类型分数的七种最先进的计算方法提供了统一且用户友好的访问。在这里,我们展示如何安装和应用 immunedeconv 到一个典型的数据集。首先,我们给出了一个使用 quanTIseq 获取细胞类型分数的示例。其次,我们展示了如何使用 MCP-counter 生成的无量纲分数进行跨样本比较。对于这些示例中的每一个,我们提供了 R 代码来说明如何以图形方式总结 immunedeconv 的结果。