Zou Jian, Düren Yannick, Qin Li-Xuan
Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, United States.
Department of Mathematics, Ruhr-University Bochum, Bochum, Germany.
Front Genet. 2022 Jan 28;12:823431. doi: 10.3389/fgene.2021.823431. eCollection 2021.
We present a new R package for assessing the performance of depth normalization in microRNA sequencing data. It provides a pair of microRNA sequencing data sets for the same set of tumor samples, additional pairs of data sets simulated by re-sampling under various patterns of differential expression, and a collection of numerical and graphical tools for assessing the performance of normalization methods. Users can easily assess their chosen normalization method and compare its performance to nine methods already included in the package. enables an objective and systematic evaluation of normalization methods in microRNA sequencing using realistically distributed and robustly benchmarked data under a wide range of differential expression patterns. To our best knowledge, this is the first such tool available. The data sets and source code of the R package can be found at https://github.com/LXQin/PRECISION.seq.
我们展示了一个用于评估微小RNA测序数据中深度归一化性能的新R包。它为同一组肿瘤样本提供了一对微小RNA测序数据集，通过在各种差异表达模式下重新采样模拟的额外数据集对，以及一组用于评估归一化方法性能的数值和图形工具。用户可以轻松评估他们选择的归一化方法，并将其性能与该包中已包含的九种方法进行比较。这使得能够在广泛的差异表达模式下，使用实际分布且经过严格基准测试的数据，对微小RNA测序中的归一化方法进行客观和系统的评估。据我们所知，这是首个此类可用工具。该R包的数据集和源代码可在https://github.com/LXQin/PRECISION.seq上找到。
