Department of Computation Science and Artificial Intelligence, University of the Basque Country UPV/EHU, Donostia, Spain.
Department of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, Spain.
BMC Bioinformatics. 2020 Apr 7;21(1):135. doi: 10.1186/s12859-020-3463-4.
Microarray technology provides the expression level of many genes. Nowadays, an important issue is to select a small number of informative differentially expressed genes that provide biological knowledge and may be key elements for a disease. With the increasing volume of data generated by modern biomedical studies, software is required for effective identification of differentially expressed genes. Here, we describe an R package, called ORdensity, that implements a recent methodology (Irigoien and Arenas, 2018) developed in order to identify differentially expressed genes. The benefits of parallel implementation are discussed.
ORdensity gives the user the list of genes identified as differentially expressed genes in an easy and comprehensible way. The experimentation carried out in an off-the-self computer with the parallel execution enabled shows an improvement in run-time. This implementation may also lead to an important use of memory load. Results previously obtained with simulated and real data indicated that the procedure implemented in the package is robust and suitable for differentially expressed genes identification.
The new package, ORdensity, offers a friendly and easy way to identify differentially expressed genes, which is very useful for users not familiar with programming.
微阵列技术提供了许多基因的表达水平。如今，一个重要的问题是选择少量有信息的差异表达基因，这些基因提供生物学知识，并且可能是疾病的关键因素。随着现代生物医学研究产生的数据量不断增加，需要软件来有效识别差异表达基因。在这里，我们描述了一个名为 ORdensity 的 R 包，它实现了一种最近的方法（Irigoien 和 Arenas，2018），用于识别差异表达基因。讨论了并行实现的好处。
ORdensity 以一种简单易懂的方式为用户提供了被识别为差异表达基因的基因列表。在一台现成的计算机上进行的并行执行实验表明，运行时间有所提高。这种实现方式还可能导致内存负载的重要利用。使用模拟和真实数据进行的实验结果表明，该软件包中实现的过程是稳健的，适用于差异表达基因的识别。
新的 ORdensity 软件包提供了一种友好且易于使用的方法来识别差异表达基因，这对于不熟悉编程的用户非常有用。
