Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data.
Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data.
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DOI：
10.1186/s12859-021-04438-4
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发表时间：
2021-10-20
影响因子：
3
通讯作者：
Kadota K
中科院分区：
文献类型：
--
作者：
Osabe T;Shimizu K;Kadota K
RNA-seq是一种测量基因表达的工具，通常用于鉴定差异表达基因（DEG）。基因聚类用于对具有相似表达模式的DEG进行分类，以用于对来自实验的数据进行后续分析，例如时间过程或多组比较。然而，基因聚类很少用于分析简单的两组数据或差异表达（DE）。在这项研究中，我们报告说，一个基于模型的聚类算法实现在R包，MBCluster.Seq，也可以用于DE分析。MBCluster.Seq最初使用的输入数据是DEG，而建议的方法（称为MBCdeg）使用所有基因进行分析。该方法使用后验概率的基因分配到一个集群显示非DEG模式的整体基因排名。我们使用模拟和真实的数据比较了MBCdeg与传统R软件包（如edgeR、DESeq 2和TCC）的性能，这些软件包专门用于DE分析。我们的研究结果表明，MBCdeg优于其他方法时，DEG（PDEG）的比例小于50%。然而，DEG识别使用MBCdeg是不太一致的，比传统的方法。我们比较了使用MBCdeg的不同归一化算法的效果，并使用MBCdeg与MBCdeg中未实现的鲁棒归一化算法（称为DEGES）组合进行了分析。新的分析方法显示出比使用具有默认归一化算法的原始MBCdeg更高的稳定性。当PDEG相对较低时，具有DEGES归一化的MBCdeg可用于DEG的识别。由于该方法是基于基因聚类的，DE结果包括关于基因属于哪个表达模式的信息。新方法可能是有用的时间过程和多组数据的分析，其中表达模式的分类往往是必需的。在线版本包含补充材料，可通过10.1186/s12859-021-04438-4获得。
RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering is used to classify DEGs with similar expression patterns for the subsequent analyses of data from experiments such as time-courses or multi-group comparisons. However, gene clustering has rarely been used for analyzing simple two-group data or differential expression (DE). In this study, we report that a model-based clustering algorithm implemented in an R package, MBCluster.Seq, can also be used for DE analysis. The input data originally used by MBCluster.Seq is DEGs, and the proposed method (called MBCdeg) uses all genes for the analysis. The method uses posterior probabilities of genes assigned to a cluster displaying non-DEG pattern for overall gene ranking. We compared the performance of MBCdeg with conventional R packages such as edgeR, DESeq2, and TCC that are specialized for DE analysis using simulated and real data. Our results showed that MBCdeg outperformed other methods when the proportion of DEG (PDEG) was less than 50%. However, the DEG identification using MBCdeg was less consistent than with conventional methods. We compared the effects of different normalization algorithms using MBCdeg, and performed an analysis using MBCdeg in combination with a robust normalization algorithm (called DEGES) that was not implemented in MBCluster.Seq. The new analysis method showed greater stability than using the original MBCdeg with the default normalization algorithm. MBCdeg with DEGES normalization can be used in the identification of DEGs when the PDEG is relatively low. As the method is based on gene clustering, the DE result includes information on which expression pattern the gene belongs to. The new method may be useful for the analysis of time-course and multi-group data, where the classification of expression patterns is often required. The online version contains supplementary material available at 10.1186/s12859-021-04438-4.
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