论文标题
rleave：用于转录差分表达分析的硅交叉验证方案
RLeave: an in silico cross-validation protocol for transcript differential expression analysis
论文作者
论文摘要
背景和客观：大规模的并行测序技术促进了成绩单差分分析的新发现；但是，必须验证所有新发现，因为转录本表达的多样性可能会损害最相关的识别。 方法：所提出的RLEAVE算法（在R环境中实施）利用常规分析（经典EDGER）以及其他数学方法（保留的样本技术和决策树验证）的组合来识别更相关的候选者是体外或在硅胶验证中。 结果：使用两个样品组的miRNOME表达分析（糖尿病和急性淋巴细胞白血病）测试了RLEAVE方案，并且两者都有RT-QPCR证实的最重要的差异表达miRNA。 结论：该方案适用于RNA-seq研究，突出了用于计算机和/或体外验证中最相关的成绩单。
Background and Objective: The massive parallel sequencing technology facilitates new discoveries in terms of transcript differential analysis; however, all the new findings must be validated, since the diversity of transcript expression may impair the identification of the most relevant ones. Methods: The proposed RLeave algorithm (implemented in the R environment) utilizes a combination of conventional analysis (classic edgeR) together with other mathematical methods (Leave-one-out sample technique and Decision Trees validation) to identify more relevant candidates to be in vitro or in silico validated. Results: The RLeave protocol was tested using miRNome expression analysis of two sample groups (diabetes mellitus and acute lymphoblastic leukemia), and both had their most important differentially expressed miRNA confirmed by RT-qPCR. Conclusion: This protocol is applicable in RNA-SEQ research, highlighting the most relevant transcripts for in silico and/or in vitro validation.
