Interpretation of 'Omics dynamics in a single subject using local estimates of dispersion between two transcriptomes
Interpretation of 'Omics dynamics in a single subject using local estimates of dispersion between two transcriptomes
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发表时间：
2019
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通讯作者：
Qike Li;Samir Rachid Zaim;Dillon Aberasturi;J. Berghout;Haiquan Li;Francesca Vitali;C. Kenost;H. Zhang;Y. Lussier
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作者：
Qike Li;Samir Rachid Zaim;Dillon Aberasturi;J. Berghout;Haiquan Li;Francesca Vitali;C. Kenost;H. Zhang;Y. Lussier
根据RNA测序计算差异表达基因(Deg)需要重复来估计基因的可变性，这一要求在临床上有时在经济上或生理上是不可行的。通过施加限制性的转录组范围的假设来限制传统方法(Edger，NOISeq-sim，DESeq，DEGseq)的推断机会，已经提出了两种没有重复的条件(TCWR)，但没有进行评估。在TCWR条件下(例如，未受影响的组织与肿瘤)，所建议的个体化DEG(IDEG)方法的转换表达的差异遵循在基线表达下跨相关转录本的局部分区计算的分布；此后，使用两组混合模型通过局部错误发现率控制的经验贝叶斯估计每个DEG的概率。在对TCWR方法的大量模拟研究中，iDEG和NOISeq的准确率分别为5%-90%，召回率为75%，假阳性率为1%)和30%-40%(精确度=召回率~90%)。所提出的iDEG方法借用了来自同一个体的局部分布信息，这是一种在低DEGS条件下在没有复制的情况下提高转录本比较准确性的策略。Http://www.lussiergroup.org/publications/iDEG.
Calculating Differentially Expressed Genes (DEGs) from RNA-sequencing requires replicates to estimate gene-wise variability, a requirement that is at times financially or physiologically infeasible in clinics. By imposing restrictive transcriptome-wide assumptions limiting inferential opportunities of conventional methods (edgeR, NOISeq-sim, DESeq, DEGseq), comparing two conditions without replicates (TCWR) has been proposed, but not evaluated. Under TCWR conditions (e.g., unaffected tissue vs. tumor), differences of transformed expression of the proposed individualized DEG (iDEG) method follow a distribution calculated across a local partition of related transcripts at baseline expression; thereafter the probability of each DEG is estimated by empirical Bayes with local false discovery rate control using a two-group mixture model. In extensive simulation studies of TCWR methods, iDEG and NOISeq are more accurate at 5%90%, recall>75%, false_positive_rate<1%) and 30%<DEGs<40% (precision=recall~90%), respectively. The proposed iDEG method borrows localized distribution information from the same individual, a strategy that improves accuracy to compare transcriptomes in absence of replicates at low DEGsconditions. http://www.lussiergroup.org/publications/iDEG.