Evaluation of logistic regression models and effect of covariates for case-control study in RNA-Seq analysis.
Evaluation of logistic regression models and effect of covariates for case-control study in RNA-Seq analysis.
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DOI：
10.1186/s12859-017-1498-y
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发表时间：
2017-02-06
影响因子：
3
通讯作者：
DeStefano AL
中科院分区：
文献类型：
--
作者：
Choi SH;Labadorf AT;Myers RH;Lunetta KL;Dupuis J;DeStefano AL
下一代测序以短读数的形式提供RNA分子的计数，产生离散的、通常高度非正态分布的基因表达测量。尽管负二项（NB）回归在RNA测序（RNA-Seq）数据分析中已被普遍接受，但其适当性尚未得到详尽评价。我们探索逻辑回归作为RNA-Seq研究的替代方法，旨在比较病例和对照，其中使用模拟和亨廷顿疾病数据将疾病状态建模为RNA-Seq读数的函数。我们评估调整与基因表达有未知关系的协变量的效果。最后，我们将数据自适应方法，以比较假阳性率。当样本量较小或基因表达水平高度分散时，NB回归显示出膨胀的I型错误率，但经典逻辑和贝叶斯逻辑（BL）回归是保守的。Firth的逻辑（FL）回归表现良好或稍显保守。大样本量和低离散度通常使所有方法的I类错误率接近0.05和0.01的标称α水平。然而，在应用数据自适应方法之后，I型错误率得到控制。NB、BL和FL回归在大样本量、大log 2倍数变化和低离散度的情况下获得增加的功效。FL回归的功效与NB回归相当。我们得出结论，实施数据自适应方法适当地控制RNA-Seq分析中的I型错误率。Firth的逻辑回归提供了一个简洁的统计推断过程，并减少了负二项框架中不准确估计的分散参数的虚假关联。本文的在线版本（doi：10.1186/s12859-017-1498-y）包含补充材料，可供授权用户使用。
Next generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Although Negative Binomial (NB) regression has been generally accepted in the analysis of RNA sequencing (RNA-Seq) data, its appropriateness has not been exhaustively evaluated. We explore logistic regression as an alternative method for RNA-Seq studies designed to compare cases and controls, where disease status is modeled as a function of RNA-Seq reads using simulated and Huntington disease data. We evaluate the effect of adjusting for covariates that have an unknown relationship with gene expression. Finally, we incorporate the data adaptive method in order to compare false positive rates. When the sample size is small or the expression levels of a gene are highly dispersed, the NB regression shows inflated Type-I error rates but the Classical logistic and Bayes logistic (BL) regressions are conservative. Firth’s logistic (FL) regression performs well or is slightly conservative. Large sample size and low dispersion generally make Type-I error rates of all methods close to nominal alpha levels of 0.05 and 0.01. However, Type-I error rates are controlled after applying the data adaptive method. The NB, BL, and FL regressions gain increased power with large sample size, large log2 fold-change, and low dispersion. The FL regression has comparable power to NB regression. We conclude that implementing the data adaptive method appropriately controls Type-I error rates in RNA-Seq analysis. Firth’s logistic regression provides a concise statistical inference process and reduces spurious associations from inaccurately estimated dispersion parameters in the negative binomial framework. The online version of this article (doi:10.1186/s12859-017-1498-y) contains supplementary material, which is available to authorized users.