ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-Seq.
ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-Seq.
复制标题
DOI：
10.1371/journal.pone.0067019
复制
发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Gloor GB
中科院分区：
文献类型：
--
作者：
Fernandes AD;Macklaim JM;Linn TG;Reid G;Gloor GB
在处理高通量测序数据时，实验方差是一个主要挑战。这种差异有几个来源：抽样复制，技术复制，生物条件内的变异性，以及生物条件之间的变异性。RNA-Seq的每个样品的高成本通常排除了根据标准ANOVA模型将观察到的方差划分为这些类别所需的大量实验。我们发现，无论是在单一生物体RNA-Seq还是在Meta-RNA-Seq实验中，条件内到条件间变化的分区都不能被合理地忽略，并且进一步发现，如文献中所述，常用的RNA-Seq分析工具不强制执行相对表达水平之和必须为1的约束，从而报告系统性扭曲的表达水平。这两个因素如果不适当地加以适应，就会导致误导性的推论。由于它通常是唯一的生物条件之间和条件内的差异是感兴趣的，我们开发了ALDEx，方差分析的差异表达程序，以确定基因之间的条件内的差异更大。我们发现，差异表达的存在和这些比较差异的大小可以合理地估计，即使是非常小的样本量。
Experimental variance is a major challenge when dealing with high-throughput sequencing data. This variance has several sources: sampling replication, technical replication, variability within biological conditions, and variability between biological conditions. The high per-sample cost of RNA-Seq often precludes the large number of experiments needed to partition observed variance into these categories as per standard ANOVA models. We show that the partitioning of within-condition to between-condition variation cannot reasonably be ignored, whether in single-organism RNA-Seq or in Meta-RNA-Seq experiments, and further find that commonly-used RNA-Seq analysis tools, as described in the literature, do not enforce the constraint that the sum of relative expression levels must be one, and thus report expression levels that are systematically distorted. These two factors lead to misleading inferences if not properly accommodated. As it is usually only the biological between-condition and within-condition differences that are of interest, we developed ALDEx, an ANOVA-like differential expression procedure, to identify genes with greater between- to within-condition differences. We show that the presence of differential expression and the magnitude of these comparative differences can be reasonably estimated with even very small sample sizes.
登录
查看更多内容
影响因子：
--
作者：
Langmead, Ben
通讯作者：
Langmead, Ben
影响因子：
5.8
作者：
Kristiansson, Erik;Hugenholtz, Philip;Dalevi, Daniel
通讯作者：
Dalevi, Daniel
影响因子：
3.7
作者：
La Rosa PS;Brooks JP;Deych E;Boone EL;Edwards DJ;Wang Q;Sodergren E;Weinstock G;Shannon WD
通讯作者：
Shannon WD
影响因子：
7
作者：
Blekhman, Ran;Marioni, John C.;Gilad, Yoav
通讯作者：
Gilad, Yoav
DOI：
10.2307/2987937
发表时间：
1983-01-01
期刊：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES D-THE STATISTICIAN
影响因子：
--
作者：
ALTMAN, DG;BLAND, JM
通讯作者：
BLAND, JM