Statistical analysis of MPSS measurements: Application to the study of LPS-activated macrophage gene expression
Statistical analysis of MPSS measurements: Application to the study of LPS-activated macrophage gene expression
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DOI：
10.1073/pnas.0406555102
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发表时间：
2005-02-01
影响因子：
11.1
通讯作者：
Roach, JC
中科院分区：
文献类型：
--
作者：
Stolovitzky, GA;Kundaje, A;Roach, JC
大规模平行签名测序（MPSS）是近年来发展起来的一种高通量转录谱分析技术，它能够对样品中的几乎所有转录本进行谱分析，而不需要事先知道转录基因的序列。与DNA微阵列的情况一样，有效的数据分析至关重要地取决于了解噪音如何影响测量。我们分析了MPSS中噪声的来源，并提出了一个定量模型，描述重复MPSS测定之间的变异性。我们使用这个模型来构建统计假设，以检验在成对比较中观察到的基因表达变化是否显著。然后将该分析扩展到确定在时间序列测量过程中测量的表达水平变化的显著性。我们将这些分析技术应用于LPS刺激的巨噬细胞上MPSS基因表达测量的时间序列的研究。为了评估我们的统计学显著性指标，我们将我们的结果与使用Affyssin GeneChips测量的巨噬细胞活化的已发表数据进行比较。
Massively Parallel Signature Sequencing (MPSS), a recently developed high-throughput transcription profiling technology, has the ability to profile almost every transcript in a sample without requiring prior knowledge of the sequence of the transcribed genes. As is the case with DNA microarrays, effective data analysis depends crucially on understanding how noise affects measurements. We analyze the sources of noise in MPSS and present a quantitative model describing the variability between replicate MPSS assays. We use this model to construct statistical hypotheses that test whether an observed change in gene expression in a pair-wise comparison is significant. This analysis is then extended to the determination of the significance of changes in expression levels measured over the course of a time series of measurements. We apply these analytic techniques to the study of a time series of MPSS gene expression measurements on LPS-stimulated macrophages. To evaluate our statistical significance metrics, we compare our results with published data on macrophage activation measured by using Affymetrix GeneChips.