多项研究RNA-seq数据的差异荟萃分析
Differential meta-analysis of RNA-seq data from multiple studies
摘要：
高通量测序现在经常用于转录组（RNA-seq）的研究，特别是用于实验条件之间的比较。目前，在这样的实验中通常考虑有限数量的生物学重复，导致差异表达的检测能力低。随着其成本不断下降，可能会进行更多的后续研究，以重新解决同样的生物学问题。我们演示了如何将先前用于微阵列荟萃分析的p值组合技术用于多个相关研究的RNA-seq数据的差异分析。将这些技术与负二项广义线性模型（GLM）进行比较，包括对人黑素瘤细胞系模拟数据和真实数据的固定研究效应。固定研究效应的GLM在低研究间变异性和少量研究中表现良好，但在中至大研究间变异性和大量研究中表现优于荟萃分析方法。综上所述，本文所述的p值组合技术是一种有价值的工具，可通过适当解释研究中的生物学和技术变异性以及其他研究特异性效应，对RNA-seq数据进行差异荟萃分析。R包MetaRNASeq在R Forge上可用。
英文摘要：
High-throughput sequencing is now regularly used for studies of the transcriptome (RNA-seq), particularly for comparisons among experimental conditions. For the time being, a limited number of biological replicates are typically considered in such experiments, leading to low detection power for differential expression. As their cost continues to decrease, it is likely that additional follow-up studies will be conducted to re-address the same biological question. We demonstrate how p-value combination techniques previously used for microarray meta-analyses can be used for the differential analysis of RNA-seq data from multiple related studies. These techniques are compared to a negative binomial generalized linear model (GLM) including a fixed study effect on simulated data and real data on human melanoma cell lines. The GLM with fixed study effect performed well for low inter-study variation and small numbers of studies, but was outperformed by the meta-analysis methods for moderate to large inter-study variability and larger numbers of studies. To conclude, the p-value combination techniques illustrated here are a valuable tool to perform differential meta-analyses of RNA-seq data by appropriately accounting for biological and technical variability within studies as well as additional study-specific effects. An R package metaRNASeq is available on the R Forge.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
