当前位置: X-MOL 学术Genomics › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Recommendations for the analysis of gene expression data to identify intrinsic differences between similar tissues.
Genomics ( IF 3.4 ) Pub Date : 2020-05-30 , DOI: 10.1016/j.ygeno.2020.05.026
Tooba Abbassi-Daloii 1 , Hermien E Kan 2 , Vered Raz 1 , P A C 't Hoen 3
Affiliation  

Identifying genes involved in functional differences between similar tissues from expression profiles is challenging, because the expected differences in expression levels are small. To exemplify this challenge, we studied the expression profiles of two skeletal muscles, deltoid and biceps, in healthy individuals. We provide a series of guides and recommendations for the analysis of this type of studies. These include how to account for batch effects and inter-individual differences to optimize the detection of gene signatures associated with tissue function. We provide guidance on the selection of optimal settings for constructing gene co-expression networks through parameter sweeps of settings and calculation of the overlap with an established knowledge network. Our main recommendation is to use a combination of the data-driven approaches, such as differential gene expression analysis and gene co-expression network analysis, and hypothesis-driven approaches, such as gene set connectivity analysis. Accordingly, we detected differences in metabolic gene expression between deltoid and biceps that were supported by both data- and hypothesis-driven approaches. Finally, we provide a bioinformatic framework that support the biological interpretation of expression profiles from related tissues from this combination of approaches, which is available at github.com/tabbassidaloii/AnalysisFrameworkSimilarTissues.



中文翻译:

分析基因表达数据以识别相似组织之间的内在差异的建议。

从表达谱中识别涉及相似组织之间功能差异的基因具有挑战性,因为表达水平的预期差异很小。为了举例说明这一挑战,我们研究了健康个体的两个骨骼肌三角肌和二头肌的表达谱。我们为此类研究的分析提供了一系列指南和建议。其中包括如何考虑批次效应和个体间差异,以优化与组织功能相关的基因特征的检测。我们通过设置的参数扫描和与已建立的知识网络的重叠计算,为构建基因共表达网络的最佳设置的选择提供指导。我们的主要建议是结合使用数据驱动的方法,例如差异基因表达分析和基因共表达网络分析,以及假设驱动的方法,例如基因集连接分析。因此,我们检测到三角肌和二头肌之间代谢基因表达的差异,这得到了数据和假设驱动方法的支持。最后,我们提供了一个生物信息学框架,支持通过这种方法组合对相关组织的表达谱进行生物学解释,可在 github.com/tabbassidaloii/AnalysisFrameworkSimilarTissues 上获得。我们检测到三角肌和二头肌之间代谢基因表达的差异,这得到了数据和假设驱动方法的支持。最后,我们提供了一个生物信息学框架,支持通过这种方法组合对相关组织的表达谱进行生物学解释,可在 github.com/tabbassidaloii/AnalysisFrameworkSimilarTissues 上获得。我们检测到三角肌和二头肌之间代谢基因表达的差异,这得到了数据和假设驱动方法的支持。最后,我们提供了一个生物信息学框架,支持通过这种方法组合对相关组织的表达谱进行生物学解释,可在 github.com/tabbassidaloii/AnalysisFrameworkSimilarTissues 上获得。

更新日期:2020-05-30
down
wechat
bug