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用于人类大脑基因组分析的性别基因:用于比较探针水平数据提取的内部对照

Sex genes for genomic analysis in human brain: internal controls for comparison of probe level data extraction.

作者信息

Galfalvy Hanga C, Erraji-Benchekroun Loubna, Smyrniotopoulos Peggy, Pavlidis Paul, Ellis Steven P, Mann J John, Sibille Etienne, Arango Victoria

机构信息

Department of Neuroscience, New York State Psychiatric Institute, New York, NY 10032, USA.

出版信息

BMC Bioinformatics. 2003 Sep 8;4:37. doi: 10.1186/1471-2105-4-37.

Abstract

BACKGROUND

Genomic studies of complex tissues pose unique analytical challenges for assessment of data quality, performance of statistical methods used for data extraction, and detection of differentially expressed genes. Ideally, to assess the accuracy of gene expression analysis methods, one needs a set of genes which are known to be differentially expressed in the samples and which can be used as a "gold standard". We introduce the idea of using sex-chromosome genes as an alternative to spiked-in control genes or simulations for assessment of microarray data and analysis methods.

RESULTS

Expression of sex-chromosome genes were used as true internal biological controls to compare alternate probe-level data extraction algorithms (Microarray Suite 5.0 [MAS5.0], Model Based Expression Index [MBEI] and Robust Multi-array Average [RMA]), to assess microarray data quality and to establish some statistical guidelines for analyzing large-scale gene expression. These approaches were implemented on a large new dataset of human brain samples. RMA-generated gene expression values were markedly less variable and more reliable than MAS5.0 and MBEI-derived values. A statistical technique controlling the false discovery rate was applied to adjust for multiple testing, as an alternative to the Bonferroni method, and showed no evidence of false negative results. Fourteen probesets, representing nine Y- and two X-chromosome linked genes, displayed significant sex differences in brain prefrontal cortex gene expression.

CONCLUSION

In this study, we have demonstrated the use of sex genes as true biological internal controls for genomic analysis of complex tissues, and suggested analytical guidelines for testing alternate oligonucleotide microarray data extraction protocols and for adjusting multiple statistical analysis of differentially expressed genes. Our results also provided evidence for sex differences in gene expression in the brain prefrontal cortex, supporting the notion of a putative direct role of sex-chromosome genes in differentiation and maintenance of sexual dimorphism of the central nervous system. Importantly, these analytical approaches are applicable to all microarray studies that include male and female human or animal subjects.

摘要

背景

复杂组织的基因组研究在数据质量评估、用于数据提取的统计方法性能以及差异表达基因的检测方面带来了独特的分析挑战。理想情况下,为了评估基因表达分析方法的准确性,需要一组已知在样本中差异表达且可作为“金标准”的基因。我们引入了使用性染色体基因作为替代掺入对照基因或模拟来评估微阵列数据和分析方法的想法。

结果

性染色体基因的表达被用作真正的内部生物学对照,以比较替代的探针水平数据提取算法(微阵列套件5.0 [MAS5.0]、基于模型的表达指数 [MBEI] 和稳健多阵列平均 [RMA]),评估微阵列数据质量,并建立一些用于分析大规模基因表达的统计指南。这些方法在一个新的大型人类脑样本数据集上实施。RMA生成的基因表达值比MAS5.0和MBEI衍生的值具有明显更小的变异性和更高的可靠性。一种控制错误发现率的统计技术被应用于调整多重检验,作为Bonferroni方法的替代方法,并且没有显示出假阴性结果的证据。代表九个Y染色体和两个X染色体连锁基因的14个探针集在脑前额叶皮质基因表达中显示出显著的性别差异。

结论

在本研究中,我们证明了使用性基因作为复杂组织基因组分析的真正生物学内部对照,并提出了用于测试替代寡核苷酸微阵列数据提取方案以及调整差异表达基因多重统计分析的分析指南。我们的结果还为脑前额叶皮质基因表达中的性别差异提供了证据,支持性染色体基因在中枢神经系统性二态性的分化和维持中具有假定直接作用的观点。重要的是,这些分析方法适用于所有包括男性和女性人类或动物受试者的微阵列研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90b2/212256/9a846f1747b6/1471-2105-4-37-1.jpg

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