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Integrative enrichment analysis of gene expression based on an artificial neuron
BMC Medical Genomics ( IF 2.7 ) Pub Date : 2021-08-25 , DOI: 10.1186/s12920-021-00988-x
Xue Jiang 1 , Weihao Pan 1 , Miao Chen 1 , Weidi Wang 1 , Weichen Song 1 , Guan Ning Lin 1, 2
Affiliation  

Huntington’s disease is a kind of chronic progressive neurodegenerative disease with complex pathogenic mechanisms. To data, the pathogenesis of Huntington’s disease is still not fully understood, and there has been no effective treatment. The rapid development of high-throughput sequencing technologies makes it possible to explore the molecular mechanisms at the transcriptome level. Our previous studies on Huntington’s disease have shown that it is difficult to distinguish disease-associated genes from non-disease genes. Meanwhile, recent progress in bio-medicine shows that the molecular origin of chronic complex diseases may not exist in the diseased tissue, and differentially expressed genes between different tissues may be helpful to reveal the molecular origin of chronic diseases. Therefore, developing integrative analysis computational methods for the multi-tissues gene expression data, exploring the relationship between differentially expressed genes in different tissues and the disease, can greatly accelerate the molecular discovery process. For analysis of the intra- and inter- tissues’ differentially expressed genes, we designed an integrative enrichment analysis method based on an artificial neuron (IEAAN). Firstly, we calculated the differential expression scores of genes which are seen as features of the corresponding gene, using fold-change approach with intra- and inter- tissues’ gene expression data. Then, we weighted sum all the differential expression scores through a sigmoid function to get differential expression enrichment score. Finally, we ranked the genes according to the enrichment score. Top ranking genes are supposed to be the potential disease-associated genes. In this study, we conducted large amounts of experiments to analyze the differentially expressed genes of intra- and inter- tissues. Experimental results showed that genes differentially expressed between different tissues are more likely to be Huntington’s disease-associated genes. Five disease-associated genes were selected out in this study, two of which have been reported to be implicated in Huntington’s disease. We proposed a novel integrative enrichment analysis method based on artificial neuron (IEAAN), which displays better prediction precision of disease-associated genes in comparison with the state-of-the-art statistical-based methods. Our comprehensive evaluation suggests that genes differentially expressed between striatum and liver tissues of health individuals are more likely to be Huntington’s disease-associated genes.

中文翻译:

基于人工神经元的基因表达综合富集分析

亨廷顿舞蹈病是一种致病机制复杂的慢性进行性神经退行性疾病。资料显示,亨廷顿舞蹈病的发病机制至今尚未完全明了,也没有有效的治疗方法。高通量测序技术的快速发展使得在转录组水平上探索分子机制成为可能。我们之前对亨廷顿舞蹈症的研究表明,很难区分疾病相关基因和非疾病基因。同时,生物医学的最新进展表明,慢性复杂疾病的分子起源可能并不存在于患病组织中,不同组织之间差异表达的基因可能有助于揭示慢性疾病的分子起源。因此,开发多组织基因表达数据的综合分析计算方法,探索不同组织中差异表达基因与疾病的关系,可以大大加速分子发现进程。为了分析组织内和组织间的差异表达基因,我们设计了一种基于人工神经元(IEAAN)的综合富集分析方法。首先,我们使用组织内和组织间基因表达数据的倍数变化方法计算基因的差异表达分数,这些差异表达分数被视为相应基因的特征。然后,我们通过 sigmoid 函数对所有差异表达分数进行加权求和,得到差异表达富集分数。最后,我们根据富集分数对基因进行排序。排名靠前的基因应该是潜在的疾病相关基因。在本研究中,我们进行了大量的实验来分析组织内和组织间的差异表达基因。实验结果表明,不同组织之间差异表达的基因更有可能是亨廷顿病相关基因。这项研究中选出了五个与疾病相关的基因,其中两个据报道与亨廷顿病有关。我们提出了一种基于人工神经元(IEAAN)的新型综合富集分析方法,与最先进的基于统计的方法相比,该方法显示出更好的疾病相关基因预测精度。我们的综合评估表明,健康个体纹状体和肝组织之间差异表达的基因更有可能是亨廷顿病相关基因。
更新日期:2021-08-25
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