摘要
利用双向聚类方法提高系统性红斑狼疮(systemic lupus erythematosus,SLE)基因表达数据特征基因提取的有效性;预测SLE患病过程中转录因子活性以及对靶基因的调控强度变化,构建和挖掘与SLE发病密切相关的转录调控网络及其生物过程.利用FastICA方法进行双向聚类筛选出800个SLE显著基因,结合转录调控信息选取其中表达显著的转录因子及其靶基因,利用NCA预测SLE发病过程中转录因子活性以及对靶基因调控强度的变化,并构建调控网络.构建了由9个转录因子和47个靶基因所组成的调控网络,结合分子生物学分析发现,转录因子在正常样本和患病样本中的活性有明显的变化趋势,其调控的靶基因的变化符合SLE的病理特征.利用矩阵分解技术进行SLE特征基因提取及转录调控网络构建能够发现多个与炎症反应及免疫系统等密切相关的生物过程,对研究SLE致病机理和相应的生物学实验提供了方法和依据.
Using biclustering method to improve the effectiveness of extraction of characteristic genes from systemic lupus erythematosus(SLE)gene expression data.To predict the activities of transcriptional factors(TFs)and their regulatory influences on target genes during SLE course,to construct a SLE-related transcriptional regulatory network and to discover SLE-related biological processes by matrix decomposition techniques.Bicluster method,FastICA,was applied to SLE gene expression data to extract 800 characteristic genes.Then,by integrating the prior biological information of transcriptional regulation,significant TFs and their target genes were extracted.Based on that,NCA was applied to determine the activities of TFs and their regulatory influences on target genes,and the transcriptional regulatory network of SLE was constructed.The transcriptional regulatory network consisting of 9significantly changed TFs and their regulated 47 target genes was constructed.Combining with molecular biological analysis we found that the changes of the TFs activities had evident influences on target genes between normal and SLE samples,which was consistent with the pathological features of SLE.The methods applied here using matrix decomposition technique have effectively extracted characteristic genes and constructed a transcriptional regulatory network of SLE,and biological processes closely related to inflammatory response and immune system could be discovered.Our methods would provide a novel method for uncovering the pathogenesis of SLE and designing the corresponding biological experiments.
出处
《河北师范大学学报(自然科学版)》
CAS
2016年第2期156-164,共9页
Journal of Hebei Normal University:Natural Science
基金
国家自然科学基金(61271446)
关键词
系统性红斑狼疮
基因表达数据
快速独立成分分析
网络成分分析
调控网络
systemic lupus erythematosus
gene expression data
fast independent component analysis
network component analysis
regulatory network