摘要
传统表达谱数据分析方法集中于寻找差异表达基因和共表达基因集合,没有考虑基因表达产物之间已知的相互作用.近年来在系统生物学的研究中发展了将基因表达谱与蛋白质相互作用网络进行整合分析的方法.现有方法未能综合考虑基因表达差异性和相关性信息,容易导致辨识结果中重要功能分子缺失且生物学功能相关度不高.提出一种融合表达谱差异性和相关性信息的激活子网辨识算法,能够在蛋白质相互作用网络中辨识高功能相关度的激活子网.应用到人免疫缺陷病毒HIV-1感染过程的研究,结果表明,该算法可以有效避免仅考虑基因表达差异性所引入的偏差,揭示了高相关性低表达差异基因在相关通路中的关键性作用.
Traditional analysis of gene expression data focused on identifying differentially expressed and co-expressed genes,which didn＇t take known interactions into consideration.In recent years,many methods have been developed to identify active subnetwork by integrating protein interaction networks with gene expression profiles.Current approaches failed to take full account of both difference and correlation in gene expression that may lead to false positive results.A new algorithm is proposed for identifying active subnetwork by considering both difference and correlation of gene expression profile.The algorithm is employed in the process of gene expression profiles of human immunodeficiency virus infection.The results showed that the algorithm can identify the active subnetwork that has extremely high biologically functional connectivity with human immunodeficiency virus,and effectively avoid the bias introduced by considering differences of gene expression profiles only,i.e.,genes less differentially expressed are also included due to high correlations in gene expression.
出处
《生物化学与生物物理进展》
SCIE
CAS
CSCD
北大核心
2010年第2期208-217,共10页
Progress In Biochemistry and Biophysics
基金
国家重点基础研究发展计划(973)(2005CB321801)
国家自然科学基金(30600281)资助项目~~
关键词
激活子网
表达谱
模拟退火算法
最大生成子树
active subnetwork
gene expression data
simulated annealing algorithm
maximum weight spanning tree
