基因芯片用于组分中药新双龙方的配伍机制研究

采用基因芯片技术研究了中药新双龙方及其有效组分人参总皂苷和丹参总酚酸治疗急性心肌梗塞(AMI)大鼠的作用机制. 以结扎大鼠冠脉左前降支的方法制备急性心梗模型, 采用大鼠全基因组表达谱芯片分别筛选出AMI大鼠给药前及新双龙方与组分给药后的差异表达基因(Ratio>2或<0.5), 通过对4组差异基因的综合分析, 比较了新双龙方与组分作用方式的异同, 从分子调控水平上探讨复方配伍后的增效作用, 同时对与药效作用机理直接相关的钙离子转运相关的Cacna1d基因和血管修复相关的Flnb基因进行了实时荧光逆转录聚合酶链式反应(RT-PCR)定量. 结果表明, 新双龙方与2种有效组分对心梗大鼠基因表达的影响不同. 对差异基因的聚类分析以及药物的调控作用比较结果均表明复方比组分的疗效更好; 通路分析结果表明, 新双龙方与组分均主要通过调控钙信号转导通路、 丝裂原活化蛋白激酶信号转导通路(MAPK)信号等通路发挥药效作用, 且复方涉及更多通路; 筛选出了复方与组分共同调控的靶点基因, 其在3组中具有不完全相同的表达趋势, 但复方具有更积极的调控作用. 本研究结果从分子调控角度证实了复方新双龙方的配伍优势.     

cDNA基因芯片技术分析三聚氰胺肾毒性的相关基因表达

利用基因芯片技术筛查不同剂量的三聚氰胺干预大鼠肾脏的差异表达基因, 并对筛查的差异基因进行生物信息学分析, 推测三聚氰胺肾毒性的分子作用机制. 结果表明, 高剂量三聚氰胺干预的大鼠肾脏差异表达基因数多于低剂量干预的肾脏差异表达基因, 并且涉及到更多重要的分子功能和代谢途径, 表明三聚氰胺肾毒性具有剂量依赖性, 相比低剂量而言, 高剂量三聚氰胺干预对肾脏的危害更为严重.     

X射线荧光光谱结合聚类分析对橡胶轮胎的检验

应用X射线荧光光谱结合系统聚类分析对30种不同的橡胶轮胎样本进行检验研究。利用X射线荧光光谱仪对橡胶轮胎中的主要元素及其含量进行测定,根据测定结果使用聚类分析对样本进行分类。选择最长距离法作为类与类之间距离的定义方法,采用平方欧式距离作为度量区间描述样品间亲疏程度进行系统聚类,最终将30个样本分为12类。该方法简单易行,分类结果可靠且无损轮胎样本,为实际案件的橡胶轮胎检验提供了一种检验思路。     

基于单体烃分析的辛烷值测定方法中模糊聚类技术的应用研究

介绍了一种应用模糊聚类技术构建辛烷值预测模型的新方法。该模型用来由汽油单体烃数据预测汽油辛烷值,通过提取单体烃分析谱图中的140个工艺特征组分以及对辛烷值贡献大的组分为特征值进行模糊聚类。实际应用时,在待测汽油样品共同参与聚类的条件下,用与待测汽油样品为同一类、并有最小欧氏距离(<1.5)的3~10个样本作为构建辛烷值预测模型的样本。这种按新的建模样本选择方式得到预测模型的方法具有更好的辛烷值预测精度、更广的适用范围和更高的数据资源的利用率。     

HPLC-MS结合多元统计分析区分人参产地及筛选皂苷类标志物

利用高效液相色谱-质谱联用(HPLC-MS)技术结合多元统计分析方法, 区分中国人参主产区5个不同产地的45个人参样本, 筛选出差异性皂苷类标志物. 根据人参总皂苷在反相C₁₈色谱柱中的洗脱顺序, 结合串联质谱分析和标准品比对, 在提取的人参总皂苷中鉴定出15种原人参三醇型、 24种原人参二醇型和2种齐墩果酸型共41种皂苷. 对人参总皂苷的HPLC-MS全扫描数据进行了多元统计分析. 正交偏最小二乘-判别分析(OPLS-DA)结果表明, 所建立的分析模型具有良好的数据描述能力和预测能力. 所有人参样本能够根据产地被区分, 并筛选得到同时区分5个产地的差异性皂苷类组分18种; 能够区分任意2个产地人参样本的差异性组分主要为在人参中含量较高的人参皂苷Rb1, Rg1, Re, Rc, Rd, Ro和m-Rb1等. 分层聚类分析(HCA)结果显示, 黑龙江和吉林两省的样本能够独自聚类, 但是绥化市的样本更接近于吉林省. 初步推断原因为绥化市地理位置较接近吉林省, 两地人参生长环境相似并可能存在种质资源交换.     

长期喂饮钇对子代大鼠脑组织中基因表达的影响

通过在饮水中加入钇(0,53.4,5340 mg.L-1),使大鼠长期摄入稀土。7个月后,采用基因芯片技术检测F1子代大脑组织中的基因表达情况。结果显示,与对照组相比,高浓度组有787个基因发生了差异表达,其中505个上调表达,282个下高表达。差异表达基因与细胞受体、信号转导、离子通道有关;低浓度组有44个基因发生了差异表达,其中32个上高表达,12个下调表达。差异表达的基因与细胞骨架、细胞运动以及DNA结合蛋白密切相关。提示长期喂饮稀土钇能改变大鼠脑组织中某些基因的表达,进一步造成机体某些生理功能如学习记忆能力的变化。     

新的聚类方法综合评价血脂指标

本文结合中心聚类和模糊聚类提出了一个新的聚类方法。该法避免了在迭代过程中出现局部最小问题,并可给出每个样本归入每一类的成分矩阵,因而提供更多的聚类程度的信息。应用这种方法对正常人和冠心病患者的血脂指标进行了综合评价,结果满意。     

不相交主成分分析(PCA)和遗传算法(GA)用于差异表达基因的识别

建立了一种基于不相交主成分分析(Disjoint PCA)和遗传算法(GA)的特征变量选择方法, 并用于从基因表达谱(Gene expression profiles)数据中识别差异表达的基因. 在该方法中, 用不相交主成分分析评估基因组在区分两类不同样品时的区分能力; 用GA寻找区分能力最强的基因组; 所识别基因的偶然相关性用统计方法评估. 由于该方法考虑了基因间的协同作用更接近于基因的生物过程, 从而使所识别的基因具有更好的差异表达能力. 将该方法应用于肝细胞癌(HCC)样品的基因芯片数据分析, 结果表明, 所识别的基因具有较强的区分能力, 优于常用的基因芯片显著性分析(Significance analysis of microarrays, SAM)方法.     

基于聚类分析对易燃液体塑料容器盖的光谱检验

将光谱分析方法与聚类方法相结合,对38个模拟案件环境的纵火现场助燃剂塑料容器盖样本进行X射线荧光光谱分析,根据元素种类将38个样本分为6组,建立了一种无损快速检验纵火现场物证的光谱分析方法。使用最短距离聚类法,以欧氏距离作为度量区间表示样本间亲疏关系,进行系统聚类分析,同时使用K-Means快速聚类法与t检验验证聚类效果。结果表明,38个样本分为6组时效果最好。并将各类的样本观测点在Fisher判别空间中的分布和位置情况绘出,与使用元素种类进行分类结果一致,为确定火灾案件性质,侦破案件线索提供了理论依据。     

基于Gene Ontology的表达谱与基因功能相似性的相关性研究

聚类是芯片数据分析中被广泛使用的方法。未知基因的功能通常通过其与已知基因在不同生物状态下具有表达相似性来进行预测。然而，还未有人就这种通过表达相似性来进行功能注释的方法的可靠性进行评估。本文利用Gene Ontology对表达相似性和基因功能相似性的相关关系进行了全面的研究。研究表明，尽管表达谱的相似性和基因功能相似性之间有一定的依赖关系，但相关性较弱。在Gene Ontology的三大类中，相对生物过程和分子功能，基因表达谱的相似性更有助于细胞组分的注释。本文的研究结果对于基因功能的预测有一定的指导意义。     

A statistical method for estimating the proportion of differentially expressed genes

Microarrays have been widely used to identify differentially expressed genes. One related problem is to estimate the proportion of differentially expressed genes. For some complex diseases, the amount of differentially expressed genes may be relatively small and these genes may only have subtly differential expressions. For these microarray data, it is generally difficult to efficiently estimate the proportion of differentially expressed genes. In this study, I propose a likelihood-based method coupled with an expectation-maximization (E-M) algorithm for estimating the proportion of differentially expressed genes. The proposed method has favorable performances if either (i) the P values of differentially expressed genes are homogeneously distributed or (ii) the proportion of differentially expressed genes is relatively small. In both of these situations, I showed through simulations that the proposed method gave satisfactory performances when it was compared to other existing methods. As applications, these methods were applied to two microarray gene expression data sets generated from different platforms.     

On the identification of differentially expressed genes: improving the generalized F-statistics for Affymetrix microarray gene expression data

It has been shown that the generalized F-statistics can give satisfactory performances in identifying differentially expressed genes with microarray data. However, for some complex diseases, it is still possible to identify a high proportion of false positives because of the modest differential expressions of disease related genes and the systematic noises of microarrays. The main purpose of this study is to develop statistical methods for Affymetrix microarray gene expression data so that the impact on false positives from non-expressed genes can be reduced. I proposed two novel generalized F-statistics for identifying differentially expressed genes and a novel approach for estimating adjusting factors. The proposed statistical methods systematically combine filtering of non-expressed genes and identification of differentially expressed genes. For comparison, the discussed statistical methods were applied to an experimental data set for a type 2 diabetes study. In both two- and three-sample analyses, the proposed statistics showed improvement on the control of false positives.     

Identification of molecular markers for the oncogenic differentiation of hepatocellular carcinoma

The aim of this study was to identify molecular markers associated with oncogenic differentiation in hepatocellular carcinoma (HCC). Using an unsupervised clustering method with a cDNA microarray, HCC (T) gene expression profiles and corresponding non-tumor tissues (NT) from 40 patients were analyzed. Of total 217 genes, 72 were expressed preferentially in HCC tissues. Among 186 differentially regulated genes, there were molecular chaperone and tumor suppressor gene clusters in the Edmondson grades I and II (GI/II) subclass compared with the liver cirrhosis (LC) subclass. The Edmondson grades III and IV (GIII/IV) subclass with a poor survival (P=0.0133) contained 122 differentially regulated genes with a cluster containing various metastasis- and invasion-related genes compared with the GI/II subclass. Immunohistochemical analysis revealed that ANXA2, one of the 72 genes preferentially expressed in HCC, was over-expressed in the sinusoidal endothelium and in malignant hepatocytes in HCC. The genes identified in the HCC subclasses will be useful molecular markers for the genesis and progression of HCC. In addition, ANXA2 might be a novel marker for tumor angiogenesis in HCC.     

Study of mRNA expression of Arabidopsis thaliana under stimulation using modified differential display RT-PCR with silver staining

We studied differential expressions of Arabidopsis thaliana seedlings under sound stimulation using modified differential display RT-PCR with silver staining in this paper. Six differentially expressed cDNA fragments were obtained. Molecular weight of fragments was between 200 and 600 bp, respectively. On the base of the experimentation, we considered sound stress would have been a positive or negative influence on plant growth. The experimental results indicated some genes was activated, meanwhile some were restrained under after stimulation under sound stress.     

A Gibbs sampling method to determine biomarkers for asthma

PurposeTo identify potential biomarkers and to uncover the mechanisms underlying asthma based on Gibbs sampling.MethodsThe molecular functions (MFs) with genes greater than 5 were determined using AnnotationMFGO of BAGS package, and the obtained MFs were then transformed to Markov chain (MC). Gibbs sampling was conducted to obtain a new MC. Meanwhile, the average probabilities of MFs were computed via MC Monte Carlo (MCMC) algorithm, followed by identification of differentially expressed MFs based on the probabilities of MF more than 0.6. Moreover, the differentially expressed genes (DEGs) and their correlated genes were screened and merged, called as co-expressed genes. Pathways enrichment analysis was implemented for the co-expressed genes.ResultsBased on the gene set more than 5, overall 396 MFs were determined. After Gibbs sampling, 5 differentially expressed MF were acquired according to alfa.pi > 0.6. Moreover, the genes in these 5 differentially expressed MF were merged, and 110 DEGs were identified. Subsequently, 338 co-expressed genes were gained. Based on the P value < 0.01, the co-expressed genes were significantly enriched in 6 pathways. Among these, ubiquitin mediated proteolysis contained the maximum numbers of 35 co-expressed genes, and cell cycle were enriched by the second largest number of 11 co-expressed genes, respectively.ConclusionsThe identified pathways such as ubiquitin mediated proteolysis and cell cycle might play important roles in the development of asthma and may be useful for developing the credible therapeutic approaches for diagnosis and treatment of asthma in future.     

Improving the power to detect differentially expressed genes in comparative microarray experiments by including information from self-self hybridizations

Our ability to detect differentially expressed genes in a microarray experiment can be hampered when the number of biological samples of interest is limited. In this situation, we propose the use of information from self-self hybridizations to acuminate our inference of differential expression. A unified modelling strategy is developed to allow better estimation of the error variance. This principle is similar to the use of a pooled variance estimate in the two-sample t-test. The results from real dataset examples suggest that we can detect more genes that are differentially expressed in the combined models. Our simulation study provides evidence that this method increases sensitivity compared to using the information from comparative hybridizations alone, given the same control for false discovery rate. The largest increase in sensitivity occurs when the amount of information in the comparative hybridization is limited.     

Differentially expressed genes selection via Laplacian regularized low-rank representation method

With the rapid development of DNA microarray technology and next-generation technology, a large number of genomic data were generated. So how to extract more differentially expressed genes from genomic data has become a matter of urgency. Because Low-Rank Representation (LRR) has the high performance in studying low-dimensional subspace structures, it has attracted a chunk of attention in recent years. However, it does not take into consideration the intrinsic geometric structures in data.In this paper, a new method named Laplacian regularized Low-Rank Representation (LLRR) has been proposed and applied on genomic data, which introduces graph regularization into LRR. By taking full advantages of the graph regularization, LLRR method can capture the intrinsic non-linear geometric information among the data. The LLRR method can decomposes the observation matrix of genomic data into a low rank matrix and a sparse matrix through solving an optimization problem. Because the significant genes can be considered as sparse signals, the differentially expressed genes are viewed as the sparse perturbation signals. Therefore, the differentially expressed genes can be selected according to the sparse matrix. Finally, we use the GO tool to analyze the selected genes and compare the P-values with other methods.The results on the simulation data and two real genomic data illustrate that this method outperforms some other methods: in differentially expressed gene selection.     

Considering Dependencies Amongst Genes Helps to Adjust the Significance Rank of DEGs

Lists of differentially expressed genes (DEGs) detected often show low reproducibility even in technique replicate experiments. The reproducibility is even lower for those real cancer data with large biological variations and limited number of samples. Since existing methods for identifying differentially expressed genes treat each gene separately, they cannot circumvent the problem of low reproducibility. Considering correlation structures of genes may help to mitigate the effect of errors on individual gene estimates and thus get more reliable lists of DEGs. We borrowed information from large amount of existing microarray data to define the expression dependencies amongst genes. We use this prior knowledge of dependencies amongst genes to adjust the significance rank of DEGs. We applied our method and four popular ranking algorithms including mean fold change (FC), SAM, t‐statistic and Wilcoxon rank sum‐test on two cancer microarray datasets. Our method achieved higher reproducibility than other methods across a range of sample sizes. Furthermore, our method obtained higher accuracy than other methods, especially when the sample size is small. The results demonstrate that considering the dependencies amongst genes helps to adjust the significance rank of genes and find those truly differentially expressed genes.