弥漫大B细胞淋巴瘤1号染色体的基因表达分析

目的:探讨弥漫大B细胞淋巴瘤(Diffuse Large B-Cell Lymphoma,DLBCL)中1号染色体基因表达情况。方法:采用激光显微切割技术分离临床DLBCL病人淋巴结标本中的淋巴细胞,提取淋巴细胞的mRNA并与表达谱芯片杂交,通过信号扫描、处理后获得表达基因杂交信号强度。每基因设11-20对探针。杂交信号与错配探针对比,扣除背景值后,使用Wilcoxon符号秩和检验选取与错配杂交信号有显著差异的基因作为分析结果(P=0.05)。然后随机选取四个检测到的基因,使用PCR方法检验基因芯片结果的可靠性。结果:成功地从快速冷冻保存的DLBCL标本中提取RNA。使用表达谱芯片进行研究,发现了共316条1号染色体编码的基因在DLBCL细胞中表达。根据胞内定位,基因功能和基因所属的代谢通路三种分类方法对所得基因进行分类分析。基因表达密度分析显示DLBCL中1号染色体上的基因表达情况与编码基因分布情况存在统计学差异。结论:使用表达谱芯片研究了DLBCL中1号染色体上的基因表达情况。     

基于SNP共表达网络肝癌分子分型及预后分析

基于SNP突变数据与mRNA表达谱关联分析,构建一种肝癌分子分型方法并对比不同分型预后的差异,并对不同分型肝癌的发生发展机制进一步研究。首先通过TCGA数据库收集359例肝细胞癌患者的SNP突变数据和mRNA表达数据,采用Wilcoxon秩和检验,筛选突变后差异表达基因,并通过生物信息学工具String和Cytoscape构建差异表达基因的蛋白互作网络,筛选连接度最高的10个Hub基因。利用Consensus Cluster Plus软件包,基于Hub基因mRNA表达水平构建NMF分子分型模型,再结合生存数据评估各分型患者的预后。最后利用加权基因共表达网络分析(WGCNA),识别与肝癌分子分型相关的模块,并针对关键模块的基因进行通路富集,从而对不同分型肝癌的基因表达谱进行比较。结果：NMF模型将肝癌分为高危、低危2个分型,其中CDKN2A和FOXO1基因对分型贡献度高。生存分析显示低危组患者的生存情况显著优于高危组,高危组富集多个与肿瘤细胞侵蚀、转移、复发过程相关的信号通路,低危组则与细胞周期和胰液分泌相关。本研究在无先验性信息的前提下,基于突变后显著差异表达的Hub基因表达水平构建的...     

弥漫大B 淋巴瘤12 号染色体基因表达的研究

目的:研究弥漫大B淋巴瘤(Diffuse Large B-Cell Lymphoma,DLBCL)12号染色体基因表达情况。方法:收取临床DLBCL病人淋巴结标本液氮速冻,快速冷冻切片,采用激光显微切割技术分离单纯淋巴瘤细胞,提取淋巴瘤细胞中的mRNA与表达谱芯片杂交,通过信号扫描、处理后获得表达基因杂交信号强度。每基因设11-20对探针。杂交信号与错配探针对比,扣除背景值后,使用Wilcoxon符号秩和检验选取与错配杂交信号有显著差异的基因作为分析结果(P=0.05)。随机选取两个检测到的基因,使用PCR方法检验基因芯片结果的可靠性。结果:成功地从快速冷冻保存的DLBCL标本中提取了RNA。使用表达谱芯片进行研究,发现了共164条12号染色体编码的基因在淋巴瘤细胞中表达。并根据胞内定位,基因功能和基因所属的代谢通路三种分类方法对所得基因进行分类分析。基因表达密度分析显示12号染色体上的基因表达情况与编码基因分布情况比较一致。结论:使用表达谱芯片研究了12号染色体上的基因表达情况,为研究DLBCL提供了依据。     

常用的生物统计方法及其R语言实现

统计分析是科学研究中一个极其重要的环节.本文以昆虫学研究为实例,利用模拟数据,总结了14种常用的生物统计方法及其R语言实现,重点强调了如何根据科学问题和样本数据的具体情形选取合适的统计方法.这些统计方法包括可用于均值比较分析的符号检验、Wilcoxon符号秩检验、t-检验、Wilcoxon秩和检验、Kruskal-Wa...     

肿瘤表达谱基因芯片数据的混合效应模型分析

目的:研究混合效应模型(Mixed Effects Model)在肿瘤表达谱基因芯片数据分析中的检验效能,并探讨其分析效果。方法:采用混合效应模型分析肿瘤实例基因芯片数据,并以基因集富集分析方法(GSEA)作为参照比较分析结果的有效性和科学性,探讨其检验效果。结果:通过混合效应模型和基因集富集分析(GSEA)两种方法对肿瘤基因芯片数据的分析和比较,两种方法筛选出共同的差异表达通路外,混合效应模型额外地筛选出来GSEA未能检验到的8条差异表达通路,且得到文献支持;混和效应模型筛选出的前10个差异表达通路中有6个已有生物学证明而基因集富集分析方法(GSEA)筛选出的前10个差异表达通路中仅有4个已有生物学证明。结论:混合效应模型作为top-down方法中的典型代表,其优势在于通过构建潜变量达到降维目的,可有效地减少多个复杂的变异来源从而保证了结果的准确性和科学性,其检验效能优于基因集富集分析方法(GSEA),是一种行之有效的筛选肿瘤基因芯片数据的分析方法。     

GAGE和GSEA在基因集研究中的有效性比较

目的:研究在基因芯片数据分析中自限性原假设和竞争性原假设两类方法的优劣性和准确型,选取各自具有代表性的GAGE(Generally Applicable Gene-set Enrichment)和GSEA(Gene Set Enrichment Analysis)两种基因集分析方法筛选富集基因集的效能,并探讨其筛选效果.方法:采用两种待比较的方法在实际基因表达谱数据中分析研究,比较筛选结果的准确性和科学性,探讨两种方法筛选富集基因集的效果.结果:两方法对已知的基因表达谱数据进行应用分析表明GAGE的检验效能和筛选出的基因集生物学相关性均优于GSEA.结论:GAGE作为一种自限性原假设的基因集分析方法,由于其充分利用了表达谱数据,并将表达数据分为实验集和通路集分别进行分析处理,同时考虑到基因集的上调和下调,其检验效能优于竞争性原假设的GSEA,能够得到更为准确和科学的结果.     

基于表达谱芯片和下一代测序技术筛选先天性心脏病胎儿心肌组织差异表达的miRNA

目的 联合采用表达谱芯片和下一代测序技术同时高通量筛选先天性心脏病胎儿心肌组织表达差异的miRNA.方法 实验组为孕中期先天性畸形胎儿,对照组为同胎龄无心脏畸形的难免流产的胎儿,取胎儿心室心肌组织,联合采用Agilent Human 2.0 microRNAs表达谱芯片和SOLiD下一代测序技术同时观察心肌组织microRNA的表达变化,数据采用生物信息学方法进行分析,并用实时PCR方法验证芯片结果.结果 通过差异miRNA筛选,发现先天性心脏畸形组在表达谱芯片和下一代测序中共同差异的24个miRNA,生物信息学预测到1 606个靶基因,靶基因Gene Ontology分析表明其中与细胞进程、代谢过程、生物调控相关的靶基因为主,Pathway显著性分析表明,部分靶基因为生物信号通路中的关键因子;随机挑选共同表达差异的4个miRNA进行验证,结果表明定量PCR检测结果与芯片与下一代测序共同筛选结果基本相符.结论 这些在先天性心脏病中异常表达的miRNA为研究先天性心脏病分子水平上的发病机制提供了重要的线索,将有可能为心脏相关疾病的诊断和治疗提供新的靶点和研发新的药物.     

基于蒙特卡洛模拟分析不同基因集方法的效能

目的:探索差异表达基因集(DEGs)筛选的有效方法.方法:基于蒙特卡洛模拟比较Efron's GSA、SAFE、Globaltest、PCOT2等四种基因集方法在分析微阵列数据时的统计推断能力.结果:Globaltest和PCOT2两种基于模型构建的基因集方法在处理模拟微阵列数据时效能相当,Globaltest略优于PCOT2,而Effort's GSA、SAFE方法检验效能低下.结论:Globaltest是一种较有效的微阵列数据分析方法.     

卵巢癌相关长链非编码RNA的生物信息学挖掘

探讨卵巢癌中差异表达的长链非编码RNA,从NCBI基因芯片数据库GEO下载卵巢癌基因芯片数据GSE14407,用SAM软件输出差异表达的基因,对其进行重注释并筛选出其中的长链非编码RNA,用Gene Cluster和Tree View软件验证SAM软件分析结果。针对该芯片数据,筛出133个在卵巢癌中表达有差异的长链非编码RNA,其中112个上调,21个下调,且这些长链非编码RNA的表达倍数均2或0.5,差异有统计学意义(q值0.05)。用生物信息学方法挖掘普通基因芯片中卵巢癌相关长链非编码RNA是十分有效的方法,可为卵巢癌相关长链非编码RNA的探索提供新途径。     

从microarray时序表达数据识别周期表达基因

根据周期表达基因的周期性和峰值特点,提出了一种将microarray时序表达数据划分为若干个基因表达周期,并对周期内的峰值特点进行评估以识别周期表达基因的方法,能有效减小microarray实验时的噪声干扰。选取了三组广泛使用的时序表达数据和一组可靠的周期表达基因集合对该方法的效果进行了测试,并与三种典型的周期表达基因识别方法的效果进行了比较。该方法能有效地从各种microarray时序表达数据中识别周期表达基因。     

On the use of permutation in and the performance of a class of nonparametric methods to detect differential gene expression

MOTIVATION: Recently a class of nonparametric statistical methods, including the empirical Bayes (EB) method, the significance analysis of microarray (SAM) method and the mixture model method (MMM), have been proposed to detect differential gene expression for replicated microarray experiments conducted under two conditions. All the methods depend on constructing a test statistic Z and a so-called null statistic z. The null statistic z is used to provide some reference distribution for Z such that statistical inference can be accomplished. A common way of constructing z is to apply Z to randomly permuted data. Here we point our that the distribution of z may not approximate the null distribution of Z well, leading to possibly too conservative inference. This observation may apply to other permutation-based nonparametric methods. We propose a new method of constructing a null statistic that aims to estimate the null distribution of a test statistic directly. RESULTS: Using simulated data and real data, we assess and compare the performance of the existing method and our new method when applied in EB, SAM and MMM. Some interesting findings on operating characteristics of EB, SAM and MMM are also reported. Finally, by combining the idea of SAM and MMM, we outline a simple nonparametric method based on the direct use of a test statistic and a null statistic.     

Using weighted permutation scores to detect differential gene expression with microarray data

A class of nonparametric statistical methods, including a nonparametric empirical Bayes (EB) method, the Significance Analysis of Microarrays (SAM) and the mixture model method (MMM) have been proposed to detect differential gene expression for replicated microarray experiments. They all depend on constructing a test statistic, for example, a t-statistic, and then using permutation to draw inferences. However, due to special features of microarray data, using standard permutation scores may not estimate the null distribution of the test statistic well, leading to possibly too conservative inferences. We propose a new method of constructing weighted permutation scores to overcome the problem: posterior probabilities of having no differential expression from the EB method are used as weights for genes to better estimate the null distribution of the test statistic. We also propose a weighted method to estimate the false discovery rate (FDR) using the posterior probabilities. Using simulated data and real data for time-course microarray experiments, we show the improved performance of the proposed methods when implemented in MMM, EB and SAM.     

The clustering of regression models method with applications in gene expression data

Identification of differentially expressed genes and clustering of genes are two important and complementary objectives addressed with gene expression data. For the differential expression question, many "per-gene" analytic methods have been proposed. These methods can generally be characterized as using a regression function to independently model the observations for each gene; various adjustments for multiplicity are then used to interpret the statistical significance of these per-gene regression models over the collection of genes analyzed. Motivated by this common structure of per-gene models, we proposed a new model-based clustering method--the clustering of regression models method, which groups genes that share a similar relationship to the covariate(s). This method provides a unified approach for a family of clustering procedures and can be applied for data collected with various experimental designs. In addition, when combined with per-gene methods for assessing differential expression that employ the same regression modeling structure, an integrated framework for the analysis of microarray data is obtained. The proposed methodology was applied to two microarray data sets, one from a breast cancer study and the other from a yeast cell cycle study.     

The Baumgartner-Weiss-Schindler test for the detection of differentially expressed genes in replicated microarray experiments

MOTIVATION: An important application of microarray experiments is to identify differentially expressed genes. Because microarray data are often not distributed according to a normal distribution nonparametric methods were suggested for their statistical analysis. Here, the Baumgartner-Weiss-Schindler test, a novel and powerful test based on ranks, is investigated and compared with the parametric t-test as well as with two other nonparametric tests (Wilcoxon rank sum test, Fisher-Pitman permutation test) recently recommended for the analysis of gene expression data. RESULTS: Simulation studies show that an exact permutation test based on the Baumgartner-Weiss-Schindler statistic B is preferable to the other three tests. It is less conservative than the Wilcoxon test and more powerful, in particular in case of asymmetric or heavily tailed distributions. When the underlying distribution is symmetric the differences in power between the tests are relatively small. Thus, the Baumgartner-Weiss-Schindler is recommended for the usual situation that the underlying distribution is a priori unknown. AVAILABILITY: SAS code available on request from the authors.     

Pathway analysis using random forests classification and regression

MOTIVATION: Although numerous methods have been developed to better capture biological information from microarray data, commonly used single gene-based methods neglect interactions among genes and leave room for other novel approaches. For example, most classification and regression methods for microarray data are based on the whole set of genes and have not made use of pathway information. Pathway-based analysis in microarray studies may lead to more informative and relevant knowledge for biological researchers. RESULTS: In this paper, we describe a pathway-based classification and regression method using Random Forests to analyze gene expression data. The proposed methods allow researchers to rank important pathways from externally available databases, discover important genes, find pathway-based outlying cases and make full use of a continuous outcome variable in the regression setting. We also compared Random Forests with other machine learning methods using several datasets and found that Random Forests classification error rates were either the lowest or the second-lowest. By combining pathway information and novel statistical methods, this procedure represents a promising computational strategy in dissecting pathways and can provide biological insight into the study of microarray data. AVAILABILITY: Source code written in R is available from http://bioinformatics.med.yale.edu/pathway-analysis/rf.htm.     

Differential gene expression detection and sample classification using penalized linear regression models

Differential gene expression detection and sample classification using microarray data have received much research interest recently. Owing to the large number of genes p and small number of samples n (p > n), microarray data analysis poses big challenges for statistical analysis. An obvious problem owing to the 'large p small n' is over-fitting. Just by chance, we are likely to find some non-differentially expressed genes that can classify the samples very well. The idea of shrinkage is to regularize the model parameters to reduce the effects of noise and produce reliable inferences. Shrinkage has been successfully applied in the microarray data analysis. The SAM statistics proposed by Tusher et al. and the 'nearest shrunken centroid' proposed by Tibshirani et al. are ad hoc shrinkage methods. Both methods are simple, intuitive and prove to be useful in empirical studies. Recently Wu proposed the penalized t/F-statistics with shrinkage by formally using the (1) penalized linear regression models for two-class microarray data, showing good performance. In this paper we systematically discussed the use of penalized regression models for analyzing microarray data. We generalize the two-class penalized t/F-statistics proposed by Wu to multi-class microarray data. We formally derive the ad hoc shrunken centroid used by Tibshirani et al. using the (1) penalized regression models. And we show that the penalized linear regression models provide a rigorous and unified statistical framework for sample classification and differential gene expression detection.     

Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network

We propose a new statistical method for constructing a genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network construction is the estimation of the conditional distribution of each random variable. We consider fitting nonparametric regression models with heterogeneous error variances to the microarray gene expression data to capture the nonlinear structures between genes. Selecting the optimal graph, which gives the best representation of the system among genes, is still a problem to be solved. We theoretically derive a new graph selection criterion from Bayes approach in general situations. The proposed method includes previous methods based on Bayesian networks. We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae gene expression data newly obtained by disrupting 100 genes.     

Detecting differential gene expression with a semiparametric hierarchical mixture method

Mixture modeling provides an effective approach to the differential expression problem in microarray data analysis. Methods based on fully parametric mixture models are available, but lack of fit in some examples indicates that more flexible models may be beneficial. Existing, more flexible, mixture models work at the level of one-dimensional gene-specific summary statistics, and so when there are relatively few measurements per gene these methods may not provide sensitive detectors of differential expression. We propose a hierarchical mixture model to provide methodology that is both sensitive in detecting differential expression and sufficiently flexible to account for the complex variability of normalized microarray data. EM-based algorithms are used to fit both parametric and semiparametric versions of the model. We restrict attention to the two-sample comparison problem; an experiment involving Affymetrix microarrays and yeast translation provides the motivating case study. Gene-specific posterior probabilities of differential expression form the basis of statistical inference; they define short gene lists and false discovery rates. Compared to several competing methodologies, the proposed methodology exhibits good operating characteristics in a simulation study, on the analysis of spike-in data, and in a cross-validation calculation.