基因表达数据在肿瘤诊断、基因功能预测中的应用研究
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摘要
前言
后基因组时代应用了大量高通量方法,由此产生了海量的基因表达数据。可靠准确的分类对于癌症的诊断和治疗至关重要。微阵列的使用可以同时检测每个样本的上千个基因表达,不但为客观准确的肿瘤分类提供了可能,而且也为临床医生选择适当形式的治疗提供数据支持。
基因表达数据通常存在基因个数远远大于观察例数的情形,传统的统计分析方法有时失效,因此有必要分析何时用何法才能获取最有用的信息。虽然已有研究分析特征基因选择方法并用于肿瘤分类,然而大部分集中于一个方法或单个数据库,并缺乏统计学基础。因此,有必要使用多个数据库对各种方法的性能进行系统比较与分析。
随着基因组及后基因组计划的不断开展,越来越多的生物信息被人类不断获得。合理的利用这些信息不但能有效的抑制噪声的影响,也能够避免单纯根据单独实验获得的片面信息,但是只有较少的文献意识到先验信息的重要性。
聚类分析是一种有效的数据分析工具,已有研究表明参与同一个生物过程的基因具有相同的功能,因此对基因表达数据的聚类分析成为基因功能预测的一种主要方法。然而在聚类分析中,大部分现有方法都忽视了基因的已知功能。随着基因注释数据库的不断完善,尤其是当数据存在噪声时,在聚类过程中整合已知基因功能不失为一个明智之举。在聚类分析中通常是需要先定义基因表达距离,然后再根据此测量距离将基因聚类。如果这个距离单纯从生物实验出发,并没有考虑已有的先验知识,因此得到的距离就不全面、准确。
目的
选择合适的特征基因,比较不同方法在基因表达数据肿瘤分类中的优劣;在肿瘤基因表达数据中加入先验信息,提高肿瘤分类准确性;结合已知的生物学功能,提高基因表达聚类分析的准确性和解释性。
方法
本研究使用五个经典的基因表达数据库,分别包括二分类肺癌、结肠癌、多分类肺癌、儿童期肿瘤和脑肿瘤。分别采用最近收缩质心法(PAM),收缩质心的调整判别分析(SCRDA)和多重比较方法(MTP)选择特征基因,再分别利用所得到的特征基因集进行判别分析,判别分析方法包括:K近邻法(KNN)、线性判别分析(LDA)、C-分类支持向量机(C-SVM)、收缩线性判别分析(SLDA)、收缩对角判别分析(SDDA)、最近收缩质心法(PAM)、收缩质心的调整判别分析(SCRDA)和BP人工神经网络(BP-ANN)。
本研究使用恶性胸膜间皮瘤和肺腺癌基因表达数据库,通过检索CancerResearch杂志报道的部分有关肺腺癌的基因,获得这些基因在原始数据集中的位置,并进行MTP检验,剔除不显著基因,保留显著基因,再分别与PAM和SCRDA方法获得的显著基因共同组成特征基因集,然后利用所得到的特征基因集进行判别分析。
利用积累的基因功能关系,我们提出将已知基因的功能加入一个新的距离矩阵。这个新距离等于测量距离和功能距离之和。算法分为两步进行;第一步,在基于距离的聚类分析(如K-中心或系统聚类)中使用新距离。第二步,将上一步的聚类结果用于功能未知的基因功能预测,判断其是具有已知的功能,还是具有新功能。
结果
当基因个数多于样本个数时,传统LDA无法正常执行。从二分类与多分类数据来看,SCRDA选择出的基因个数明显多于PAM选择出的基因个数;SDA、PAM和SCRDA的准确率高于传统LDA方法;在机器学习方法中,SVM的准确率高于BP-ANN;使用全部基因与部分基因相比,KNN准确率有所下降。
对于利用PAM和SCRDA方法获得基因集后再结合先验信息的分类方法中,只有少数方法的检验集分类准确率没有得到提高,其它方法都有一定提高,除了PCR等少数方法外,训练集的分类准确率都得到提高,相应的标准差也随之降低。
模拟试验和对于酵母菌数据的研究证实整合功能距离方法比标准方法更有效。
结论
本研究发现特征基因的选择对于分类方法具有一定影响,PAM方法使用的特征基因的数目一般要小于SCRDA方法,而后者又要小于MTP方法。
改进的判别方法,尤其是SLDA在肿瘤分类判别方面具有良好的表现,优于传统LDA,各改进方法间差别并不明显。在机器学习方法中,SVM好于BP-ANN,但是需要注意核函数及参数的选取。
在判别分析中加入先验信息能够有效提高判别分析能力,降低基因表达数据中噪声的影响,这种思想无论在方法学上还是在实践上都具有实际应用前景。
基因表达中结合生物学功能在一定程度上能够提高基因表达聚类分析的准确性和解释性,具有一定实际应用意义。
Introduction
The postgenomic era has led to a multitude of high-throughput methodologies that generate massive volumes of gene expression data.Microarrays are capable of determining the expression levels of thousands of genes simultaneously and have greatly facilitated the discovery of new biological knowledge.Microarray experiments may lead to a more complete understanding of the molecular variations among tumors and hence to a more accurate and informative classification.However,this kind of knowledge is often difficult to grasp,and turing raw microarray data into biological understanding is by no means a simple task.Even a simple,small-scale,microarray experiment generates thousands to millions of data points.
One feature of microarray data is that the number of tumor samples collected tends to be much smaller than the number of genes.The number for the former tends to be on the order of tens or hundreds,while microarray data typically contain thousands of genes on each chip.In statistical terms,it is called 'large p,small n' problem,i.e.the number of predictor variables is much larger than the number of samples.Thus, microarrays present new challenge for statistical methods.Traditional statistic methodologies in classification or prediction do not work well when the number of variables p(genes) far too exceeds the number of samples n.So,appropriate choice of existing statistical methodologies or development of new methodologies is needed for the analysis of gene expression microarray data.
A reliable and precise classification of tumors is essential for successful diagnosis and treatment of cancer.Gene expression microarrays have provided the high-throughput platform to discover genomic biomarkers for cancer diagnosis and prognosis.Many gene expression signatures have been identified in recent years for accurate classification of tumor subtypes or for prognosis of patient survival outcome. Current methods to help classifying human malignancies mostly rely on a variety of feature selection methods and classifiers for selecting informative genes.Many previous studies focused on one method or single dataset.Cancer is not a single disease, there are many different kinds of cancer,arising in different organs and tissues through the accumulated mutation of multiple genes.Evaluation of the most commonly employed methods may give more accurate results if it is based on the collection of multiple databases from the statistical point of view.
Rational use of the available bioinformation can not only effectively remove or suppress noise in gene chips,but also avoid one-sided results of separate experiment. However,only some studies have been aware of the importance of priori information in tumor classification.Together with the application of discriminant techniques,we propose one method that incorporates prior knowledge into tumor classification based on gene expression data.The main problem is how to incorporate prior biological knowledge and where to get it from.For the purposes of this study,prior knowledge is any information about lung adenocarcinoma related genes that have been confirmed in literature.Prior knowledge is viewed here as a means of directing the classifier using known lung adenocarcinoma genes.
Clustering analysis was first applied to microarray analysis in the late 1990s,and has become an increasingly important tool for gene expression analysis.Because co-expressed genes are likely to share the same biological function,cluster analysis of gene expression profiles has been applied for gene function discovery.Cluster diagrams display the "terminal branches" as a list of genes that share similar behavior across multiple experiments.Most clustering analyses work by first defining a distance metric based on a biological network,either a pathway or GO.The distance is not complete and accurate if ignoring enough prior knowledge.In general,with relatively high noise levels of genomic data,it is recognized that incorporating biological knowledge into statistical analysis is a reliable way to maximize statistical efficiency and enhance the interpretability of analysis results.
Objectives
In the present study,we evaluated most commonly employed discriminant methods and explored their features and application in order to improve accuracy for tumor/tissue classification.We then incorporated prior biological knowledge into tumor classification to improve accuracy for tumor/tissue classification.We presented a new methodology that combines prior knowledge about gene functions and cluster analysis for analyzing gene expression data in order to improve the accuracy and explanation validity of cluster results.
Methods
The performances of several popular discrimination methods for gene expression data were studied with five publicly available cancer microarray datasets.Nearest shrunken centroid method(PAM),shrunken centroids regularized discriminant analysis (SCRDA) and multiple testing procedure(MTP) were used for feature gene selection, the methods of classification included K nearest-neighbor classifiers(KNN),linear discriminant analysis(LDA),SCRDA,PAM,C-classification support vector machine(C-SVM),shrinkage linear discriminant analysis(SLDA),shrinkage diagonal discriminant analysis(SDDA) and back-propagation artificial neural network(BP-ANN).The five publicly available cancer microarray datasets were(1) MPM &ADCA,(2) colon,(3) multi-class lung cancer(4) multi-class children cancer, (5) multi-class brain cancer.The performances of the above mentioned discrimination methods for significant gene selection were also studied.
A public well-known dataset,Malignant pleural mesothelioma and lung cancer gene expression database,was used in this study.Information about genes which are associated with lung adenocarcinoma was retrieved from the journal entitled "Cancer Research".The location and expression level of these genes in database were gotten, differential expression was analyzed by multiple t test.Genes with significance were retained,feature(gene) set by combining gene from PAM or RDA gene selection method was constructed,and then the feature(gene) set was used for later discriminant analysis.The methods included K nearest-neighbor classifiers(KNN),linear discriminant analysis(LDA),quadratic discriminant analysis(QDA),shrunken centroids regularized discriminant analysis(SCRDA),nearest shrunken centroid method(PAM), partial least square(PLS),generalized partial least squares(GPLS),principal component regression(PCR),ridge regression(RR),C-classification support vector machine(C-SVM),shrinkage linear discriminant analysis(SLDA),shrinkage diagonal discriminant analysis(SDDA) and back-propagation artificial neural network (BP-ANN).
To take advantage of accumulating gene functional annotations,we proposed incorporating known gene functions into a new distance metric,which equals the sum of the measure distance and biological distance.A two-step procedure was used,first, the shrinkage distance metric was used in any distance-based clustering method,e.g. K-medoids or hierarchical clustering,to cluster the genes with known functions. Second,while keeping the clustering results from the first step for the genes with known functions,the expression-based distance metric was used to cluster the remaining genes of unknown function,assigning each of them to either one of the clusters obtained in the first step or some new clusters.The above procedures were performed by software R 2.80(R foundation for Statistical Computer,Vienna,Austria).
Results
Conventional method-LDA could not work when the number of genes was more than sample size.The SCRDA used much more genes than PAM for all cancer datasets.
When comparing the performance of classifiers in two-class and multi-class diagnosis problem,SDA,SCRDA and PAM all had better classification accuracy and stability than LDA.SVM got higher accuracy than BP-ANN.Performance of KNN declined obviously when the use of feature(gene) selection was compared with that of all genes.
Compared with conventional methods,the performance of new method improved more or less except several special cases.Average accuracy of new method improved in training and test set when compared with conventional methods in most cases,while the standard deviations of new method were usually less than those of conventional method.
A simulation study and an application to gene function prediction for the yeast demonstrated the advantage of our proposal over the standard method.
Conclusions
Variable selection did have impact on the performance of the classifiers,especially on KNN.There existed obvious differences for gene selection between PAM,SCRDA and MTP.PAM selected fewer genes than SCRDA and SCRDA selected fewer genes than MTP.Regularized discriminant method,especially SLDA was superior to conventional LDA.While given the same genes,performance of PAM,SCRDA and SDA had no difference at all.SVM showed better performance than BP-ANN in some circumstances,while selection of kernel and parameter should be paid more attention.
The method that incorporated prior knowledge into discriminant analysis could effectively improve the capacity and reduce the impact of noise.This idea may have good future not only in practice but also in methodology.
The accuracy and explanation validity of cluster results for gene expression profiling could be improved by combining prior knowledge,it will have a good future of application.
后基因组时代应用了大量高通量方法,由此产生了海量的基因表达数据。可靠准确的分类对于癌症的诊断和治疗至关重要。微阵列的使用可以同时检测每个样本的上千个基因表达,不但为客观准确的肿瘤分类提供了可能,而且也为临床医生选择适当形式的治疗提供数据支持。
基因表达数据通常存在基因个数远远大于观察例数的情形,传统的统计分析方法有时失效,因此有必要分析何时用何法才能获取最有用的信息。虽然已有研究分析特征基因选择方法并用于肿瘤分类,然而大部分集中于一个方法或单个数据库,并缺乏统计学基础。因此,有必要使用多个数据库对各种方法的性能进行系统比较与分析。
随着基因组及后基因组计划的不断开展,越来越多的生物信息被人类不断获得。合理的利用这些信息不但能有效的抑制噪声的影响,也能够避免单纯根据单独实验获得的片面信息,但是只有较少的文献意识到先验信息的重要性。
聚类分析是一种有效的数据分析工具,已有研究表明参与同一个生物过程的基因具有相同的功能,因此对基因表达数据的聚类分析成为基因功能预测的一种主要方法。然而在聚类分析中,大部分现有方法都忽视了基因的已知功能。随着基因注释数据库的不断完善,尤其是当数据存在噪声时,在聚类过程中整合已知基因功能不失为一个明智之举。在聚类分析中通常是需要先定义基因表达距离,然后再根据此测量距离将基因聚类。如果这个距离单纯从生物实验出发,并没有考虑已有的先验知识,因此得到的距离就不全面、准确。
目的
选择合适的特征基因,比较不同方法在基因表达数据肿瘤分类中的优劣;在肿瘤基因表达数据中加入先验信息,提高肿瘤分类准确性;结合已知的生物学功能,提高基因表达聚类分析的准确性和解释性。
方法
本研究使用五个经典的基因表达数据库,分别包括二分类肺癌、结肠癌、多分类肺癌、儿童期肿瘤和脑肿瘤。分别采用最近收缩质心法(PAM),收缩质心的调整判别分析(SCRDA)和多重比较方法(MTP)选择特征基因,再分别利用所得到的特征基因集进行判别分析,判别分析方法包括:K近邻法(KNN)、线性判别分析(LDA)、C-分类支持向量机(C-SVM)、收缩线性判别分析(SLDA)、收缩对角判别分析(SDDA)、最近收缩质心法(PAM)、收缩质心的调整判别分析(SCRDA)和BP人工神经网络(BP-ANN)。
本研究使用恶性胸膜间皮瘤和肺腺癌基因表达数据库,通过检索CancerResearch杂志报道的部分有关肺腺癌的基因,获得这些基因在原始数据集中的位置,并进行MTP检验,剔除不显著基因,保留显著基因,再分别与PAM和SCRDA方法获得的显著基因共同组成特征基因集,然后利用所得到的特征基因集进行判别分析。
利用积累的基因功能关系,我们提出将已知基因的功能加入一个新的距离矩阵。这个新距离等于测量距离和功能距离之和。算法分为两步进行;第一步,在基于距离的聚类分析(如K-中心或系统聚类)中使用新距离。第二步,将上一步的聚类结果用于功能未知的基因功能预测,判断其是具有已知的功能,还是具有新功能。
结果
当基因个数多于样本个数时,传统LDA无法正常执行。从二分类与多分类数据来看,SCRDA选择出的基因个数明显多于PAM选择出的基因个数;SDA、PAM和SCRDA的准确率高于传统LDA方法;在机器学习方法中,SVM的准确率高于BP-ANN;使用全部基因与部分基因相比,KNN准确率有所下降。
对于利用PAM和SCRDA方法获得基因集后再结合先验信息的分类方法中,只有少数方法的检验集分类准确率没有得到提高,其它方法都有一定提高,除了PCR等少数方法外,训练集的分类准确率都得到提高,相应的标准差也随之降低。
模拟试验和对于酵母菌数据的研究证实整合功能距离方法比标准方法更有效。
结论
本研究发现特征基因的选择对于分类方法具有一定影响,PAM方法使用的特征基因的数目一般要小于SCRDA方法,而后者又要小于MTP方法。
改进的判别方法,尤其是SLDA在肿瘤分类判别方面具有良好的表现,优于传统LDA,各改进方法间差别并不明显。在机器学习方法中,SVM好于BP-ANN,但是需要注意核函数及参数的选取。
在判别分析中加入先验信息能够有效提高判别分析能力,降低基因表达数据中噪声的影响,这种思想无论在方法学上还是在实践上都具有实际应用前景。
基因表达中结合生物学功能在一定程度上能够提高基因表达聚类分析的准确性和解释性,具有一定实际应用意义。
Introduction
The postgenomic era has led to a multitude of high-throughput methodologies that generate massive volumes of gene expression data.Microarrays are capable of determining the expression levels of thousands of genes simultaneously and have greatly facilitated the discovery of new biological knowledge.Microarray experiments may lead to a more complete understanding of the molecular variations among tumors and hence to a more accurate and informative classification.However,this kind of knowledge is often difficult to grasp,and turing raw microarray data into biological understanding is by no means a simple task.Even a simple,small-scale,microarray experiment generates thousands to millions of data points.
One feature of microarray data is that the number of tumor samples collected tends to be much smaller than the number of genes.The number for the former tends to be on the order of tens or hundreds,while microarray data typically contain thousands of genes on each chip.In statistical terms,it is called 'large p,small n' problem,i.e.the number of predictor variables is much larger than the number of samples.Thus, microarrays present new challenge for statistical methods.Traditional statistic methodologies in classification or prediction do not work well when the number of variables p(genes) far too exceeds the number of samples n.So,appropriate choice of existing statistical methodologies or development of new methodologies is needed for the analysis of gene expression microarray data.
A reliable and precise classification of tumors is essential for successful diagnosis and treatment of cancer.Gene expression microarrays have provided the high-throughput platform to discover genomic biomarkers for cancer diagnosis and prognosis.Many gene expression signatures have been identified in recent years for accurate classification of tumor subtypes or for prognosis of patient survival outcome. Current methods to help classifying human malignancies mostly rely on a variety of feature selection methods and classifiers for selecting informative genes.Many previous studies focused on one method or single dataset.Cancer is not a single disease, there are many different kinds of cancer,arising in different organs and tissues through the accumulated mutation of multiple genes.Evaluation of the most commonly employed methods may give more accurate results if it is based on the collection of multiple databases from the statistical point of view.
Rational use of the available bioinformation can not only effectively remove or suppress noise in gene chips,but also avoid one-sided results of separate experiment. However,only some studies have been aware of the importance of priori information in tumor classification.Together with the application of discriminant techniques,we propose one method that incorporates prior knowledge into tumor classification based on gene expression data.The main problem is how to incorporate prior biological knowledge and where to get it from.For the purposes of this study,prior knowledge is any information about lung adenocarcinoma related genes that have been confirmed in literature.Prior knowledge is viewed here as a means of directing the classifier using known lung adenocarcinoma genes.
Clustering analysis was first applied to microarray analysis in the late 1990s,and has become an increasingly important tool for gene expression analysis.Because co-expressed genes are likely to share the same biological function,cluster analysis of gene expression profiles has been applied for gene function discovery.Cluster diagrams display the "terminal branches" as a list of genes that share similar behavior across multiple experiments.Most clustering analyses work by first defining a distance metric based on a biological network,either a pathway or GO.The distance is not complete and accurate if ignoring enough prior knowledge.In general,with relatively high noise levels of genomic data,it is recognized that incorporating biological knowledge into statistical analysis is a reliable way to maximize statistical efficiency and enhance the interpretability of analysis results.
Objectives
In the present study,we evaluated most commonly employed discriminant methods and explored their features and application in order to improve accuracy for tumor/tissue classification.We then incorporated prior biological knowledge into tumor classification to improve accuracy for tumor/tissue classification.We presented a new methodology that combines prior knowledge about gene functions and cluster analysis for analyzing gene expression data in order to improve the accuracy and explanation validity of cluster results.
Methods
The performances of several popular discrimination methods for gene expression data were studied with five publicly available cancer microarray datasets.Nearest shrunken centroid method(PAM),shrunken centroids regularized discriminant analysis (SCRDA) and multiple testing procedure(MTP) were used for feature gene selection, the methods of classification included K nearest-neighbor classifiers(KNN),linear discriminant analysis(LDA),SCRDA,PAM,C-classification support vector machine(C-SVM),shrinkage linear discriminant analysis(SLDA),shrinkage diagonal discriminant analysis(SDDA) and back-propagation artificial neural network(BP-ANN).The five publicly available cancer microarray datasets were(1) MPM &ADCA,(2) colon,(3) multi-class lung cancer(4) multi-class children cancer, (5) multi-class brain cancer.The performances of the above mentioned discrimination methods for significant gene selection were also studied.
A public well-known dataset,Malignant pleural mesothelioma and lung cancer gene expression database,was used in this study.Information about genes which are associated with lung adenocarcinoma was retrieved from the journal entitled "Cancer Research".The location and expression level of these genes in database were gotten, differential expression was analyzed by multiple t test.Genes with significance were retained,feature(gene) set by combining gene from PAM or RDA gene selection method was constructed,and then the feature(gene) set was used for later discriminant analysis.The methods included K nearest-neighbor classifiers(KNN),linear discriminant analysis(LDA),quadratic discriminant analysis(QDA),shrunken centroids regularized discriminant analysis(SCRDA),nearest shrunken centroid method(PAM), partial least square(PLS),generalized partial least squares(GPLS),principal component regression(PCR),ridge regression(RR),C-classification support vector machine(C-SVM),shrinkage linear discriminant analysis(SLDA),shrinkage diagonal discriminant analysis(SDDA) and back-propagation artificial neural network (BP-ANN).
To take advantage of accumulating gene functional annotations,we proposed incorporating known gene functions into a new distance metric,which equals the sum of the measure distance and biological distance.A two-step procedure was used,first, the shrinkage distance metric was used in any distance-based clustering method,e.g. K-medoids or hierarchical clustering,to cluster the genes with known functions. Second,while keeping the clustering results from the first step for the genes with known functions,the expression-based distance metric was used to cluster the remaining genes of unknown function,assigning each of them to either one of the clusters obtained in the first step or some new clusters.The above procedures were performed by software R 2.80(R foundation for Statistical Computer,Vienna,Austria).
Results
Conventional method-LDA could not work when the number of genes was more than sample size.The SCRDA used much more genes than PAM for all cancer datasets.
When comparing the performance of classifiers in two-class and multi-class diagnosis problem,SDA,SCRDA and PAM all had better classification accuracy and stability than LDA.SVM got higher accuracy than BP-ANN.Performance of KNN declined obviously when the use of feature(gene) selection was compared with that of all genes.
Compared with conventional methods,the performance of new method improved more or less except several special cases.Average accuracy of new method improved in training and test set when compared with conventional methods in most cases,while the standard deviations of new method were usually less than those of conventional method.
A simulation study and an application to gene function prediction for the yeast demonstrated the advantage of our proposal over the standard method.
Conclusions
Variable selection did have impact on the performance of the classifiers,especially on KNN.There existed obvious differences for gene selection between PAM,SCRDA and MTP.PAM selected fewer genes than SCRDA and SCRDA selected fewer genes than MTP.Regularized discriminant method,especially SLDA was superior to conventional LDA.While given the same genes,performance of PAM,SCRDA and SDA had no difference at all.SVM showed better performance than BP-ANN in some circumstances,while selection of kernel and parameter should be paid more attention.
The method that incorporated prior knowledge into discriminant analysis could effectively improve the capacity and reduce the impact of noise.This idea may have good future not only in practice but also in methodology.
The accuracy and explanation validity of cluster results for gene expression profiling could be improved by combining prior knowledge,it will have a good future of application.
引文
1 Campoli M,Ferrone S.HLA antigen changes in malignant cells:epigenetic mechanisms and biologic significance.Oncogene.2008;27(45):5869-5885.
2 Dudoit S,Fridlyand J,Speed TP.Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data.J Am Stat Assoc.2002;97(457):77-87.
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