A hybrid SOM-SVM approach for the zebrafish gene expression analysis

Microarray technology can be employed to quantitatively measure the expression of thousands of genes in a single experiment. It has become one of the main tools for global gene expression analysis in molecular biology research in recent years. The large amount of expression data generated by this technology makes the study of certain complex biological problems possible, and machine learning methods are expected to play a crucial role in the analysis process. In this paper, we present our results from integrating the self-organizing map （SOM） and the support vector machine （SVM） for the analysis of the various functions of zebrafish genes based on their expression. The most distinctive characteristic of our zebrafish gene expression is that the number of samples of different classes is imbalanced. We discuss how SOM can be used as a data-filtering tool to improve the classification performance of the SVM on this data set.     

Bayesian Joint Analysis of Gene Expression Data and Gene Functional Annotations

Identifying which genes and which gene sets are differentially expressed (DE) under two experimental conditions are both key questions in microarray analysis. Although closely related and seemingly similar, they cannot replace each other, due to their own importance and merits in scientific discoveries. Existing approaches have been developed to address only one of the two questions. Further, most of the methods for detecting DE genes purely rely on gene expression analysis, without using the information about gene functional grouping. Methods for detecting altered gene sets often use a two-step procedure, of which the first step conducts differential expression analysis using expression data only, and the second step takes results from the first step and tries to examine whether each predefined gene set is overrepresented by DE genes through some testing procedure. Such a sequential manner in analysis might cause information loss by just focusing on summary results without using the entire expression data in the second step. Here, we propose a Bayesian joint modeling approach to address the two key questions in parallel, which incorporates the information of functional annotations into expression data analysis and meanwhile infer the enrichment of functional groups. Simulation results and analysis of experimental data obtained for E.?coli show improved statistical power of our integrated approach in both identifying DE genes and altered gene sets, when compared to conventional methods.     

基于基因差异表达的微弱信号识别疾病相关功能

当两组样本间基因表达的差异程度较低或样本量较少时,采用通常的错误发现率(falsediscovery rate,FDR)控制水平(如5%或10%),可能无法识别足够多的差异表达基因以进行后续的功能富集分析。然而,功能富集分析对差异表达基因中的错误发现具有一定的稳健性。所以,采用较低的FDR控制水平(即允许较高的FDR)识别差异表达基因,可能可以可靠地发现疾病相关功能。本文分析了5套研究乳腺癌转移的基因表达谱,通过其中差异表达信号较强的3套数据,论证了即使差异表达基因的FDR达到25%,功能富集分析的结果仍具有较高的稳健性。然后,在另外2套差异表达信号微弱的数据中,采用25%的FDR控制水平筛选差异表达基因来进行功能富集分析,并与前述3套数据的功能富集结果做比较。结果显示,采用较低的FDR控制水平筛选差异表达基因,仍然可以可靠地识别乳腺癌转移相关功能。分析结果也提示,在乳腺癌转移过程中,一些功能较为宽泛的生物学过程(如细胞分裂、细胞周期和DNA复制等)整体受到了扰动,反映出乳腺癌转移是一种涉及广泛基因表达改变的系统性疾病。     

Identifying Differentially Expressed Genes for Time-course Microarray Data through Functional Data Analysis

Identification of differentially expressed (DE) genes across two conditions is a common task with microarray. Most existing approaches accomplish this goal by examining each gene separately based on a model and then control the false discovery rate over all genes. We took a different approach that employs a uniform platform to simultaneously depict the dynamics of the gene trajectories for all genes and select differently expressed genes. A new Functional Principal Component (FPC) approach is developed for time-course microarray data to borrow strength across genes. The approach is flexible as the temporal trajectory of the gene expressions is modeled nonparametrically through a set of orthogonal basis functions, and often fewer basis functions are needed to capture the shape of the gene expression trajectory than existing nonparametric methods. These basis functions are estimated from the data reflecting major modes of variation in the data. The correlation structure of the gene expressions over time is also incorporated without any parametric assumptions and estimated from all genes such that the information across other genes can be shared to infer one individual gene. Estimation of the parameters is carried out by an efficient hybrid EM algorithm. The performance of the proposed method across different scenarios was compared favorably in simulation to two-way mixed-effects ANOVA and the EDGE method using B-spline basis function. Application to the real data on C. elegans developmental stages also suggested that FPC analysis combined with hybrid EM algorithm provides a computationally fast and efficient method for identifying DE genes based on time-course microarray data.     

Mining gene expression data for positive and negative co-regulated gene clusters

MOTIVATION: Analysis of gene expression data can provide insights into the positive and negative co-regulation of genes. However, existing methods such as association rule mining are computationally expensive and the quality and quantities of the rules are sensitive to the support and confidence values. In this paper, we introduce the concept of positive and negative co-regulated gene cluster (PNCGC) that more accurately reflects the co-regulation of genes, and propose an efficient algorithm to extract PNCGCs. RESULTS: We experimented with the Yeast dataset and compared our resulting PNCGCs with the association rules generated by the Apriori mining algorithm. Our results show that our PNCGCs identify some missing co-regulations of association rules, and our algorithm greatly reduces the large number of rules involving uncorrelated genes generated by the Apriori scheme. AVAILABILITY: The software is available upon request.     

Establishing Informative Prior for Gene Expression Variance from Public Databases

Identifying differential expressed genes across various conditions or genotypes is the most typical approach to studying the regulation of gene expression. An estimate of gene-specific variance is often needed for the assessment of statistical significance in most differential expression (DE) detection methods, including linear models (e.g., for transformed and normalized microarray data) and generalized linear models (e.g., for count data in RNAseq). Due to a common limit in sample size, the variance estimate is often unstable in small experiments. Shrinkage estimates using empirical Bayes methods have proven useful in improving the variance estimate, hence improving the detection of DE. The most widely used empirical Bayes methods borrow information across genes within the same experiments. In these methods, genes are considered exchangeable or exchangeable conditioning on expression level. We propose, with the increasing accumulation of expression data, borrowing information from historical data on the same gene can provide better estimate of gene-specific variance, thus further improve DE detection. Specifically, we show that the variation of gene expression is truly gene-specific and reproducible between different experiments. We present a new method to establish informative gene-specific prior on the variance of expression using existing public data, and illustrate how to shrink the variance estimate and detect DE. We demonstrate improvement in DE detection under our strategy compared to leading DE detection methods.     

Modeling nonlinearity in dilution design microarray data

MOTIVATION: Dilution design (Mixed tissue RNA) has been utilized by some researchers to evaluate and assess the performance of multiple microarray platforms. Current microarray data analysis approaches assume that the quantified signal intensities are linearly related to the expression of the corresponding genes in the sample. However, there are sources of nonlinearity in microarray expression measurements. Such nonlinearity study in the expressions of the RNA mixtures provides a new way to analyze gene expression data, and we argue that the nonlinearity can reveal novel information for microarray data analysis. Therefore, we proposed a statistical model, called proportion model, which is based on the linear regression analysis. To approximately quantify the nonlinearity in the dilution design, a new calibration, beta ratio (BR) was derived from the proportion model. Furthermore, a new adjusted fold change (adj-FC) was proposed to predict the true FC without nonlinearity, in particular for large FC. RESULTS: We applied our method to one microarray dilution dataset. The experimental results indicated that, to some extent, there are global biases comparing with the linear assumption for the significant genes. Further analysis of those highly expressed genes with significant nonlinearity revealed some promising results, e.g. 'poison' effect was discovered for some genes in RNA mixtures. The adj-FCs of those genes with 'poison' effect, indicate that the nonlinearity can be also caused by the inherent feature of the genes besides signal noise and technical variation. Moreover, when percentage of overlapping genes (POG) was used as a cross-platform consistency measure, adj-FC outperformed simple fold change to show that Affymetrix and Illumina platforms are consistent. AVAILABILITY: The R codes which implements all described methods, and some Supplementary material, are freely available from http://www.utdallas.edu/~ying.liu/BetaRatio.htm     

Identifying Differentially Expressed Genes in Time Course Microarray Data

Identifying differentially expressed (DE) genes across conditions or treatments is a typical problem in microarray experiments. In time course microarray experiments (under two or more conditions/treatments), it is sometimes of interest to identify two classes of DE genes: those with no time-condition interactions (called parallel DE genes, or PDE), and those with time-condition interactions (nonparallel DE genes, NPDE). Although many methods have been proposed for identifying DE genes in time course experiments, methods for discerning NPDE genes from the general DE genes are still lacking. We propose a functional ANOVA mixed-effect model to model time course gene expression observations. The fixed effect of (the mean curve) of the model decomposes bivariate functions of time and treatments (or experimental conditions) as in the classic ANOVA method and provides the associated notions of main effects and interactions. Random effects capture time-dependent correlation structures. In this model, identifying NPDE genes is equivalent to testing the significance of the time-condition interaction, for which an approximate F-test is suggested. We examined the performance of the proposed method on simulated datasets in comparison with some existing methods, and applied the method to a study of human reaction to the endotoxin stimulation, as well as to a cell cycle expression data set.     

Interval-valued analysis for discriminative gene selection and tissue sample classification using microarray data

An important application of gene expression data is to classify samples in a variety of diagnostic fields. However, high dimensionality and a small number of noisy samples pose significant challenges to existing classification methods. Focused on the problems of overfitting and sensitivity to noise of the dataset in the classification of microarray data, we propose an interval-valued analysis method based on a rough set technique to select discriminative genes and to use these genes to classify tissue samples of microarray data. We first select a small subset of genes based on interval-valued rough set by considering the preference-ordered domains of the gene expression data, and then classify test samples into certain classes with a term of similar degree. Experiments show that the proposed method is able to reach high prediction accuracies with a small number of selected genes and its performance is robust to noise.     

Analysis of Stop-Gain and Frameshift Variants in Human Innate Immunity Genes

Loss-of-function variants in innate immunity genes are associated with Mendelian disorders in the form of primary immunodeficiencies. Recent resequencing projects report that stop-gains and frameshifts are collectively prevalent in humans and could be responsible for some of the inter-individual variability in innate immune response. Current computational approaches evaluating loss-of-function in genes carrying these variants rely on gene-level characteristics such as evolutionary conservation and functional redundancy across the genome. However, innate immunity genes represent a particular case because they are more likely to be under positive selection and duplicated. To create a ranking of severity that would be applicable to innate immunity genes we evaluated 17,764 stop-gain and 13,915 frameshift variants from the NHLBI Exome Sequencing Project and 1,000 Genomes Project. Sequence-based features such as loss of functional domains, isoform-specific truncation and nonsense-mediated decay were found to correlate with variant allele frequency and validated with gene expression data. We integrated these features in a Bayesian classification scheme and benchmarked its use in predicting pathogenic variants against Online Mendelian Inheritance in Man (OMIM) disease stop-gains and frameshifts. The classification scheme was applied in the assessment of 335 stop-gains and 236 frameshifts affecting 227 interferon-stimulated genes. The sequence-based score ranks variants in innate immunity genes according to their potential to cause disease, and complements existing gene-based pathogenicity scores. Specifically, the sequence-based score improves measurement of functional gene impairment, discriminates across different variants in a given gene and appears particularly useful for analysis of less conserved genes.     

Ranking: a closer look on globalisation methods for normalisation of gene expression arrays

Data from gene expression arrays are influenced by many experimental parameters that lead to variations not simply accessible by standard quantification methods. To compare measurements from gene expression array experiments, quantitative data are commonly normalised using reference genes or global normalisation methods based on mean or median values. These methods are based on the assumption that (i) selected reference genes are expressed at a standard level in all experiments or (ii) that mean or median signal of expression will give a quantitative reference for each individual experiment. We introduce here a new ranking diagram, with which we can show how the different normalisation methods compare, and how they are influenced by variations in measurements (noise) that occur in every experiment. Furthermore, we show that an upper trimmed mean provides a simple and robust method for normalisation of larger sets of experiments by comparative analysis.     

Extensive up-regulation of gene expression in cancer: the normalised use of microarray data

Based on the assumption that only a few genes are differentially expressed in a disease and have balanced upward and downward expression level changes, researchers usually normalise microarray data by forcing all of the arrays to have the same probe intensity distributions to remove technical variations in the data. However, accumulated evidence suggests that gene expressions could be widely altered in cancer, so we need to evaluate the sensitivities of biological discoveries to violation of the normalisation assumption. Here, we show that the medians of the original probe intensities increase in most of the ten cancer types analyzed in this paper, indicating that genes may be widely up-regulated in many cancer types. Thus, at least for cancer study, normalising all arrays to have the same distribution of probe intensities regardless of the state (diseased vs. normal) tends to falsely produce many down-regulated differentially expressed (DE) genes while missing many truly up-regulated DE genes. We also show that the DE genes solely detected in the non-normalised data for cancers are highly reproducible across different datasets for the same cancers, indicating that effective biological signals naturally exist in the non-normalised data. Because the powers of current statistical analyses using the non-normalised data tend to be low, we suggest selecting DE genes in both normalised and non-normalised data and then filter out the false DE genes extracted from the normalised data that show opposite deregulation directions in the non-normalised data.     

微阵列数据揭示基因在癌样本中广泛表达

癌的发生与发展过程涉及大量基因的异常表达。在目前基因表达谱分析中采用的标准化方法通常假设在疾病中差异表达的基因的比例很小并且差异上、下调的比例大致相等。这个被研究者所广泛采用的标准化的前提假设尚未被充分地论证过。通过分析胰腺癌的两套表达谱数据,我们发现在胰腺癌样本中基因表达的中值显著高于正常样本,提示传统的标准化假设并不适用于胰腺癌表达谱数据。采用标准化数据会导致错误地判断大量的差异下调的基因并失查许多差异上调的基因。采用原始数据分析发现在胰腺癌中的基因表达有广泛上调的特征,为深入研究胰腺癌的发生和发展机制提供了新线索。     

Attribute clustering for grouping, selection, and classification of gene expression data

This paper presents an attribute clustering method which is able to group genes based on their interdependence so as to mine meaningful patterns from the gene expression data. It can be used for gene grouping, selection, and classification. The partitioning of a relational table into attribute subgroups allows a small number of attributes within or across the groups to be selected for analysis. By clustering attributes, the search dimension of a data mining algorithm is reduced. The reduction of search dimension is especially important to data mining in gene expression data because such data typically consist of a huge number of genes (attributes) and a small number of gene expression profiles (tuples). Most data mining algorithms are typically developed and optimized to scale to the number of tuples instead of the number of attributes. The situation becomes even worse when the number of attributes overwhelms the number of tuples, in which case, the likelihood of reporting patterns that are actually irrelevant due to chances becomes rather high. It is for the aforementioned reasons that gene grouping and selection are important preprocessing steps for many data mining algorithms to be effective when applied to gene expression data. This paper defines the problem of attribute clustering and introduces a methodology to solving it. Our proposed method groups interdependent attributes into clusters by optimizing a criterion function derived from an information measure that reflects the interdependence between attributes. By applying our algorithm to gene expression data, meaningful clusters of genes are discovered. The grouping of genes based on attribute interdependence within group helps to capture different aspects of gene association patterns in each group. Significant genes selected from each group then contain useful information for gene expression classification and identification. To evaluate the performance of the proposed approach, we applied it to two well-known gene expression data sets and compared our results with those obtained by other methods. Our experiments show that the proposed method is able to find the meaningful clusters of genes. By selecting a subset of genes which have high multiple-interdependence with others within clusters, significant classification information can be obtained. Thus, a small pool of selected genes can be used to build classifiers with very high classification rate. From the pool, gene expressions of different categories can be identified.