Analysis Of High-dimensional Genomic Data Research Articles

Selection probability of multivariate regularization to identify pleiotropic variants in genetic association studies

In genetic association studies, pleiotropy is a phenomenon where a variant or a genetic region affects multiple traits or diseases. There have been many studies identifying cross-phenotype genetic associations. But, most of statistical approaches for detection of pleiotropy are based on individual tests where a single variant association with multiple traits is tested one at a time. These approaches fail to account for relations among correlated variants. Recently, multivariate regularization methods have been proposed to detect pleiotropy in analysis of high-dimensional genomic data. However, they suffer a problem of tuning parameter selection, which often results in either too many false positives or too small true positives. In this article, we applied selection probability to multivariate regularization methods in order to identify pleiotropic variants associated with multiple phenotypes. Selection probability was applied to individual elastic-net, unified elastic-net and multi-response elastic-net regularization methods. In simulation studies, selection performance of three multivariate regularization methods was evaluated when the total number of phenotypes, the number of phenotypes associated with a variant, and correlations among phenotypes are different. We also applied the regularization methods to a wild bean dataset consisting of 169,028 variants and 17 phenotypes.

Analysis of high-dimensional genomic data using MapReduce based probabilistic neural network

Background: The size of genomics data has been growing rapidly over the last decade. However, the conventional data analysis techniques are incapable of processing this huge amount of data. For the efficient processing of high dimensional datasets, it is essential to develop some new parallel methods.Methods: In this work, a novel distributed method is presented using Map-Reduce (MR)-based approach. The proposed algorithm consists of MR-based Fisher score (mrFScore), MR-based ReliefF (mrRefiefF), and MR-based probabilistic neural network (mrPNN) using a weighted chaotic grey wolf optimization technique (WCGWO). Here, mrFScore, and mrRefiefF methods are introduced for feature selection (FS), and mrPNN is implemented as an effective method for microarray classification. The proper choice of smoothing parameter (σ) plays a major role in the prediction ability of the PNN which is addressed using a novel technique namely, WCGWO. The WCGWO algorithm is used to select the optimal value of σ in PNN.Results: These algorithms have been successfully implemented using the Hadoop framework. The proposed model is tested by using three large and one small microarray datasets, and a comparative analysis is carried out with the existing FS and classification techniques. The results suggest that WCGWO-mrPNN can outperform other methods for high dimensional microarray classification.Conclusion: The effectiveness of the proposed methods are compared with other existing schemes. Experimental results reveal that the proposed scheme is accurate and robust. Hence, the suggested scheme is considered to be a reliable framework for microarray data analysis.Significance: Such a method promotes the application of parallel programming using Hadoop cluster for the analysis of large-scale genomics data, particularly when the dataset is of high dimension.

An empirical threshold of selection probability for analysis of high-dimensional correlated data

For the analysis of high-dimensional data, regularization methods based on penalized likelihood have been extensively studied over the last few decades. But, they commonly require the optimal choice of tuning parameters to select relevant variables. Although cross-validation has been popularly used for tuning parameter selection, its selection result is not often stable due to random split of samples. As an alternative to cross-validation, computation of selection probability has been proposed for stable variable selection. Ranking of individual variables can be determined based on their selection probability, regardless of tuning parameter values. However, a theoretical threshold of selection probability fails to control the number of false discoveries when it applies to high-dimensional correlated data. In this article, we propose new strategy to compute an empirical threshold of selection probability. Selection performance of the proposed threshold is evaluated through extensive simulation studies and high-dimensional genomic data analysis.

HDMAC: A Web-Based Interactive Program for High-Dimensional Analysis of Molecular Alterations in Cancer

Recent advances in high-throughput genomic technologies have nurtured a growing demand for statistical tools to facilitate identification of molecular changes as potential prognostic biomarkers or drugable targets for personalized precision medicine. In this study, we developed a web-based interactive and user-friendly platform for high-dimensional analysis of molecular alterations in cancer (HDMAC) (https://ripsung26.shinyapps.io/rshiny/). On HDMAC, several penalized regression models that are suitable for high-dimensional data analysis, Ridge, Lasso and adaptive Lasso, are offered, with Cox regression for survival and logistic regression for binary outcomes. Choice of a first-step screening is provided to address the multiple-comparison issue that often arises with large-volume genomic data. Hazard ratio or estimated coefficient is provided with each selected gene so that a multivariate regression model may be built based on the genes selected. Cross validation is provided as the method to estimate the prediction power of each regression model. In addition, R codes are also provided to facilitate download of whole sets of molecular variables from TCGA. In this study, illustration of the use of HDMAC was made through a set of data on gene mutations and a set on mRNA expression from ovarian cancer patients and a set on mRNA expression from bladder cancer patient. From the analysis of each set of data, a list of candidate genes was obtained that might be associated with mutations or abnormal expression of genes in ovarian and bladder cancers. HDMAC offers a solution for rigorous and validation analysis of high-dimensional genomic data.

Model-free feature screening for categorical outcomes: Nonlinear effect detection and false discovery rate control.

Feature screening has become a real prerequisite for the analysis of high-dimensional genomic data, as it is effective in reducing dimensionality and removing redundant features. However, existing methods for feature screening have been mostly relying on the assumptions of linear effects and independence (or weak dependence) between features, which might be inappropriate in real practice. In this paper, we consider the problem of selecting continuous features for a categorical outcome from high-dimensional data. We propose a powerful statistical procedure that consists of two steps, a nonparametric significance test based on edge count and a multiple testing procedure with dependence adjustment for false discovery rate control. The new method presents two novelties. First, the edge-count test directly targets distributional difference between groups, therefore it is sensitive to nonlinear effects. Second, we relax the independence assumption and adapt Efron’s procedure to adjust for the dependence between features. The performance of the proposed procedure, in terms of statistical power and false discovery rate, is illustrated by simulated data. We apply the new method to three genomic datasets to identify genes associated with colon, cervical and prostate cancers.

Analysis of high-dimensional genomic data employing a novel bio-inspired algorithm

Over the last decade, there has been a rapid growth in the generation and analysis of the genomics data. Though the existing data analysis methods are capable of handling a particular problem, they cannot guarantee to solve all problems with different nature. Therefore, there always lie a scope of a new algorithm to solve a problem which cannot be efficiently solved by the existing algorithms. In the present work, a novel hybrid approach is proposed based on the improved version of a recently developed bio-inspired optimization technique, namely, salp swarm algorithm (SSA) for microarray classification. Initially, the Fisher score filter is employed to pre-select a subset of relevant genes from the original high-dimensional microarray dataset. Later, a weighted-chaotic SSA (WCSSA) is proposed for the simultaneous optimal gene selection and parameter optimization of the kernel extreme learning machine (KELM) classifier. The proposed scheme is experimented on both binary-class and multi-class microarray datasets. An extensive comparison is performed against original SSA-KELM, particle swarm optimized-KELM (PSO-KELM), and genetic algorithm-KELM (GA-KELM). Lastly, the proposed method is also compared against the results of sixteen existing techniques to emphasize its capacity and competitiveness to successfully reduce the number of original genes by more than 98%. The experimental results show that the genes selected by the proposed method yield higher classification accuracy compared to the alternative techniques. The performance of the proposed scheme demonstrates its effectiveness in terms of number of selected genes (NSG), accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and F-measure. The proposed WCSSA-KELM method is validated using a ten-fold cross-validation technique.

New variable selection strategy for analysis of high-dimensional DNA methylation data.

In genetic association studies, regularization methods are often used due to their computational efficiency for analysis of high-dimensional genomic data. DNA methylation data generated from Infinium HumanMethylation450 BeadChip Kit have a group structure where an individual gene consists of multiple Cytosine-phosphate-Guanine (CpG) sites. Consequently, group-based regularization can precisely detect outcome-related CpG sites. Representative examples are sparse group lasso (SGL) and network-based regularization. The former is powerful when most of the CpG sites within the same gene are associated with a phenotype outcome. In contrast, the latter is preferred when only a few of the CpG sites within the same gene are related to the outcome. In this paper, we propose new variable selection strategy based on a selection probability that measures selection frequency of individual variables selected by both SGL and network-based regularization. In extensive simulation study, we demonstrated that the proposed strategy can show relatively outstanding selection performance under any situation, compared with both SGL and network-based regularization. Also, we applied the proposed strategy to identify differentially methylated CpG sites and their corresponding genes from ovarian cancer data.

Computation and application of tissue-specific gene set weights.

MotivationGene set testing, or pathway analysis, has become a critical tool for the analysis of high-dimensional genomic data. Although the function and activity of many genes and higher-level processes is tissue-specific, gene set testing is typically performed in a tissue agnostic fashion, which impacts statistical power and the interpretation and replication of results.ResultsTo address this challenge, we have developed a bioinformatics approach to compute tissue-specific weights for individual gene sets using information on tissue-specific gene activity from the Human Protein Atlas (HPA). We used this approach to create a public repository of tissue-specific gene set weights for 37 different human tissue types from the HPA and all collections in the Molecular Signatures Database. To demonstrate the validity and utility of these weights, we explored three different applications: the functional characterization of human tissues, multi-tissue analysis for systemic diseases and tissue-specific gene set testing.Availability and implementationAll data used in the reported analyses is publicly available. An R implementation of the method and tissue-specific weights for MSigDB gene set collections can be downloaded at http://www.dartmouth.edu/∼hrfrost/TissueSpecificGeneSets.

DaMiRseq-an R/Bioconductor package for data mining of RNA-Seq data: normalization, feature selection and classification.

RNA-Seq is becoming the technique of choice for high-throughput transcriptome profiling, which, besides class comparison for differential expression, promises to be an effective and powerful tool for biomarker discovery. However, a systematic analysis of high-dimensional genomic data is a demanding task for such a purpose. DaMiRseq offers an organized, flexible and convenient framework to remove noise and bias, select the most informative features and perform accurate classification. DaMiRseq is developed for the R environment (R ≥ 3.4) and is released under GPL (≥2) License. The package runs on Windows, Linux and Macintosh operating systems and is freely available to non-commercial users at the Bioconductor open-source, open-development software project repository (https://bioconductor.org/packages/DaMiRseq/). In compliance with Bioconductor standards, the authors ensure stable package maintenance through software and documentation updates. luca.piacentini@ccfm.it. Supplementary data are available at Bioinformatics online.

그룹 구조를 갖는 고차원 유전체 자료 분석을 위한 네트워크 기반의 규제화 방법

그룹 구조를 갖는 고차원 유전체 자료 분석을 위한 네트워크 기반의 규제화 방법

Interaction-Based Feature Selection for Uncovering Cancer Driver Genes Through Copy Number-Driven Expression Level.

Driver gene selection is crucial to understand the heterogeneous system of cancer. To identity cancer driver genes, various statistical strategies have been proposed, especially the L1-type regularization methods have drawn a large amount of attention. However, the statistical approaches have been developed purely from algorithmic and statistical point, and the existing studies have applied the statistical approaches to genomic data analysis without consideration of biological knowledge. We consider a statistical strategy incorporating biological knowledge to identify cancer driver gene. The alterations of copy number have been considered to driver cancer pathogenesis processes, and the region of strong interaction of copy number alterations and expression levels was known as a tumor-related symptom. We incorporate the influence of copy number alterations on expression levels to cancer driver gene-selection processes. To quantify the dependence of copy number alterations on expression levels, we consider [Formula: see text] and [Formula: see text] effects of copy number alterations on expression levels of genes, and incorporate the symptom of tumor pathogenesis to gene-selection procedures. We then proposed an interaction-based feature-selection strategy based on the adaptive L1-type regularization and random lasso procedures. The proposed method imposes a large amount of penalty on genes corresponding to a low dependency of the two features, thus the coefficients of the genes are estimated to be small or exactly 0. It implies that the proposed method can provide biologically relevant results in cancer driver gene selection. Monte Carlo simulations and analysis of the Cancer Genome Atlas (TCGA) data show that the proposed strategy is effective for high-dimensional genomic data analysis. Furthermore, the proposed method provides reliable and biologically relevant results for cancer driver gene selection in TCGA data analysis.

High-dimensional genomic data bias correction and data integration using MANCIE.

High-dimensional genomic data analysis is challenging due to noises and biases in high-throughput experiments. We present a computational method matrix analysis and normalization by concordant information enhancement (MANCIE) for bias correction and data integration of distinct genomic profiles on the same samples. MANCIE uses a Bayesian-supported principal component analysis-based approach to adjust the data so as to achieve better consistency between sample-wise distances in the different profiles. MANCIE can improve tissue-specific clustering in ENCODE data, prognostic prediction in Molecular Taxonomy of Breast Cancer International Consortium and The Cancer Genome Atlas data, copy number and expression agreement in Cancer Cell Line Encyclopedia data, and has broad applications in cross-platform, high-dimensional data integration.

Multiple Group Testing Procedures for Analysis of High-Dimensional Genomic Data.

In genetic association studies with high-dimensional genomic data, multiple group testing procedures are often required in order to identify disease/trait-related genes or genetic regions, where multiple genetic sites or variants are located within the same gene or genetic region. However, statistical testing procedures based on an individual test suffer from multiple testing issues such as the control of family-wise error rate and dependent tests. Moreover, detecting only a few of genes associated with a phenotype outcome among tens of thousands of genes is of main interest in genetic association studies. In this reason regularization procedures, where a phenotype outcome regresses on all genomic markers and then regression coefficients are estimated based on a penalized likelihood, have been considered as a good alternative approach to analysis of high-dimensional genomic data. But, selection performance of regularization procedures has been rarely compared with that of statistical group testing procedures. In this article, we performed extensive simulation studies where commonly used group testing procedures such as principal component analysis, Hotelling's T2 test, and permutation test are compared with group lasso (least absolute selection and shrinkage operator) in terms of true positive selection. Also, we applied all methods considered in simulation studies to identify genes associated with ovarian cancer from over 20,000 genetic sites generated from Illumina Infinium HumanMethylation27K Beadchip. We found a big discrepancy of selected genes between multiple group testing procedures and group lasso.

Recursive Random Lasso (RRLasso) for Identifying Anti-Cancer Drug Targets.

Uncovering driver genes is crucial for understanding heterogeneity in cancer. L 1-type regularization approaches have been widely used for uncovering cancer driver genes based on genome-scale data. Although the existing methods have been widely applied in the field of bioinformatics, they possess several drawbacks: subset size limitations, erroneous estimation results, multicollinearity, and heavy time consumption. We introduce a novel statistical strategy, called a Recursive Random Lasso (RRLasso), for high dimensional genomic data analysis and investigation of driver genes. For time-effective analysis, we consider a recursive bootstrap procedure in line with the random lasso. Furthermore, we introduce a parametric statistical test for driver gene selection based on bootstrap regression modeling results. The proposed RRLasso is not only rapid but performs well for high dimensional genomic data analysis. Monte Carlo simulations and analysis of the “Sanger Genomics of Drug Sensitivity in Cancer dataset from the Cancer Genome Project” show that the proposed RRLasso is an effective tool for high dimensional genomic data analysis. The proposed methods provide reliable and biologically relevant results for cancer driver gene selection.

An Independent Filter for Gene Set Testing Based on Spectral Enrichment.

Gene set testing has become an indispensable tool for the analysis of high-dimensional genomic data. An important motivation for testing gene sets, rather than individual genomic variables, is to improve statistical power by reducing the number of tested hypotheses. Given the dramatic growth in common gene set collections, however, testing is often performed with nearly as many gene sets as underlying genomic variables. To address the challenge to statistical power posed by large gene set collections, we have developed spectral gene set filtering (SGSF), a novel technique for independent filtering of gene set collections prior to gene set testing. The SGSF method uses as a filter statistic the p-value measuring the statistical significance of the association between each gene set and the sample principal components (PCs), taking into account the significance of the associated eigenvalues. Because this filter statistic is independent of standard gene set test statistics under the null hypothesis but dependent under the alternative, the proportion of enriched gene sets is increased without impacting the type I error rate. As shown using simulated and real gene expression data, the SGSF algorithm accurately filters gene sets unrelated to the experimental outcome resulting in significantly increased gene set testing power.

AB0019 A PLS multivariate model to predict RA radiological severity by selecting key predictors from a large panel of SNPS and environmental factors

BackgroundThe mechanisms contributing to the development of rheumatoid arthritis and to the development of tissue damage are complex [1]. Candidate gene association and linkage studies have recently identified a role...

High-Dimensional Regression and Variable Selection Using CAR Scores

Variable selection is a difficult problem that is particularly challenging in the analysis of high-dimensional genomic data. Here, we introduce the CAR score, a novel and highly effective criterion for variable ranking in linear regression based on Mahalanobis-decorrelation of the explanatory variables. The CAR score provides a canonical ordering that encourages grouping of correlated predictors and down-weights antagonistic variables. It decomposes the proportion of variance explained and it is an intermediate between marginal correlation and the standardized regression coefficient. As a population quantity, any preferred inference scheme can be applied for its estimation. Using simulations, we demonstrate that variable selection by CAR scores is very effective and yields prediction errors and true and false positive rates that compare favorably with modern regression techniques such as elastic net and boosting. We illustrate our approach by analyzing data concerned with diabetes progression and with the effect of aging on gene expression in the human brain. The R package “care” implementing CAR score regression is available from CRAN.

Super-sparse principal component analyses for high-throughput genomic data

BackgroundPrincipal component analysis (PCA) has gained popularity as a method for the analysis of high-dimensional genomic data. However, it is often difficult to interpret the results because the principal components are linear combinations of all variables, and the coefficients (loadings) are typically nonzero. These nonzero values also reflect poor estimation of the true vector loadings; for example, for gene expression data, biologically we expect only a portion of the genes to be expressed in any tissue, and an even smaller fraction to be involved in a particular process. Sparse PCA methods have recently been introduced for reducing the number of nonzero coefficients, but these existing methods are not satisfactory for high-dimensional data applications because they still give too many nonzero coefficients.ResultsHere we propose a new PCA method that uses two innovations to produce an extremely sparse loading vector: (i) a random-effect model on the loadings that leads to an unbounded penalty at the origin and (ii) shrinkage of the singular values obtained from the singular value decomposition of the data matrix. We develop a stable computing algorithm by modifying nonlinear iterative partial least square (NIPALS) algorithm, and illustrate the method with an analysis of the NCI cancer dataset that contains 21,225 genes.ConclusionsThe new method has better performance than several existing methods, particularly in the estimation of the loading vectors.

Extensions of Sparse Canonical Correlation Analysis with Applications to Genomic Data

In recent work, several authors have introduced methods for sparse canonical correlation analysis (sparse CCA). Suppose that two sets of measurements are available on the same set of observations. Sparse CCA is a method for identifying sparse linear combinations of the two sets of variables that are highly correlated with each other. It has been shown to be useful in the analysis of high-dimensional genomic data, when two sets of assays are available on the same set of samples. In this paper, we propose two extensions to the sparse CCA methodology. (1) Sparse CCA is an unsupervised method; that is, it does not make use of outcome measurements that may be available for each observation (e.g., survival time or cancer subtype). We propose an extension to sparse CCA, which we call sparse supervised CCA, which results in the identification of linear combinations of the two sets of variables that are correlated with each other and associated with the outcome. (2) It is becoming increasingly common for researchers to collect data on more than two assays on the same set of samples; for instance, SNP, gene expression, and DNA copy number measurements may all be available. We develop sparse multiple CCA in order to extend the sparse CCA methodology to the case of more than two data sets. We demonstrate these new methods on simulated data and on a recently published and publicly available diffuse large B-cell lymphoma data set.

Partial least squares: a versatile tool for the analysis of high-dimensional genomic data

Partial least squares (PLS) is an efficient statistical regression technique that is highly suited for the analysis of genomic and proteomic data. In this article, we review both the theory underlying PLS as well as a host of bioinformatics applications of PLS. In particular, we provide a systematic comparison of the PLS approaches currently employed, and discuss analysis problems as diverse as, e.g. tumor classification from transcriptome data, identification of relevant genes, survival analysis and modeling of gene networks and transcription factor activities.