Gene ranking and biomarker discovery under correlation

Zuber, Verena; Strimmer, Korbinian

doi:10.1093/bioinformatics/btp460

Cited by 91 publications

(111 citation statements)

References 34 publications

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“…Correlation between the first 100 variables is set to 0.7 and correlation between the remaining 900 variables is equal to 0.3. This correlation matrix is used to study impact of dependence in multiple testing in the context of gene expression analysis in Zuber and Strimmer (2009); (C) Σ is decomposed into a specific and a common part as in a factor model (see Sect. 3.2): Σ = B B + Ψ .…”

Section: Simulation Designmentioning

confidence: 99%

“…Indeed, the superiority of approaches based on an erroneous independence assumption is reported (Dudoit et al 2002;Levina 2002;Bickel and Levina 2004), whereas more and more methods account for the covariance structure (Guo et al 2007;Dabney and Storey 2007;Xu et al 2009;Zuber and Strimmer 2009). More recently, Ahdesmäki and Strimmer (2010) gives more insight to this issue by revisiting the naive Bayes approach of Efron (2008) called diagonal discriminant analysis (DDA), using decorrelated individual scores.…”

Section: Introductionmentioning

confidence: 99%

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Stability of feature selection in classification issues for high-dimensional correlated data

2015

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Handling dependence or not in feature selection is still an open question in supervised classification issues where the number of covariates exceeds the number of observations. Some recent papers surprisingly show the superiority of naive Bayes approaches based on an obviously erroneous assumption of independence, whereas others recommend to infer on the dependence structure in order to decorrelate the selection statistics. In the classical linear discriminant analysis (LDA) framework, the present paper first highlights the impact of dependence in terms of instability of feature selection. A second objective is to revisit the above issue using a flexible factor modeling for the covariance. This framework introduces latent components of dependence, conditionally on which a new Bayes consistency is defined. A procedure is then proposed for the joint estimation of the expectation and variance parameters of the model. The present method is compared to recent regularized diagonal discriminant analysis approaches, assuming independence among features, and regularized LDA procedures, both in terms of classification performance and stability of feature selection. The proposed method is implemented in the R package FADA, freely available from the R repository CRAN.

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Section: Simulation Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stability of feature selection in classification issues for high-dimensional correlated data

2015

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“…As with the grouped CAT score (Zuber and Strimmer, 2009) we also may add a sign in this definition. An estimate of the squared grouped CAR score is an example of a simple global test statistic that may be useful, e.g., in studying gene set enrichment (e.g.…”

Section: Grouped Car Scorementioning

confidence: 99%

High-Dimensional Regression and Variable Selection Using CAR Scores

Zuber¹,

Strimmer²

2011

Statistical Applications in Genetics and Molecular Biology

125

128

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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.

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“…The default is to generate data with a diagonal covariance matrix. To obtain data with a specific covariance structure (as done in, e.g., Zuber and Strimmer 2009;Meinshausen and Bühlmann 2010;Wehrens et al 2011), one can specify the cormat argument:…”

Section: Test Datamentioning

confidence: 99%

Meta-Statistics for Variable Selection: TheRPackageBioMark

Wehrens¹,

Franceschi²

2012

J. Stat. Soft.

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Biomarker identification is an ever more important topic in the life sciences. With the advent of measurement methodologies based on microarrays and mass spectrometry, thousands of variables are routinely being measured on complex biological samples. Often, the question is what makes two groups of samples different. Classical hypothesis testing suffers from the multiple testing problem; however, correcting for this often leads to a lack of power. In addition, choosing α cutoff levels remains somewhat arbitrary. Also in a regression context, a model depending on few but relevant variables will be more accurate and precise, and easier to interpret biologically.We propose an R package, BioMark, implementing two meta-statistics for variable selection. The first, higher criticism, presents a data-dependent selection threshold for significance, instead of a cookbook value of α = 0.05. It is applicable in all cases where two groups are compared. The second, stability selection, is more general, and can also be applied in a regression context. This approach uses repeated subsampling of the data in order to assess the variability of the model coefficients and selects those that remain consistently important. It is shown using experimental spike-in data from the field of metabolomics that both approaches work well with real data. BioMark also contains functionality for simulating data with specific characteristics for algorithm development and testing.

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Gene ranking and biomarker discovery under correlation

Abstract: The shrinkage cat score is implemented in the R package 'st', which is freely available under the terms of the GNU General Public License (version 3 or later) from CRAN (http://cran.r-project.org/web/packages/st/).

Cited by 91 publications

References 34 publications

Stability of feature selection in classification issues for high-dimensional correlated data

Stability of feature selection in classification issues for high-dimensional correlated data

High-Dimensional Regression and Variable Selection Using CAR Scores

Meta-Statistics for Variable Selection: TheRPackageBioMark

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