Reference point insensitive molecular data analysis

Altenbuchinger, Michael; Rehberg, Thorsten; Zacharias, Helena U.; Stämmler, Frank; Dettmer, Katja; Weber, Daniela; Hiergeist, Andreas; Gessner, André; Holler, Ernst; Oefner, Peter J.; Spang, Rainer

doi:10.1093/bioinformatics/btw598

Cited by 35 publications

(60 citation statements)

References 25 publications

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“…LASSO is an algorithm used in artificial intelligence. Here it detected the intrinsic normalization problems with the TSP-normalization and "intelligently" decided for a scale-independent zero-sum type signature [Altenbuchinger et al 2016].…”

Section: Discussionmentioning

confidence: 99%

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Scale-Invariant Biomarker Discovery in Urine and Plasma Metabolite Fingerprints

Zacharias¹,

Rehberg²,

Mehrl³

et al. 2017

J. Proteome Res.

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Metabolomics data is typically scaled to a common reference like a constant volume of body fluid, a constant creatinine level, or a constant area under the spectrum. Such scaling of the data, however, may affect the selection of biomarkers and the biological interpretation of results in unforeseen ways. Here, we studied how both the outcome of hypothesis tests for differential metabolite concentration and the screening for multivariate metabolite signatures are affected by the choice of scale. To overcome this problem for metabolite signatures and to establish a scale-invariant biomarker discovery algorithm, we extended linear zero-sum regression to the logistic regression framework and showed in two applications to H NMR-based metabolomics data how this approach overcomes the scaling problem. Logistic zero-sum regression is available as an R package as well as a high-performance computing implementation that can be downloaded at https://github.com/rehbergT/zeroSum .

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Section: Discussionmentioning

confidence: 99%

“…Zero-sum regression [Lin et al 2014] is a novel machinelearning algorithm that is insensitive to rescaling the data [Altenbuchinger et al 2016]. It allows for a selection of biomarkers that does not depend on the units chosen.…”

Section: Logistic Zero-sum Regressionmentioning

confidence: 99%

Scale-Invariant Biomarker Discovery in Urine and Plasma Metabolite Fingerprints

Zacharias¹,

Rehberg²,

Mehrl³

et al. 2017

J. Proteome Res.

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“…Sum to zero constraints In this subsection we consider the sum to zero constraints, i.e., e T x = 0. Recently this problem generates much interest in the statistics and bioinformatics communities (Altenbuchinger et al, 2016;Lin et al, 2014;Shi et al, 2016). Table 5 reports the performance of all methods we test on synthetic datasets.…”

Section: Synthetic Datamentioning

confidence: 99%

An Efficient Augmented Lagrangian-Based Method for Linear Equality-Constrained Lasso

Deng

Yue

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Variable selection is one of the most important tasks in statistics and machine learning. To incorporate more prior information about the regression coefficients, the constrained Lasso model has been proposed in the literature. In this paper, we present an inexact augmented Lagrangian method to solve the Lasso problem with linear equality constraints. By fully exploiting second-order sparsity of the problem, we are able to greatly reduce the computational cost and obtain highly efficient implementations. Furthermore, numerical results on both synthetic data and real data show that our algorithm is superior to existing first-order methods in terms of both running time and solution accuracy.Set J = {j : |x j | > σλ} with cardinality |J | = r. By utilizing the diagonal structure of Q, we can writē

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“…Additional numerical simulations comparing the CKF and original model-X knockoff filter (KF) methods are available through the online supplementary materials. The zeroSum R package (Altenbuchinger et al, 2017;Rehberg, 2017) was used to perform the sum-to-zero constrained optimization in this simulation.…”

Section: Simulation Studiesmentioning

confidence: 99%

Compositional knockoff filter for high-dimensional regression analysis of microbiome data

Srinivasan

Zhan

Xue

2019

Preprint

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A critical task in microbiome data analysis is to explore the association between a scalar response of interest and a large number of microbial taxa that are summarized as compositional data at different taxonomic levels. Motivated by fine-mapping of the microbiome, we propose a two-step compositional knockoff filter (CKF) to provide the effective finite-sample false discovery rate (FDR) control in high-dimensional linear log-contrast regression analysis of microbiome compositional data. In the first step, we employ the compositional screening procedure to remove insignificant microbial taxa while retaining the essential sum-to-zero constraint. In the second step, we extend the knockoff filter to identify the significant microbial taxa in the sparse regression model for compositional data.Thereby, a subset of the microbes is selected from the high-dimensional microbial taxa as related to the response using a pre-specified FDR threshold. We study the asymptotic properties of the proposed two-step procedure, including both sure screening and effective false discovery control. We demonstrate the finite-sample properties in simulation studies, which show the gain in the empirical power while controlling the nominal FDR. The potential usefulness of the proposed method is also illustrated with application to an inflammatory bowel disease dataset to identify microbial taxa that influence host gene expressions.

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Reference point insensitive molecular data analysis

Cited by 35 publications

References 25 publications

Scale-Invariant Biomarker Discovery in Urine and Plasma Metabolite Fingerprints

Scale-Invariant Biomarker Discovery in Urine and Plasma Metabolite Fingerprints

An Efficient Augmented Lagrangian-Based Method for Linear Equality-Constrained Lasso

Compositional knockoff filter for high-dimensional regression analysis of microbiome data

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