Dataset Information

EFAMIX, a tool to decompose inline chromatography SAXS data from partially overlapping components.

ABSTRACT: Small-angle X-ray scattering (SAXS) is an established technique for structural analysis of biological macromolecules in solution. During the last decade, inline chromatography setups coupling SAXS with size exclusion (SEC-SAXS) or ion exchange (IEC-SAXS) have become popular in the community. These setups allow one to separate individual components in the sample and to record SAXS data from isolated fractions, which is extremely important for subsequent data interpretation, analysis, and structural modeling. However, in case of partially overlapping elution peaks, inline chromatography SAXS may still yield scattering profiles from mixtures of components. The deconvolution of these scattering data into the individual fractions is nontrivial and potentially ambiguous. We describe a cross-platform computer program, EFAMIX, for restoring the scattering and concentration profiles of the components based on the evolving factor analysis (EFA). The efficiency of the program is demonstrated in a number of simulated and experimental SEC-SAXS data sets. Sensitivity and limitations of the method are explored, and its applicability to IEC-SAXS data is discussed. EFAMIX requires minimal user intervention and is available to academic users through the program package ATSAS as from release 3.1.

SUBMITTER: Konarev PV

PROVIDER: S-EPMC8740826 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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EFAMIX, a tool to decompose inline chromatography SAXS data from partially overlapping components.

Konarev Petr V PV Graewert Melissa A MA Jeffries Cy M CM Fukuda Masakazu M Cheremnykh Taisiia A TA Volkov Vladimir V VV Svergun Dmitri I DI

Protein science : a publication of the Protein Society 20211122 1

Small-angle X-ray scattering (SAXS) is an established technique for structural analysis of biological macromolecules in solution. During the last decade, inline chromatography setups coupling SAXS with size exclusion (SEC-SAXS) or ion exchange (IEC-SAXS) have become popular in the community. These setups allow one to separate individual components in the sample and to record SAXS data from isolated fractions, which is extremely important for subsequent data interpretation, analysis, and structur ...[more]

PMID: 34767272

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Project description:BackgroundAs omics measurements profiled on different molecular layers are interconnected, integrative approaches that incorporate the regulatory effect from multi-level omics data are needed. When the multi-level omics data are from the same individuals, gene expression (GE) clusters can be identified using information from regulators like genetic variants and DNA methylation. When the multi-level omics data are from different individuals, the choice of integration approaches is limited.MethodsWe developed an approach to improve GE clustering from microarray data by integrating regulatory data from different but partially overlapping sets of individuals. We achieve this through (1) decomposing gene expression into the regulated component and the other component that is not regulated by measured factors, (2) optimizing the clustering goodness-of-fit objective function. We do not require the availability of different omics measurements on all individuals. A certain amount of individual overlap between GE data and the regulatory data is adequate for modeling the regulation, thus improving GE clustering.ResultsA simulation study shows that the performance of the proposed approach depends on the strength of the GE-regulator relationship, degree of missingness, data dimensionality, sample size, and the number of clusters. Across the various simulation settings, the proposed method shows competitive performance in terms of accuracy compared to the alternative K-means clustering method, especially when the clustering structure is due mostly to the regulated component, rather than the unregulated component. We further validate the approach with an application to 8,902 Framingham Heart Study participants with data on up to 17,873 genes and regulation information of DNA methylation and genotype from different but partially overlapping sets of participants. We identify clustering structures of genes associated with pulmonary function while incorporating the predicted regulation effect from the measured regulators. We further investigate the over-representation of these GE clusters in pathways of other diseases that may be related to lung function and respiratory health.ConclusionWe propose a novel approach for clustering GE with the assistance of regulatory data that allowed for different but partially overlapping sets of individuals to be included in different omics data.

Dataset Information

EFAMIX, a tool to decompose inline chromatography SAXS data from partially overlapping components.

Publications

EFAMIX, a tool to decompose inline chromatography SAXS data from partially overlapping components.

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