SDA: A semi-parametric differential abundance analysis method for metabolomics and proteomics data

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Abstract

Background: Identifying differentially abundant features between different experimental groups is a common goal for many metabolomics and proteomics studies. However, analyzing data from mass spectrometry (MS) is difficult because the data may not be normally distributed and there is often a large fraction of zero values. Although several statistical methods have been proposed, they either require the data normality assumption or are inefficient. Results: We propose a new semi-parametric differential abundance analysis (SDA) method for metabolomics and proteomics data from MS. The method considers a two-part model, a logistic regression for the zero proportion and a semi-parametric log-linear model for the possibly non-normally distributed non-zero values, to characterize data from each feature. A kernel-smoothed likelihood method is developed to estimate model coefficients and a likelihood ratio test is constructed for differential abundant analysis. The method has been implemented into an R package, SDAMS, which is available at https://www.bioconductor.org/packages/release/bioc/HTML/SDAMS.HTML. Conclusion: By introducing the two-part semi-parametric model, SDA is able to handle both non-normally distributed data and large fraction of zero values in a MS dataset. It also allows for adjustment of covariates. Simulations and real data analyses demonstrate that SDA outperforms existing methods.

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Li, Y., Fan, T. W. M., Lane, A. N., Kang, W. Y., Arnold, S. M., Stromberg, A. J., … Chen, L. (2019). SDA: A semi-parametric differential abundance analysis method for metabolomics and proteomics data. BMC Bioinformatics, 20(1). https://doi.org/10.1186/s12859-019-3067-z

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