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.
CITATION STYLE
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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