Dataset Information

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

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.

SUBMITTER: Li Y

PROVIDER: S-EPMC6798423 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Publications

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

Li Yuntong Y Fan Teresa W M TWM Lane Andrew N AN Kang Woo-Young WY Arnold Susanne M SM Stromberg Arnold J AJ Wang Chi C Chen Li L

BMC bioinformatics 20191017 1

<h4>Background</h4>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.<h4>Results</h4>We propose a new semi-param ...[more]

PMID: 31623550

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Dataset Information

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

Publications

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

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