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.
journal_name
BMC Bioinformaticsjournal_title
BMC bioinformaticsauthors
Li Y,Fan TWM,Lane AN,Kang WY,Arnold SM,Stromberg AJ,Wang C,Chen Ldoi
10.1186/s12859-019-3067-zsubject
Has Abstractpub_date
2019-10-17 00:00:00pages
501issue
1issn
1471-2105pii
10.1186/s12859-019-3067-zjournal_volume
20pub_type
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