Chi Wang, PhD, Department of Biostatistics, University of Kentucky

"Differential Abundance Analysis for Proteomic and Metabolomic Data"

Mass spectrometry (MS) is widely used for proteomic and metabolomic profiling of biological samples. Data obtained by MS are often zero-inflated. Those zero values are called point mass values (PMVs), which can be further grouped into biological PMVs and technical PMVs. The former type is caused by absence of compounds and the later type is caused by detection limit. A left-inflated mixture likelihood ratio test (LIM) was developed to separate the two types of zeros apart and to perform differential abundant analysis comparing samples from different experimental groups. However, we notice that LIM is unable to properly estimate the variance and thus lead to inaccurate differential abundance result when the number of non-zero values is small. We propose a new differential abundance analysis method, DASEV, which uses an empirical Bayes shrinkage method to more robustly estimate the variance and enhance the overall accuracy of differential abundance analysis. Simulation studies and real data analysis demonstrate that DASEV outperforms LIM.

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