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Abstract:
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Metagenomics has a great potential to discover previously unattainable information about microbial communities. Detecting differentially abundant features plays a critical role in revealing the contributors (i.e., pathogens) to the status (e.g., disease) of microbial samples. However, currently available methods lack power in detecting differentially abundant features across different conditions. We have proposed a robust procedure to meet with the challenges in detecting differentially abundant features (e.g., species or genes) from metagenomic samples under different biological/medical conditions. The new approach, Ratio Approach for Identifying Differential Abundance (RAIDA), avoids possible issues/concerns that all other existing methods encounter, e.g., normalization and undersampling issues that microbial samples undergo. Compared with other existing methods the new approach shows consistently powerful performance in the comprehensive simulation studies. The new method is also applied to a real metagenomic dataset relating to diabetes II and the new interesting findings may provide another angle of understanding the mechanism of the disease.
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