Abstract
We address the high dimensional variable selection problem for associating the microbial compositions with a phenotype such as body mass index and disease status. Due to various sequencing depth, the number of reads assigned to a species or an operational taxonomic unit (OTU) is not directly comparable across different samples. Usually rarefying or normalization of the metagenomic count data has to be done before performing the downstream analysis. In this chapter, we employ a log contrast model bypassing the need for normalization. We propose a new method to identify phenotype associated species or OTUs using penalized regression and stability selection. The proposed method can also be applied to variable selection for regression analysis with compositional covariates. We compare the performance of different methods through simulation studies and real data analysis in the field of metagenomics.
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Wang, P., Jiang, H. (2019). Variable Selection for High Dimensional Metagenomic Data. In: Zhang, L., Chen, DG., Jiang, H., Li, G., Quan, H. (eds) Contemporary Biostatistics with Biopharmaceutical Applications. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-15310-6_2
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DOI: https://doi.org/10.1007/978-3-030-15310-6_2
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