An investigation on sparse partial least squares algorithms for compositional data

초록

Recently, the interest in analyzing compositional data has emerged. Since the microbiome abundance datasets from the TCMA study have high-dimensional and compositional properties in this research, we investigate statistical methods to handle data having high-dimensionality and compositional characteristics, which is the sparse partial least squares (SPLS) method with SIMPLS and NIPALS algorithms. In general, the SPLS method is selected as a promising method to consider the high-dimensionality. In this study, since we capture the compositional characteristics of data using the SPLS method, the log-ratio transformation, especially \clr~transformation, is employed. To evaluate the performance of the SPLS method with the \clr~transformation, we conduct simulation studies and apply the SPLS method with SIMPLS and NIPALS algorithms to real data.

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