Accelerating microbiomic big data analysis by spectral interpolation

Dimensionality reduction and visualization are two important procedures in microbiome data analysis. With the intrinsic high dimensionality of the feature space in raw microbiome sequencing data, such as 16S rRNA, it requires proper simplification for possible further analysis. The explosively increasing size of data from large-scale microbiome studies inevitably and exponentially raises the computational complexity of existing algorithms, which is an urgent issue standing in the way requires addressing. This study proposed a new approach for dimensionality reduction and visualization on microbiome sequencing data associated with the very issue. This method not only greatly improves the efficiency of computing on microbiomic big data analysis by spectral interpolation technique but also preserves as much information as possible from original data with decent visualization results. With this adaptive method introduced to the large-scale studies of microbiome, we can better facilitate the revealing of patterns and insights of microbial communities.