Rigid geometry solves "curse of dimensionality" effects in clustering methods: An application to omics data.

ABSTRACT: The quality of samples preserved long term at ultralow temperatures has not been adequately studied. To improve our understanding, we need a strategy to analyze protein degradation and metabolism at subfreezing temperatures. To do this, we obtained liquid chromatography-mass spectrometry (LC/MS) data of calculated protein signal intensities in HEK-293 cells. Our first attempt at directly clustering the values failed, most likely due to the so-called "curse of dimensionality". The clusters were not reproducible, and the outputs differed with different methods. By utilizing rigid geometry with a prime ideal I-adic (p-adic) metric, however, we rearranged the sample clusters into a meaningful and reproducible order, and the results were the same with each of the different clustering methods te

SUBMITTER: Adachi S 

PROVIDER: S-EPMC5470695 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature