Data-driven Analytics on Scanning Probe Imaging of Biomolecules

Abstract

Data-driven analytics can provide useful information derived from the images of scanning probe microscopy for biological systems including large variety of species and complicated interactions. Bayesian inference is applied to correlation analysis for height distribution of atomic force microscopy images on SasA-KaiC complex formation involved in information transfer from circadian clock oscillator to the output pathway in cyanobacteria. Principal component analysis is applied to the ion images constituted from mass spectra obtained by tapping-mode scanning probe electrospray ionization.