Abstract High-dimensional analysis based on advanced microscopy and image-analysis technologies can assist biologists in quantitative analysis of eukaryotic cells and their dynamic processes. This requires exhaustive, precise, and high-quality descriptions of morphological traits. Given the growing trend of obtaining quantitative information from phenotypic measurements, phenomics provides useful clues for system-level dissection of cellular processes. Several methods have been developed for acquisition and analysis of high-dimensional morphological data in different model organisms, including Saccharomyces cerevisiae . However, previous studies dealt only with the means and medians of traits, which contain only some of the phenotypic information. Single-cell morphological data offer other dimensions of phenotypes, providing powerful tools to elucidate gene functions, cellular networks, and system robustness thoroughly. Advances in conceptual models and computational methodology that enable analysis of high-dimensional single-cell data have recently caused a paradigm shift in biology. Hence, in this chapter, we describe current advances and their applicability, showing how these techniques have improved our understanding of cellular processes.