Traditionally, Raman imaging data analysis involves univariate, multivariate, or clustering approaches at the level of individual maps (single cells or tissue regions) 10, , limiting comprehensive and semi-automatic analysis of the entire dataset. Global clustering approaches have been applied to FT-IR hyperspectral maps, aiming to describe the presence and distribution of the same spectral content in different samples or conditions . Nguyen et al. introduced the so-called "joint k-means clustering" process for analyzing FT-IR datasets comprising up to 15 tissue images simultaneously . To date, only one study has applied global clustering to a substantial number of Raman maps . Authors employed the so-called "common k-means analysis" on 118 cells to separate nucleus and cytoplasm, for subsequent multivariate analysis, and not for imaging purposes.