[3H6-OS-10d] OS-10

[3H6-OS-10d-03] Selective inference for spectral clustering with an application to patient stratification

〇Naotoshi Nakamura¹ (1. Yokohama City University)

[[Online]]

Keywords:data-driven, clustering, selective inference

In disease research, patient stratification through clustering in high-dimensional spaces represented by multidimensional clinical test data has gained attention as a novel data-driven analytical approach. This method aims to characterize the disease groups represented by each cluster. When applying nonlinear dimensionality reduction techniques, such as UMAP or t-SNE, to disease-related high-dimensional data, the resulting low-dimensional spaces can exhibit complex geometries, necessitating tailored clustering methods. One example is spectral clustering, which leverages spectral graph theory. However, when conducting significance testing on features associated with data-driven clusters, a challenge known as "selective inference" arises. Unlike classical hypothesis testing, where clustering and inference are performed independently, this scenario involves using the same dataset for both. Consequently, to accurately control the type I error rate, the influence of clustering must be incorporated into the inference process. In this presentation, we propose a method for addressing selective inference in the context of patient stratification via spectral clustering. Our approach adapts techniques developed for selective inference in hierarchical clustering. We also demonstrate practical applications of this method.