Ruibin Xi (席瑞斌) is a professor in Peking University. His research interests include the development of new statistical theories and algorithms to solve problems in biology, medicine, computer science, genomics and bioinformatics, such as studying cancer genome sequencing data and single cell sequencing data.

Title: Feature screening in clustering analysis with applications in single-cell analyses

Abstract: we consider feature screening for ultrahigh dimensional clustering analyses. Based on the observation that the marginal distribution of any given feature is a mixture of its conditional distributions in different clusters, we propose to screen clustering features by independently evaluating the homogeneity of each feature’s mixture distribution. Important clustering-relevant features have heterogeneous components in their mixture distributions and unimportant features have homogeneous components. The well-known EM-test statistic is used to evaluate the homogeneity. Under general parametric settings, we establish the tail probability bounds of the EM-test statistic for the homogeneous and heterogeneous features, and further show that the proposed screening procedure can achieve the sure independent screening and even the consistency in selection properties. Limiting distribution of the EM-test statistic is also obtained for general parametric distributions. The proposed method is computationally efficient, can accurately screen for important clustering-relevant features and help to significantly improve clustering, as demonstrated in our extensive simulation and real data analyses.