JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1028 10.6339/21-JDS1028 Statistical Data Science Integrative Clustering Analysis with Application in Multi-Source Gene Expression Data YangLiuqing1 PanQing1 ZhaoYunpengyunpeng.zhao@asu.edu2∗ Department of Statistics, George Washington University, Washington, D.C., U.S.A. School of Mathematical and Natural Sciences, Arizona State University, Tempe, Arizona, U.S.A. Corresponding author. Email: yunpeng.zhao@asu.edu. 202291120212011433 Supplementary Material

Code for the integrative analysis and the data used in the real data analysis are available at https://github.com/yangliuqing1992/Integrative_clustering.

64202112102021 2022 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2022 Open access article under the CC BY license.

In omics studies, different sources of information about the same set of genes are often available. When the group structure (e.g., gene pathways) within the genes are of interests, we combine the normal hierarchical model with the stochastic block model, through an integrative clustering framework, to model gene expression and gene networks jointly. The integrative framework provides higher accuracy in extensive simulation studies when one or both of the data sources contain noises or when different data sources provide complementary information. An empirical guideline in the choice between integrative versus separate clustering models is proposed. The integrative clustering method is illustrated on the mouse embryo single cell RNAseq and bulk cell microarray data, which identified not only the gene sets shared by both data sources but also the gene sets unique in one data source.

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