Abstract PO-026: GLUER: integrative analysis of multi-omics data at single-cell resolution

Abstract Single-cell omics assays such as RNA-Seq, ATAC-Seq and methylome sequencing have been developed to identify cell types and/or states in heterogeneous tissues. However, it is challenging to integrate these different types of single-cell omics data. Moreover, the advent of spatially resolved single-cell imaging data represent another challenge for integrative analysis with omics data. Here, we present a new algorithm, inteGrative anaLysis of mUlti-omics at single-cEll Resolution (GLUER), for integration of single-cell multi-omics data as well as imaging data. We tested GLUER using multiple datasets generated using multiomics data generated on the same single cells, which was taken as the ground truth. Our results demonstrate that GLUER has significantly improved performance in terms of the accuracy of matching cells with different data modalities, which in turn enhances downstream analyses such as clustering and trajectory inference. GLUER provides a principled analytical framework for studying the heterogeneity of cell populations using multi-omics and imaging data. Citation Format: Tao Peng, Kamyar Esmaeili Pourfarhangi, Kai Tan. GLUER: integrative analysis of multi-omics data at single-cell resolution [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-026.