Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning

Integrative analysis of multi-omic datasets remains a challenge due to gaps and heterogeneity. We present a bespoke unsupervised deep learning model that generates synthetic multi-omic data for 1,523 cancer cell lines, completing the gaps and increasing the number of molecular and phenotypic profiles by 32.7%. Our model augments cellular measurements, improves cancer type clustering, and increases statistical power for cancer dependency biomarker discovery. Model explanation facilitates biomarker discovery and cancer target prioritization.