StoHi-C: Using t-Distributed Stochastic Neighbor Embedding (t-SNE) to predict 3D genome structure from Hi-C Data

Abstract

In order to comprehensively understand the structure-function relationship of the genome, 3D genome structures must first be predicted from biological data (like Hi-C) using computational tools. Many of these existing tools rely partially or completely on multi-dimensional scaling (MDS) to embed predicted structures in 3D space. MDS is known to have inherent problems when applied to high-dimensional datasets like Hi-C. Alternatively, t-Distributed Stochastic Neighbor Embedding (t-SNE) is able to overcome these problems but has not been applied to predict 3D genome structures. In this manuscript, we present a new workflow called StoHi-C (pronounced "stoic") that uses t-SNE to predict 3D genome structure from Hi-C data. StoHi-C was used to predict 3D genome structures for multiple, independent existing fission yeast Hi-C datasets. Overall, StoHi-C was able to generate 3D genome structures that more clearly exhibit the established principles of fission yeast 3D genomic organization.