title: SCALABLE VISUALIZATION FOR HIGH-DIMENSIONAL SINGLE-CELL DATA.
abstract: Single-cell analysis can uncover the mysteries in the state of individual cells and enable us to construct new models about the analysis of heterogeneous tissues. State-of-the-art technologies for single-cell analysis have been developed to measure the properties of single-cells and detect hidden information. They are able to provide the measurements of dozens of features simultaneously in each cell. However, due to the high-dimensionality, heterogeneous complexity and sheer enormity of single-cell data, its interpretation is challenging. Thus, new methods to overcome high-dimensionality are necessary. Here, we present a computational tool that allows efficient visualization of high-dimensional single-cell data onto a low-dimensional (2D or 3D) space while preserving the similarity structure between single-cells. We first construct a network that can represent the similarity structure between the high-dimensional representations of single-cells, and then, embed this network into a low-dimensional space through an efficient online optimization method based on the idea of negative sampling. Using this approach, we can preserve the high-dimensional structure of single-cell data in an embedded low-dimensional space that facilitates visual analyses of the data.
authors.affiliation: Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA, juhokim2@illinois.edu.
authors.forename: Juho
authors.initials: J
authors.lastname: Kim
authors.forename: Nate
authors.initials: N
authors.lastname: Russell
authors.forename: Jian
authors.initials: J
authors.lastname: Peng
mesh_terms: D000465:Algorithms; D000818:Animals; D001854:Bone Marrow Cells / Q000145:classification / Q000166:cytology / Q000378:metabolism; D016000:Cluster Analysis; D019295:Computational Biology; D021621:Imaging, Three-Dimensional / Q000706:statistics & numerical data*; D051379:Mice; D059010:Single-Cell Analysis / Q000706:statistics & numerical data*