Efficient visualization for large-scale and high-dimensional single-cell data

Machine learning;Computational biology;Single-cell data analysis;Visualization;Dimensionality reduction;Network embedding

Kim, Juho ; Koyejo ; Oluwasanmi ; Peng ; Jian

关键词: Machine learning;    Computational biology;    Single-cell data analysis;    Visualization;    Dimensionality reduction;    Network embedding;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/99418/KIM-THESIS-2017.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

This thesis is concerned with developing an efficient and scalable visualization method for large-scale and high-dimensional single-cell data.Single-cell analysis can uncover the mysteries in the state of individual cells and enable us to construct new models 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.