Machine Learning Approaches Identify Genes Containing Spatial Information From Single-Cell Transcriptomics Data.

ABSTRACT: The development of single-cell sequencing technologies has allowed researchers to gain important new knowledge about the expression profile of genes in thousands of individual cells of a model organism or tissue. A common disadvantage of this technology is the loss of the three-dimensional (3-D) structure of the cells. Consequently, the Dialogue on Reverse Engineering Assessment and Methods (DREAM) organized the Single-Cell Transcriptomics Challenge, in which we participated, with the aim to address the following two problems: (a) to identify the top 60, 40, and 20 genes of the Drosophila melanogaster embryo that contain the most spatial information and (b) to reconstruct the 3-D arrangement of the embryo using information from those genes. We developed two independent techniques, l

SUBMITTER: Loher P 

PROVIDER: S-EPMC7902049 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature