Novel computational model of gastrula morphogenesis to identify spatial discriminator genes by self-organizing map (SOM) clustering.

ABSTRACT: Deciphering the key mechanisms of morphogenesis during embryonic development is crucial to understanding the guiding principles of the body plan and promote applications in biomedical research fields. Although several computational tissue reconstruction methods using cellular gene expression data have been proposed, those methods are insufficient with regard to arranging cells in their correct positions in tissues or organs unless spatial information is explicitly provided. Here, we report SPRESSO, a new in silico three-dimensional (3D) tissue reconstruction method using stochastic self-organizing map (stochastic-SOM) clustering, to estimate the spatial domains of cells in tissues or organs from only their gene expression profiles. With only five gene sets defined by Gene Ontology (GO), we

SUBMITTER: Mori T 

PROVIDER: S-EPMC6715814 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature