CoSTA: unsupervised convolutional neural network learning for spatial transcriptomics analysis.

ABSTRACT:

Background

The rise of spatial transcriptomics technologies is leading to new insights about how gene regulation happens in a spatial context. Determining which genes are expressed in similar spatial patterns can reveal gene regulatory relationships across cell types in a tissue. However, many current analysis methods do not take full advantage of the spatial organization of the data, instead treating pixels as independent features. Here, we present CoSTA: a novel approach to learn spatial similarities between gene expression matrices via convolutional neural network (ConvNet) clustering.

Results

By analyzing simulated and previously published spatial transcriptomics data, we demonstrate that CoSTA learns spatial relationships between genes in a way that emphasizes broader s

SUBMITTER: Xu Y 

PROVIDER: S-EPMC8351440 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature