Back

Spatially Resolved Gene Expression is Not Necessary for Identifying Spatial Domains

Lin, S.; Zhao, Y.; Yuan, Z.

2023-10-18 bioinformatics
10.1101/2023.10.15.562443 bioRxiv
Show abstract

The development of Spatially Resolved Transcriptomics (SRT) technologies has revolutionized the study of tissue organization. We introduce a graph convolutional network with an attention and positive emphasis mechanism, named "BINARY," relying exclusively on binarized SRT data to delineate spatial domains. BINARY outperforms existing methods across various SRT data types while using significantly less input information. Our study suggests that precise gene expression quantification may not always be essential, inspiring further exploration of the broader applications of spatially resolved binarized gene expression data.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.