Single Cells Are Spatial Tokens: Transformers for Spatial Transcriptomic Data Imputation

arXiv:2302.03038v2 Announce Type: replace-cross
Abstract: Spatially resolved transcriptomics brings exciting breakthroughs to single-cell analysis by providing physical locations along with gene expression. However, as a cost of the extremely high spatial resolution, the cellular level spatial transcriptomic data suffer significantly from missing values. While a standard solution is to perform imputation on the missing values, most existing methods either overlook spatial information or only incorporate localized spatial context without the ability to capture long-range spatial information. Using multi-head self-attention mechanisms …

abstract analysis arxiv cells cellular cost cs.ai cs.lg data gene imputation locations missing values q-bio.gn solution spatial standard tokens transformers type values