  Spatially resolved transcriptomics represents a significant advancement in
single-cell analysis by offering both gene expression data and their
corresponding physical locations. However, this high degree of spatial
resolution entails a drawback, as the resulting spatial transcriptomic data at
the cellular level is notably plagued by a high incidence of missing values.
Furthermore, most existing imputation methods either overlook the spatial
information between spots or compromise the overall gene expression data
distribution. To address these challenges, our primary focus is on effectively
utilizing the spatial location information within spatial transcriptomic data
to impute missing values, while preserving the overall data distribution. We
introduce \textbf{stMCDI}, a novel conditional diffusion model for spatial
transcriptomics data imputation, which employs a denoising network trained
using randomly masked data portions as guidance, with the unmasked data serving
as conditions. Additionally, it utilizes a GNN encoder to integrate the spatial
position information, thereby enhancing model performance. The results obtained
from spatial transcriptomics datasets elucidate the performance of our methods
relative to existing approaches.
