A novel biochemistry experiment named HiRES has been developed to capture both the chromosomal conformations and gene expression levels of individual single cells simultaneously. Nevertheless, when compared to the extensive volume of single-cell Hi-C data generated from individual cells, the number of datasets produced from this experiment remains limited in the scientific community. Hence, there is a requirement for a computational tool that can forecast the levels of gene expression in individual cells using single-cell Hi-C data from the same cells. We trained a graph transformer called scHiGex that accurately and effectively predicts gene expression levels based on single-cell Hi-C data. We conducted a benchmark of scHiGex that demonstrated notable performance on the predictions with an average absolute error of 0.07. Furthermore, the predicted levels of gene expression led to precise categorizations (adjusted Rand index score 1) of cells into distinct cell types, demonstrating that our model effectively captured the heterogeneity between individual cell types. scHiGex is freely available at https://github.com/zwang-bioinformatics/scHiGex.