Spatial transcriptome RNA sequencing (stRNA-seq) can characterize the gene expression patterns of samples while preserving the original spatial position of tissue, thereby facilitating the understanding of tissue’s biological characteristics and disease’s pathogenesis. However, unlike single-cell RNA sequencing (scRNA-seq), stRNA-seq data of bulk samples do not have single-cell resolution and only measure the averaged gene expression of mixed cells. Therefore, it is necessary to perform deconvolution on the spatial transcriptomic data to infer the relative abundance of cell types and/or cell-type specific gene expression profiles (GEPs). Recently, deconvolution methods have been developed, while many of them cannot simultaneously estimate the cell type abundance at each spot and cell type-specific GEPs and may not utilize spatial localization information fully. Here, we propose a deconvolution method for spatial transcriptomic data, SpatialDSSC, to simultaneously estimate cell type-specific GEPs and cell type abundance. SpatialDSSC models on sample-sample and gene-gene similarities from gene expression and sample-sample similarity from spatial position and leverages scRNA-seq data or provided cell-type specific GEPs as references. By testing on multiple sets of simulated and real data and comparing with the existing deconvolution methods, we demonstrate the effectiveness and accuracy of SpatialDSSC in inferring cell type-specific GEPs and cell type abundance.