SpatialDWLS: accurate deconvolution of spatial transcriptomic data.
Recent development of spatial transcriptomic technologies has made it possible to characterize cellular heterogeneity with spatial information. However, the technology often does not have sufficient resolution to distinguish neighboring cell types. Here, we present spatialDWLS, to quantitatively estimate the cell-type composition at each spatial location. We benchmark the performance of spatialDWLS by comparing it with a number of existing deconvolution methods and find that spatialDWLS outperforms the other methods in terms of accuracy and speed. By applying spatialDWLS to a human developmental heart dataset, we observe striking spatial temporal changes of cell-type composition during development.
1. Single-cell and spatial transcriptomics approaches of cardiovascular development and disease.
2. DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence.
3. A new era for plant science: spatial single-cell transcriptomics.
4. Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data.
5. Encoding Method of Single-cell Spatial Transcriptomics Sequencing.
1. Robust enumeration of cell subsets from tissue expression profiles (HGU133Plus2)
2. High spatial resolution multi-omics atlas sequencing of mouse embryos
3. Cell type prioritization in single-cell data
4. Seq-Scope: Submicrometer-resolution spatial barcoding technology that enables microscopic examination of tissue transcriptome at single cell and subcellular levels
5. GeoMx Cancer Transcriptome Atlas (CTA) mRNA assay on an FFPE cell pellet array of mixed HEK293T and CCRF-CEM cell lines.