This is the official implementation of Influence of cell-type ratio on spatially resolved single-cell transcriptomes using the Tangram algorithm: based on implementation on single-cell and MxIF data
Source code repository
https://github.com/hrlblab/tangram
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Description
The Tangram algorithm is a benchmarking method of aligning single-cell (sc/snRNA-seq) data to various forms of spatial data collected from the same region. With this data alignment, the annotation of the single-cell data can be projected to spatial data. However, the cell composition (cell-type ratio) of the single-cell data and spatial data might be different because of heterogeneous cell distribution. Whether the Tangram algorithm can be adapted when the two data have different cell-type ratios has not been discussed in previous works. In our practical application that maps the cell-type classification results of single-cell data to the Multiplex immunofluorescence (MxIF) spatial data, cell-type ratios were different, though they were sampled from adjacent areas.
Referece
Influence of cell-type ratio on spatially resolved single-cell transcriptomes using the Tangram algorithm: based on implementation on single-cell and MxIF data
Can Cui, Shunxing Bao, Jia Li, Ruining Deng, Lucas W Remedios, Zuhayr Asad, Sophie Chiron, Ken S Lau, Yaohong Wang, Lori A Coburn, Keith T Wilson, Joseph T Roland, Bennett A Landman, Qi Liu, Yuankai Huo
SPIE Medical Imaging 2023