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

doi:10.1117/12.2654135

7 April 2023 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

Author Affiliations +

Proceedings Volume 12471, Medical Imaging 2023: Digital and Computational Pathology; 124710A (2023) https://doi.org/10.1117/12.2654135
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

The Tangram algorithm is a benchmarking method of aligning single-cell 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 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 spatial data, cell-type ratios were different. In this work, both simulation and empirical validation were conducted to quantitatively explore the impact of the mismatched cell-type ratio on the Tangram mapping in different situations. Results show that the cell-type difference has a negative influence on annotation mapping accuracy.

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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, and Yuankai Huo "Influence of cell-type ratio on spatially resolved single-cell transcriptomes using the Tangram algorithm: based on implementation on single-cell and MxIF data", Proc. SPIE 12471, Medical Imaging 2023: Digital and Computational Pathology, 124710A (7 April 2023); https://doi.org/10.1117/12.2654135

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