Influence of Cell-type Ratio on Spatially Resolved Single-cell Transcriptomes using the Tangram Algorithm: Based on Implementation on Single-Cell and MxIF Data

AUTHORS

Cui CCan , Bao SShunxing , Li JJia , Deng RRuining , Remedios LWLucas W , Asad ZZuhayr , Chiron SSophie , Lau KSKen S , Wang YYaohong , Coburn LALori A , Wilson KTKeith T , Roland JTJoseph T , Landman BABennett A , Liu QQi , Huo YYuankai . Proceedings of SPIE--the International Society for Optical Engineering. 2023 04 07; 12471().

PMID: 37324550[PubMed].

ABSTRACT

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. 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 classification accuracy.