Cui Can, Bao Shunxing, Li Jia, Deng Ruining, Remedios Lucas W, Asad Zuhayr, Chiron Sophie, Lau Ken S, Wang Yaohong, Coburn Lori A, Wilson Keith T, Roland Joseph T, Landman Bennett A, Liu Qi, Huo Yuankai
Vanderbilt University, Nashville TN 37215, USA.
Vanderbilt University Medical Center, Nashville TN 37232, USA.
Proc SPIE Int Soc Opt Eng. 2023 Feb;12471. Epub 2023 Apr 7.
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.
七巧板算法是一种将单细胞（sc/snRNA-seq）数据与从同一区域收集的各种形式的空间数据进行比对的基准方法。通过这种数据比对，单细胞数据的注释可以投影到空间数据上。然而，由于细胞分布的异质性，单细胞数据和空间数据的细胞组成（细胞类型比例）可能会有所不同。之前的研究工作尚未讨论当这两种数据具有不同的细胞类型比例时，七巧板算法是否适用。在我们将单细胞数据的细胞类型分类结果映射到多重免疫荧光（MxIF）空间数据的实际应用中，尽管它们是从相邻区域采样的，但细胞类型比例却有所不同。在这项工作中，我们进行了模拟和实证验证，以定量探索不同情况下细胞类型比例不匹配对七巧板映射的影响。结果表明，细胞类型差异对分类准确性有负面影响。
