Integration with RCTD using one line of code

We provide the MCube::mcubeRCTD() function to integrate MCube with the cell type deconvolution results from RCTD [CMZ+22] (https://github.com/dmcable/spacexr) in just one line of code. Note that the choice of the RCTD mode determines the analysis strategy of MCube:

  • For the ST data with low resolution like Visium, we recommend using the full mode. Then, MCube will analyze all the cell types of interest together and store the results in a single mcube object.

  • For the ST data with high resolution and large sample size like Visium HD and Xenium, we recommend using the doublet mode. Then, MCube will analyze each cell type separately using the spots with a high probability of containing the target cell type according to the deconvolution results. The results will be stored in a list of mcube objects, each corresponding to a cell type.

We also provide three examples of real data analysis, including the Visium adult mouse brain dataset [YFL+24], the Visium human CRC dataset [ORC+25], and the Xenium human CRC dataset [ORC+25]:

References

[CMZ+22]

Dylan M Cable, Evan Murray, Luli S Zou, Aleksandrina Goeva, Evan Z Macosko, Fei Chen, and Rafael A Irizarry. Robust decomposition of cell type mixtures in spatial transcriptomics. Nature Biotechnology, 40(4):517–526, 2022.

[ORC+25] (1,2)

Michelli Faria de Oliveira, Juan Pablo Romero, Meii Chung, Stephen R Williams, Andrew D Gottscho, Anushka Gupta, Susan E Pilipauskas, Seayar Mohabbat, Nandhini Raman, David J Sukovich, and others. High-definition spatial transcriptomic profiling of immune cell populations in colorectal cancer. Nature Genetics, 2025.

[YFL+24]

Yue You, Yuting Fu, Lanxiang Li, Zhongmin Zhang, Shikai Jia, Shihong Lu, Wenle Ren, Yifang Liu, Yang Xu, Xiaojing Liu, and others. Systematic comparison of sequencing-based spatial transcriptomic methods. Nature Methods, 21(9):1743–1754, 2024.