Zha Yuhong, Feng Shaoqing, Gao Peng, Zou Quan, Ma Xiaoke
School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China.
Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province, Xidian University, Xi'an, Shaanxi 710071, China.
Bioinformatics. 2025 Aug 2;41(8). doi: 10.1093/bioinformatics/btaf419.
Cell type deconvolution deciphers spatial distribution of mRNA transcripts at single cell level by integrating single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data to infer mixture of cell types of spots in slices. Current algorithms are criticized for neglecting connection between scRNA-seq and spatial transcriptomics data, as well as time-consuming, hampering their application to large-scale datasets.
In this study, we propose a joint learning nonnegative matrix factorization algorithm for fast cell type deconvolution (aka jMF2D), which integrates scRNA-seq and spatial transcriptomics data with network models. To bridge scRNA-seq and spatial transcriptomics data, jMF2D jointly learns cell type similarity network to enhance quality of signatures of cell types, thereby promoting accuracy and efficiency of deconvolution. Experiments demonstrate that jMF2D outperforms state-of-the-art baselines in terms of accuracy by saving about 90% running time on various datasets generated by different platforms. Furthermore, it can also facilitates the identification of spatial domains and bio-marker genes, providing an efficient and effective model for analyzing spatial transcriptomics data.
The software is coded using python, and is free available for academic https://github.com/xkmaxidian/jMF2D.
细胞类型反卷积通过整合单细胞RNA测序（scRNA-seq）和空间转录组学数据来推断切片中斑点的细胞类型混合物，从而在单细胞水平上破译mRNA转录本的空间分布。当前的算法因忽略scRNA-seq和空间转录组学数据之间的联系以及耗时问题而受到批评，这阻碍了它们在大规模数据集上的应用。
在本研究中，我们提出了一种用于快速细胞类型反卷积的联合学习非负矩阵分解算法（即jMF2D），该算法将scRNA-seq和空间转录组学数据与网络模型相结合。为了弥合scRNA-seq和空间转录组学数据之间的差距，jMF2D联合学习细胞类型相似性网络以提高细胞类型特征的质量，从而提高反卷积的准确性和效率。实验表明，jMF2D在准确性方面优于现有最先进的基线方法，在由不同平台生成的各种数据集上运行时间节省约90%。此外，它还可以促进空间域和生物标志物基因的识别，为分析空间转录组学数据提供了一个高效且有效的模型。
该软件使用Python编码，可在https://github.com/xkmaxidian/jMF2D上免费获取以供学术使用。
