Spatial barcoding-based transcriptomic (ST) data require deconvolution for cellular-level downstream analysis. Here we present SDePER, a hybrid machine learning and regression method to deconvolve ST data using reference single-cell RNA sequencing (scRNA-seq) data. SDePER tackles platform effects between ST and scRNA-seq data, ensuring a linear relationship between them while addressing sparsity and spatial correlations in cell types across capture spots. SDePER estimates cell-type proportions, enabling enhanced resolution tissue mapping by imputing cell-type compositions and gene expressions at unmeasured locations. Applications to simulated data and four real datasets showed SDePER’s superior accuracy and robustness over existing methods.

中文翻译：

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SDePER：一种用于基于空间条形码的转录组数据细胞类型反卷积的混合机器学习和回归方法


基于空间条形码的转录组 （ST） 数据需要去卷积以进行细胞水平下游分析。在这里，我们介绍了 SDePER，这是一种混合机器学习和回归方法，使用参考单细胞 RNA 测序 （scRNA-seq） 数据对 ST 数据进行反卷积。SDePER 解决了 ST 和 scRNA-seq 数据之间的平台效应，确保它们之间的线性关系，同时解决了捕获点之间细胞类型的稀疏性和空间相关性。SDePER 估计细胞类型比例，通过在未测量的位置估算细胞类型组成和基因表达来提高分辨率的组织映射。对模拟数据和四个真实数据集的应用表明，SDePER 的准确性和稳健性优于现有方法。