In this paper we develop a graph-learning algorithm, MED-MAGMA, to fit multi-axis (Kronecker-sum-structured) models corrupted by multiplicative noise. This type of noise is natural in many application domains, such as that of single-cell RNA sequencing, in which it naturally captures technical biases of RNA sequencing platforms. Our work is evaluated against prior work on each and every public dataset in the Single Cell Expression Atlas under a certain size, demonstrating that our methodology learns networks with better local and global structure. MED-MAGMA is made available as a Python package (MED-MAGMA).

翻译：本文提出了一种图学习算法 MED-MAGMA，用于拟合受乘性噪声污染的多轴（Kronecker 和结构）模型。这种噪声在许多应用领域中自然存在，例如单细胞 RNA 测序领域，它自然捕获了 RNA 测序平台的技术偏差。我们在单细胞表达图谱中所有符合特定尺寸的公开数据集上，与先前的工作进行了对比评估，结果表明我们的方法能够学习到具有更好局部和全局结构的网络。MED-MAGMA 已作为 Python 软件包（MED-MAGMA）发布。