提出新算法提升多轴模型抗乘性噪声能力，适用于单细胞测序数据。
Making Multi-Axis Models Robust to Multiplicative Noise: How, and Why?
- 基于图学习构建抗乘性噪声的多轴模型
- 在单细胞表达图谱数据上结构更优
- 适合处理测序技术偏差的生物信息研究
本文提出一种图学习算法 MED-MAGMA，用于拟合受乘性噪声污染的多轴（克罗内克和结构）模型。此类噪声在单细胞RNA测序等应用中自然存在，能有效捕捉测序平台的技术偏差。我们在单细胞表达图谱中所有公开数据集（限定大小范围内）与先前方法进行对比，结果表明所提方法学习到的网络在局部与全局结构上均表现更优。MED-MAGMA 已作为 Python 包开源发布。
原文摘要 · Abstract (English)
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).
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。