DMT-Dens：面向生物数据的保密度流形可视化方法
DMT-Dens: Density-preserving manifold visualization for biological data
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中文总结 AI 辅助
该研究针对生物数据低维嵌入易扭曲采样密度的问题，提出DMT-Dens方法，整合流形对齐与硬对聚合，优化密度相关损失，实现优异保密度效果，同时保持标签可分性。
中文摘要 AI 辅助
研究背景：低维嵌入被广泛用于探索单细胞及其他高维生物数据中的细胞状态异质性。尽管许多方法能保留局部邻域，但可能扭曲处理后观测值的表观采样密度，改变密集与稀疏区域的视觉对比度，增加对稀有、过渡或连续细胞状态群体的解读难度。研究方法：我们提出DMT-Dens，一种基于隐式标记Transformer编码器的参数化流形可视化方法，该模型整合了基于秩的流形对齐与硬对聚合；为保密度，它基于处理后输入与二维嵌入空间中k近邻对数半径估计值的皮尔逊相关系数优化损失函数。实验结果：基准评估显示其保密度效果优异，尤其在生物数据集上，同时保持了有竞争力的标签可分性。可用性：源代码、数据处理脚本及已解析的实验配置可在该httpsURL获取。
英文摘要
Motivation: Low-dimensional embeddings are widely used to explore cell-state heterogeneity in single-cell and other high-dimensional biological data. Although many methods preserve local neighborhoods, they may distort the apparent sampling density of processed observations, altering the visual contrast between dense and sparse regions and complicating the interpretation of rare, transitional, or continuous cell-state populations. Results: We present DMT-Dens, a parametric manifold-visualization method built on a latent-token Transformer encoder. The model integrates rank-based manifold alignment with hard-pair aggregation. To preserve density, it optimizes a loss based on the Pearson correlation between k-nearest-neighbor log-radius estimates in the processed input and two-dimensional embedding spaces. Benchmark evaluations demonstrate strong density preservation, particularly on biological datasets, while retaining competitive label separability. Availability: Source code, data-processing scripts, and resolved experiment configurations are available at https://github.com/Ruizhe-wang/DMT-Dens.
发表机构
- Tsientang Institute for Advanced Study（钱塘高等研究院）
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