Nonlinear dimension reduction methods provide a low-dimensional representation of high-dimensional data by applying a Nonlinear transformation. However, the complexity of the transformations and data structures can create wildly different representations depending on the method and hyper-parameter choices. It is difficult to determine whether any of these representations are accurate, which one is the best, or whether they have missed important structures. The R package quollr has been developed as a new visual tool to determine which method and which hyper-parameter choices provide the most accurate representation of high-dimensional data. The scurve data from the package is used to illustrate the algorithm. Single-cell RNA sequencing (scRNA-seq) data from mouse limb muscles are used to demonstrate the usability of the package.

翻译：非线性降维方法通过施加非线性变换，为高维数据提供低维表示。然而，变换与数据结构的复杂性可能导致不同方法与超参数选择产生截然不同的表示结果。难以判定这些表示是否准确、何者为最优、或是否遗漏重要结构。R包quollr作为一种新型可视化工具被开发，用于确定何种方法及超参数选择能最精确地表征高维数据。本文采用该包附带的scurve数据演示算法原理，并通过小鼠肢体肌肉的单细胞RNA测序（scRNA-seq）数据展示该工具的实际应用价值。