A framework for analyzing single-cell genomics data, in which geometrical properties are harnessed to obtain insights on cellular diversity, including precise clustering, clear visualizations, and ...

Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction. The ...

Abstract: Dimensionality reduction methods are employed to decrease data dimensionality, either to enhance machine learning performance or to facilitate data visualization in two or three-dimensional ...

This Python program provides a comprehensive pipeline for processing ANNOVAR files, converting them into AnnData format, and generating UMAP visualizations along with various summary reports based on ...

Distortions from traditional dimensionality reduction methods obscure relationships in high-dimensional single-cell data, thus impeding biological insights. We introduce DTNE (diffusive topology ...

To understand the importance of eIF4F components, we employed computational methods on large public datasets to investigate the impact of positive selection on eIF4F dysregulation in cancer. By ...

Reduced-dimension or spatial in situ scatter plots are widely employed in bioinformatics papers analyzing single-cell data to present phenomena or cell-conditions of interest in cell groups. When ...

降维不仅仅是为了数据可视化。它还可以识别高维空间中的关键结构并将它们保存在低维嵌入中来克服“维度诅咒” 本文将介绍一种流行的降维技术Uniform Manifold Approximation and Projection (UMAP)的内部工作原理，并提供一个 Python 示例。 (UMAP) 如何工作的？ 分析 UMAP ...