今天的内容讲讲单细胞文章中经常出现的展示细胞marker的图： tsne / umap 图、 热图 、 堆叠小提琴图 、 气泡图，每个图我都会用两种方法绘制。 使用的数据来自文献：Single-cell transcriptomics reveals regulators underlying immune cell diversity and immune subtypes associated with prognosis in nasopharyngeal carcinoma. 去年7月发表在Cell Research上的关于 鼻咽癌 的文章，数据下载： ncbi.nlm.nih.gov/geo/qu. 髓系细胞数量相对少一些，为了方便演 …

2025年1月11日 · 1．定义：UMAP图是一种基于非线性降维的可视化方式，将高维数据映射到二维或三维空间，并保持数据之间的相对距离和结构，从而使得聚类、异质性和样本间的差异更为明显。

2023年1月20日 · UMAP is an algorithm that takes a high-dimensional dataset (such as a single-cell RNA dataset) and reduces it to a low-dimensional plot that retains much of the original information. Most, if not all, single-cell RNA sequencing datasets contain thousands of gene expression counts per individual cell.

2020年10月14日 · Uniform manifold approximation and projection (UMAP) has been rapidly adopted by the population genetics community to study population structure. It has...

2018年12月3日 · Recently, a nonlinear dimensionality-reduction technique, uniform manifold approximation and projection (UMAP), was developed for the analysis of any type of...

2024年5月24日 · Principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) are among the tools life scientists turn to for...

2024年9月18日 · 看到了一篇Nature的UMAP图，展示的是单细胞，但是特点是标记了celltype的同时指示了marker基因。

2025年3月12日 · Instead of simple linear transformations, UMAP constructs a high-dimensional graph representation of the data and optimizes a lower-dimensional embedding that retains meaningful relationships. This allows it to capture subtle variations in biological data, such as gene expression patterns or cellular heterogeneity.

UMAP is a non-linear dimensionality reduction algorithm particularly well-suited for visualizing high-dimensional data by modelling each high-dimensional object by a two- or three-dimensional points (UMAP projections) in such a way that similar objects are modelled by nearby points and dissimilar objects are modelled by distant points.

To start us off, PCA is linear, whereas t-SNE and UMAP are non-linear methods. What does that mean? In essence, linear relationships mean simple, straight-line relationships where things change at a constant rate. For example, if the expression of gene X is associated with half the expression of gene Y.