UMAP (Uniform Manifold Approximation and Projection) is a manifold learning technique suitable for visualizing high-dimensional data.

Example: sc.pl.umap(adata, color='TREM2', vcenter='p50', cmap='RdBu_r') add_outline bool | None (default: False ) If set to True, this will add a thin border around groups of dots.

2022年5月19日 · Scanpy在其发行版中包含了这个数据集的缩减版，该数据集只包含700个细胞和765个高变基因。 此数据集已经过预处理和UMAP计算。 在本篇内容里，我们使用到以下标记基因（来自于已知的文献结论，比如B-cell的标记基因为CD79A, MS4A1）： 基于scanpy， tSNE 、UMAP和其他几个embedding的散点图可以从文档轻松找到。 比如可以看这里的选项列表： …

With scanpy, scatter plots for tSNE, UMAP and several other embeddings are readily available using the sc.pl.tsne, sc.pl.umap etc. functions. See here the list of options. Those functions access the data stored in adata.obsm. For example sc.pl.umap uses the information stored in adata.obsm['X_umap'].

This tutorial explores the visualization possibilities of scanpy and is divided into three sections: Scatter plots for embeddings (eg. UMAP, t-SNE) Identification of clusters using known marker genes. Visualization of differentially expressed genes. In this tutorial, we will use a dataset from 10x containing 68k cells from PBMC. Scanpy ...

2024年6月3日 · Scanpy UMAP (Uniform Manifold Approximation and Projection) is a dimensionality reduction technique often used to visualize high-dimensional data in 2D or 3D. When applied to single-cell RNA sequencing (scRNA-seq) data, UMAP helps in understanding the relationships between different cells.

2021年4月8日 · Single cell RNA-seq analysis is a cornerstone of developmental research and provides a great level of detail in understanding the underlying dynamic processes within tissues. In the context of plants, this highlights some of the …

2024年8月22日 · AFAIK there is no option to change the color of the text, you will need to play with matplotlib for that. You can find tips on how to customize scanpy plots in this tutorial.

2024年4月19日 · scanpy的UMAP可视化优化. 在进行UMAP可视化时，经常使用scanpy.pl.umap()来进行可视化，但是有时不能画出我们想要的结果，这时应该怎么办呢？ 我们通过实例数据集进行演示，代码如下：

2024年11月7日 · My UMAP looks weird. I can see from the annotation, that the same cell type is found at the edges of a large radial/star-shaped “cluster”. Here, the different colors are different immune cell types in a PBMC sample.