Uniform Manifold Approximation and Projection, or UMAP, is a dimensionality reduction technique that allows users to create new UMAP X and UMAP Y parameters from a high-dimensional dataset. UMAP is now available in FCS Express through its new Pipelines feature.

UMAP is a dimensionality reduction technique that allows users to create new UMAP X and UMAP Y parameters from a high-dimensional dataset. Please watch this brief video to learn more about how to use UMAP in FCS Express.

2024年12月20日 · 选中新的合并后的fcs文件，出现下图以后，再右键选择 Create Gate on Peaks，这个时候各个组的编号会被识别出来。 这是两个亚群—— 本文参与 腾讯云自媒体同步曝光计划，分享自微信公众号。 原始发表：2024-12-18，如有侵权请联系 [email protected] 删除.

2021年10月4日 · Uniform Manifold Approximation and Projection, or UMAP, is a dimensionality reduction technique that allows users to create new UMAP X and UMAP Y parameters from a...

FCS Express integrates both t-SNE and UMAP via an easy to use interface where you simply select the parameters from your flow cytometry data to include and choose the variables for the algorithm to run. Drag and drop the transformation to any plot to calculate and view the results.

2021年2月12日 · Flow cytometry is a powerful tool to analyse cells based on their size, granularity and expression of various intracellular and membrane bound proteins. Thereby, the datasets it produces are multidimensional, with thousands of cells and protein expression levels for …

UMAP 调整及常见参数整理. UMAP（Uniform Manifold Approximation and Projection）是一种广泛用于高维数据降维的算法，在单细胞分析中常用于可视化细胞的聚类和群体关系。以下是 UMAP 的常见参数及调整建议。 UMAP 常用参数说明 1. dims. 作用：指定用于降维的主成分（ PCA ...

2025年1月9日 · UMAP 图，全称是 统一流形逼近与投影图，是数据降维可视化的神器 它能把复杂的高维数据，巧妙地投影到二维或三维空间，让我们一眼看清数据分布与关系。 在 单细胞测序分析 里，UMAP 图超有用。 不同颜色代表不同细胞类型，一个个点就像细胞 “小居民”，聚集成不同 “社区”，帮我们快速找到细胞类群。 和 PCA 、 t-SNE 比，UMAP 可厉害啦。 PCA 像个 “直线思维” 的老实人，擅长处理线性数据；t-SNE 是 “细节控”，但计算慢；UMAP 则是 “六边形战士”，兼 …

2025年2月25日 · To accelerate the embedding of new FCS data, we implemented a neural network as suggested by Sainburg et al. via the umap-learn Python package. To mitigate the sensitivity of classifiers to the specific scale of features, we provided an option for feature scaling based on a standard scaler fitted to the training data.

UMAP Layout allows you to change the visualization of your clustering parameter on a Heat Map plot using either a clustering assignment or a Minimum Spanning Tree as input parameter. The UMAP Layout organizes cluster nodes with on a UMAP map.