GitHub - hozumiyu/CCP-scRNAseq-UMAP-TSNE: Code for "Analyzing scRNA …
Correlated Clustering and Projection (CCP) is a data-domain dimensionality reduction algorithm. In this repository, you will find the code used to reproduce "Analyzing scRNA-seq data by CCP-assisted UMAP and t-SNE", which is under revision. CCP performs dimensionality reduction in 2 …
Analyzing scRNA-seq data by CCP-assisted UMAP and tSNE
2024年12月13日 · In this work, we utilize CCP as an initialization tool for uniform manifold approximation and projection (UMAP) and t-distributed stochastic neighbor embedding (tSNE). By using 21 publicly available datasets, we have found that CCP significantly improves UMAP and tSNE visualization and dramatically improve their accuracy.
Analyzing scRNA-seq data by CCP-assisted UMAP and tSNE
2024年12月24日 · Similarly, CCP-assisted tSNE improves standard tSNE by 11% ARI, 9% NMI and 8% in ECM. Additionally, CCP-assisted UMAP and tSNE have a higher ECM score, indicating that their clustering is more stable. Notably, both CCP-assisted UMAP and tSNE yield higher ECM scores, indicating more stable clustering. Interestingly, standard tSNE outperforms UMAP.
通过 CCP 辅助的 UMAP 和 tSNE 分析 scRNA-seq 数据,PLOS ONE
2024年12月13日 · 通过使用 21 个公开可用的数据集，我们发现 ccp 显着改善了 umap 和 tsne 可视化，并显著提高了它们的准确性。 更具体地说，CCP 将 ARI 的 UMAP 提高了 22%，NMI 的 14% 和 ECM 的 15%，并将 ARI 的 tSNE 提高了 11%，NMI 的 9% 和 ECM 的 8%。
In this work, we utilize CCP as an initialization tool for uniform manifold approximation and projection (UMAP) and t-distributed stochastic neighbor embed-ding (t-SNE). By using eight publicly available datasets, we have found that CCP significantly improves UMAP and t-SNE visualization and dramatically improve their accuracy.
Title: Analyzing scRNA-seq data by CCP-assisted UMAP and t-SNE …
2023年6月23日 · In this work, we utilize CCP as an initialization tool for uniform manifold approximation and projection (UMAP) and t-distributed stochastic neighbor embedding (t-SNE). By using eight publicly available datasets, we have found that CCP significantly improves UMAP and t-SNE visualization and dramatically improve their accuracy.
Analyzing scRNA-seq data by CCP-assisted UMAP and tSNE
2024年12月13日 · By using 21 publicly available datasets, we have found that CCP significantly improves UMAP and tSNE visualization and dramatically improve their accuracy. More specifically, CCP improves UMAP by 22% in ARI, 14% in NMI and 15% in ECM, and improves tSNE by 11% in ARI, 9% in NMI and 8% in ECM.
Run UMAP and tSNE — Galaxy tutorial Single-cell RNA-seq analysis 0.1 ...
Uniform Manifold Approximation and Projection (UMAP) is a dimensionality reduction technique that has emerged as a powerful tool in analyzing single-cell RNA sequencing (scRNA-seq) data. Unlike traditional methods such as t-distributed stochastic neighbor embedding (t-SNE), UMAP preserves both global and local structure in the data, allowing ...
Analyzing scRNA-seq data by CCP-assisted UMAP and t-SNE
2023年6月23日 · In this work, we utilize CCP as an initialization tool for uniform manifold approximation and projection (UMAP) and t-distributed stochastic neighbor embedding (t-SNE). By using eight publicly...
Chapter 4 Dimensionality reduction | Basics of Single-Cell Analysis ...
The uniform manifold approximation and projection (UMAP) method (McInnes, Healy, and Melville 2018) is an alternative to \(t\)-SNE for non-linear dimensionality reduction. It is roughly similar to \(t\) -SNE in that it also tries to find a low-dimensional representation that preserves relationships between neighbors in high-dimensional space.