sc-RNA-seq数据分析|| UMAP与t-NSE的区别
2019-06-25 本文已影响35人
周运来就是我
UMAP 将会与tsne一样作为高纬数据可视化的利器，并且优于tsne.
- UMAP 与 t-SNE 均是非线性降维，多用于数据可视化
- UMAP 结构与t-SNE一致
- UMAP 计算更快
- UMAP 能更好地反映高纬结构，比t-SNE有着更好的连续性
- 这种连续性反映到单细胞分析中就是能更好滴可视化细胞的分化状态（UMAP better represents the multi-branched continuous trajectory of hematopoietic development）
- We conclude that both techniques are similar in their visualisation capabilities, but UMAP has a clear advantage over t-SNE in runtime, making it highly plausible to employ UMAP as an alternative to t-SNE in mIF data analysis.
- Most interestingly, UMAP’s branching was able to highlight biological lineages, especially in identifying potential hybrid tumour cells (HTC)
Evaluation of UMAP as an alternative to t-SNE for single-cell data
Herein we comment on the usefulness of UMAP high-dimensional cytometry and single-cell RNA sequencing, notably highlighting faster runtime and consistency, meaningful organization of cell clusters and preservation of continuums in UMAP compared to t-SNE.
Dimensionality reduction for visualizing single-cell data using UMAP
- It also provides the useful and intuitively pleasing feature that it preserves more of the global structure and, notably, the continuity of the cell subsets.
- In addition to making plots easier to interpret, we note that this also improves its utility for gen-erating hypotheses related to cellular development.
- UMAP outputs are faster to compute compared with Barnes–Hut t-SNE, much faster than scvis, and comparable to FIt-SNE. UMAP embeddings are more reproducible than other methods, notably more so than those from t-SNE implementations.
