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Researchers at the University of Queensland have recently identified a powerful tool that can be used for the analysis of large-scale patient data sets
Transcriptomics analysis plays a key role in biomedical research
However, can t-SNE and UMAP be applied to transcriptome analysis (bulk RNA-seq) of a large number of cells? How do they compare to traditional methods? These questions have not yet been answered
Therefore, researchers at the University of Queensland, led by Professor Di Yu, compared four different mainstream tools: PCA, MDS, t-SNE and UMAP
Professor Yu explained: “Imagine that we are analyzing a large patient data set, each patient has more than 10,000 genes, then we need a very good way to reduce the complexity of these massive data in order to better Explain
He believes that among the four tools compared, UMAP is very powerful
UMAP is more effective in reporting clusters of patients
"UMAP's algorithm is more based on machine learning, which makes it more powerful than the popular PCA tool, which mainly uses linear methods," said Professor Yu
The UMAP tool is relatively new and is currently only used for biomedical research.
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Yang Yang et al, Dimensionality reduction by UMAP reinforces sample heterogeneity analysis in bulk transcriptomic data, Cell Reports (2021).