Dimensionality reduction by UMAP reinforces sample heterogeneity analysis in bulk transcriptomic data.
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DOI: 10.1016/j.celrep.2021.109442
Abstract: Transcriptomic analysis plays a key role in biomedical research. Linear dimensionality reduction methods, especially principal-component analysis (PCA), are widely used in detecting sample-to-sample heterogeneity, while recently developed non-linear methods, such as t-distributed stochastic neighbor embedding… read more here.
Keywords: sample heterogeneity; analysis; bulk transcriptomic; dimensionality reduction ... See more keywords