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What is Dimensionality Reduction in Single Cell Analysis about?

Dimensionality reduction techniques simplify complex high-dimensional data by reducing the number of features or variables, making the data easier to analyze. Principal component analysis (PCA) is a common linear dimensionality reduction technique that transforms the data into a new coordinate system of orthogonal principal components. PCA identifies patterns in the data and expresses the data in such a way that the first principal component accounts for as much variability in the data as possible, and each

Author

Albert Feng

Language

EN