![]() The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified. Add an offset of 1 to the whole data matrix to deal with zeroes after centered log ratio transformation Pre-filter the raw count data to remove features with. Principal component analysis ( PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing. Principal Component Analysis (PCA) is a linear dimensionality reduction technique that can be utilized for extracting information from a high-dimensional space by projecting it into a lower-dimensional sub-space. ![]()
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