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Thread: Principal Component Analysis (PCA)- A Genetic Analysis application

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    Principal Component Analysis (PCA)- A Genetic Analysis application

    Principal Component Analysis (PCA) is a statistical techniques used to reduce the dimensionality of the data (reduce the number of features in the dataset) by selecting the most important features that capture maximum information about the dataset.

    The features are selected on the basis of variance that they cause in the output. Original features of the dataset are converted to the Principal Components which are the linear combinations of the existing features. The feature that causes highest variance is the first Principal Component. The feature that is responsible for second highest variance is considered the second Principal Component, and so on.

    In simple words, Principal Component Analysis is a method of extracting important features (in the form of components) from a large set of features available in a dataset.

    PCA finds the directions of maximum variance in high-dimensional data and project it onto a smaller dimensional subspace while retaining most of the information. By projecting our data into a smaller space, we’re reducing the dimensionality of our feature space..

    http://theprofessionalspoint.blogspo...l%20Components.

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    Anybody care to model these with percentiles?

    Here are your G25 coordinates...

    ,PC1,PC2,PC3,PC4,PC5,PC6,PC7,PC8,PC9,PC10,PC11,PC1 2,PC13,PC14,PC15,PC16,PC17,PC18,PC19,PC20,PC21,PC2 2,PC23,PC24,PC25
    L_scaled,0.108132,0.142174,-0.008674,-0.044897,0.015387,-0.020638,-0.00329,0.004846,0.007363,0.022233,0.001461,0.0026 98,-0.003122,-0.004404,-0.000271,0.000663,0.000652,0.000127,-0.00088,0.000375,-0.001373,0.001237,-0.006409,0.003494,0.000838

    ,PC1,PC2,PC3,PC4,PC5,PC6,PC7,PC8,PC9,PC10,PC11,PC1 2,PC13,PC14,PC15,PC16,PC17,PC18,PC19,PC20,PC21,PC2 2,PC23,PC24,PC25
    L,0.0095,0.014,-0.0023,-0.0139,0.005,-0.0074,-0.0014,0.0021,0.0036,0.0122,0.0009,0.0018,-0.0021,-0.0032,-0.0002,0.0005,0.0005,0.0001,-0.0007,0.0003,-0.0011,0.001,-0.0052,0.0029,0.0007

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