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Vs (PCA & LDA)

Description

  • PCA reduces dimensions by focusing on the genes with the most variation.

    • This is useful for plotting data with a lot of dimensions (or a lot of genes) onto a simple X/Y plot.
    • However, in this case we're not super interested in the genes with the most variation.
    • Instead, we're interested in maximizing the separability between the two groups so we can make the best decisions.

  • Linear Discriminant Analysis (LDA) is like PCA, but it focuses on maximizing the separability among known categories.

  • Both rank the new axes in order of importance.

    • PC1 (the first new axis that PCA creates) accounts for the most variation in the data.

      - PC2 (the second new axis) does the second best job...

    • LD1 (the first new axis that LDA creates) accounts for the most variation between the categories.

      - LD2 (the second new axis) does the second best job...

  • Both can let you dig in and see which genes are driving the new axes.

  • LDA is like PCA – both try to reduce dimensions

    • PCA looks at the genes with the most variation.
    • LDA tries to maximize the separation of known categories.