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Points of Significance

Principal component analysis

Nature Methods volume 14, pages 641–642 (2017) Cite this article

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PCA helps you interpret your data, but it will not always find the important patterns.

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Figure 1: PCA geometrically projects data onto a lower-dimensional space.
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Figure 2: PCA reduction of nine expression profiles from six to two dimensions.
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Figure 3: PCA can help identify clusters in the data.
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Figure 4: The assumptions of PCA place limitations on its use.
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References

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Authors and Affiliations

  1. PhD candidate at Canada's Michael Smith Genome Sciences Centre,

    Jake Lever

  2. staff scientist at Canada's Michael Smith Genome Sciences Centre,

    Martin Krzywinski

  3. Professor of Statistics at The Pennsylvania State University,

    Naomi Altman

Authors
  1. Jake Lever
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  2. Martin Krzywinski
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  3. Naomi Altman
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The authors declare no competing financial interests.

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Lever, J., Krzywinski, M. & Altman, N. Principal component analysis. Nat Methods 14, 641–642 (2017). https://doi.org/10.1038/nmeth.4346

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