Supervised principal component analysis for gene set enrichment of microarray data with continuous or survival outcomes

@article{Chen2008SupervisedPC,
  title={Supervised principal component analysis for gene set enrichment of microarray data with continuous or survival outcomes},
  author={Xi Chen and Lily Wang and Jonathan D. Smith and Bing Zhang},
  journal={Bioinformatics},
  year={2008},
  volume={24 21},
  pages={2474-81}
}
MOTIVATION Gene set analysis allows formal testing of subtle but coordinated changes in a group of genes, such as those defined by Gene Ontology (GO) or KEGG Pathway databases. We propose a new method for gene set analysis that is based on principal component analysis (PCA) of genes expression values in the gene set. PCA is an effective method for reducing high dimensionality and capture variations in gene expression values. However, one limitation with PCA is that the latent variable… CONTINUE READING

Citations

Publications citing this paper.
Showing 1-10 of 32 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 44 references

Controlling the false discovery rate: a new and powerful approach to multiple testing

  • Y. Benjamini, Y. Hochberg
  • J. R. Stat. Soc. B,
  • 1995
Highly Influential
5 Excerpts

An integrated approach for the analysis of biological pathways using mixed models

  • L Wang
  • PLoS Genet.,
  • 2008
1 Excerpt

Similar Papers

Loading similar papers…