Simultaneous dimension reduction and adjustment for confounding variation.

@article{Lin2016SimultaneousDR,
  title={Simultaneous dimension reduction and adjustment for confounding variation.},
  author={Zhixiang Lin and Can Yang and Ying Jie Zhu and John C. Duchi and Yao Shong Fu and Yong Wang and Bai Jiang and Mahdi Zamanighomi and Xuming Xu and Mingfeng Li and Nenad Sestan and Hongyu Zhao and Wing Hung Wong},
  journal={Proceedings of the National Academy of Sciences of the United States of America},
  year={2016},
  volume={113 51},
  pages={
          14662-14667
        }
}
Dimension reduction methods are commonly applied to high-throughput biological datasets. However, the results can be hindered by confounding factors, either biological or technical in origin. In this study, we extend principal component analysis (PCA) to propose AC-PCA for simultaneous dimension reduction and adjustment for confounding (AC) variation. We show that AC-PCA can adjust for (i) variations across individual donors present in a human brain exon array dataset and (ii) variations of… CONTINUE READING
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