The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

Applications of Parallel Coordinate Plots for Visualizing Gene Expression Data

평행좌표 플롯을 활용한 유전자발현 자료의 시각화

  • 박미라 (을지대학교 의과대학 예방의학교실) ;
  • 곽일엽 (고려대학교 통계학과) ;
  • 허명회 (고려대학교 통계학과)
  • Published : 2008.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Visualization of the gene expression data on a low-dimensional graph is helpful in uncovering biological information contained in the data. In this study, we focus on two modified versions of the parallel coordinate plot. First one is the ePCP(enhanced parallel coordinate plot) which shows "near smooth" connecting curves between axes spaced proportionately to the proximity of re-ordered variables. Second one is APCP(Andrews' type parallel coordinate plot) which is obtained by rotating Andrews' plot that has a form of the parallel coordinate plot. Visualization procdures using ePCP and APCP are given for the lymphoma data case.

유전자발현 자료로터 유용한 생물학적 정보를 얻기 위해 여러 시각화 방법이 개발되어 왔다. 본 논문에서는 평행좌표 플롯(parallel coordinate plot: PCP)을 이용하여 유전자발현 패턴을 찾아내어 표현하고자 하였다. 평행좌표 플롯의 두 변형인 ePCP(enhanced parallel coordinate plot)와 APCP(Andrews' type parallel coordinate plot)를 림포마(lymphoma) 자료에 적용하여 62개 샘플을 의미있게 배열하고 300개 유전자를 평활 곡선으로 표현하였다.

Keywords

References

  1. Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., Rosenwald, A., Boldrick, J. C., Sabet, H., Tran, T., Yu, X., Powell, J. I., Yang, L., Marti, G. E., Moore, T., Hudson, J. J., Lu, L., Lewis, D. B., Tibshirani, R., Sherlock, G., Chan, W. C., Greiner, T. C., Weisenburger, D. D., Armitage, J. O., Warnke, R., Levy, R., Wilson, W., Grever, M. R., Byrd, J. C., Bostein, D., Brown, P. O. and Staudt, L. M. (2000). Distinc type of diffuse large b-cell lymphoma identifed by gene expression profiling, Nature, 403, 503-511 https://doi.org/10.1038/35000501
  2. Andrews, D. F. (1972). Plots of high-demensional data, Biometrics, 28, 125-136 https://doi.org/10.2307/2528964
  3. Cheng, K. O., Law, N. F., Siu, W. C. and Liew, A. W. (2008). Identification of coherent patterns in gene expression data using and effcient biclustering algorithm and parallele coordinate visualization, BMC Bioinformatics, 9, 1-28 https://doi.org/10.1186/1471-2105-9-1
  4. Huh, M. H. and Park, D. Y. (2008). Enhancing parallel coordinate plots, Journal of the Korean Statistical Society, 37, 129-133 https://doi.org/10.1016/j.jkss.2007.10.003
  5. Hurley, C. B. (2004). Clustering visualizations of multidimensional data, Journal of Computational & Graph-ical Statistics, 13, 788-806 https://doi.org/10.1198/106186004X12425
  6. Inselberg, A. (1985). The plane with parallel coordinates, The Visual Computer, 1, 69-91 https://doi.org/10.1007/BF01898350
  7. Kwak, I. Y. and Huh, M. H. (2008). Andrews' plot for extended uses, Communications of the Korean Statistical Society, 15, 87-94
  8. Park, M., Jang, Y. J. and Huh, M. H. (2005). Analysis of gene expression data using PC-SOM, In Proceedings of the 55th Session of International Statistical Institute, 313
  9. Prasad, T. V. and Ahson, S. I. (2006). Visualization of microarray gene expression data, Bioinformation, 1, 141-145 https://doi.org/10.6026/97320630001141
  10. Santamaria, R., Theron, R. and Quintales, L. (2008). A visual analytics approach for understanding biclus- tering results from microarray data, BMC Bioinformatics, 9, 1-19 https://doi.org/10.1186/1471-2105-9-1