Skip to main content
Log in

Rigorous quantitative sciences integration—the foundation of high-dimensional genomic research

  • Research Paper
  • Published:
Clinical & Experimental Metastasis Aims and scope Submit manuscript

Abstract

With the recent rapid increases in the high-dimensionality of genomic data generation comes an increased burden on the biostatisticians and bioinformaticians who process and analyze this data. Study designs must be adapted to the volume of data now available, eliminating designs that rely on fishing and taking advantage of the massive amounts of publically available genomic data through data-mining. Most importantly, it is no longer sufficient to have a single person handling the data analysis. To get the breadth of expertise needed to analyze high-dimensional data and to have the appropriate checks to eliminate the costly mistakes that are so easy to make when handling this volume of data, specialists from many different areas of quantitative sciences must be brought together to approach high-dimensional data analysis as an integrated team.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Shyr Y (2003) Statistical strategies for analyzing the microarray data in human lung cancer. Lung Cancer 41(2003):90–91

    Article  Google Scholar 

  2. Yamagata N, Shyr Y, Yanagisawa K, Edgerton M, Dang TP, Gonzalez A, Nadaf S, Larsen P, Roberts JR, Nesbitt JC, Jensen R, Levy S, Moore JH, Minna JD, Carbone DP (2003) A training-testing approach to the molecular classification of resected non-small cell lung cancer. Clin Cancer Res 9(13):4695–4704

    PubMed  CAS  Google Scholar 

  3. Yanagisawa K, Shyr Y, Xu BJ, Massion PP, Larsen PH, White BC, Roberts JR, Edgerton M, Gonzalez A, Nadaf S, Moore JH, Caprioli RM, Carbone DP (2003) Proteomic patterns of tumour subsets in non-small-cell lung cancer. Lancet 362(9382):433–439

    Article  PubMed  CAS  Google Scholar 

  4. Xu BJ, Gonzalez AL, Kikuchi T, Yanagisawa K, Massion PP, Wu H, Mason SE, Olson SJ, Shyr Y, Carbone DP, Caprioli RM (2008) MALDI-MS derived prognostic protein markers for resected non-small cell lung cancer. Proteomics Clin Appl 2(10–11):1508–1517

    Article  PubMed  CAS  Google Scholar 

  5. Smith JJ, Deane NG, Wu F, Merchant NB, Zhang B, Jiang A, Lu P, Johnson JC, Schmidt C, Bailey CE, Eschrich S, Kis C, Levy S, Washington MK, Heslin MJ, Coffey RJ, Yeatman TJ, Shyr Y, Beauchamp RD (2010) Experimentally derived metastasis gene expression profile predicts recurrence and death in patients with colon cancer. Gastroenterology 138(3):958–968

    Article  PubMed  CAS  Google Scholar 

  6. Lehmann BD, Bauer JA, Chen X, Sanders ME, Chakravarthy AB, Shyr Y, Pietenpol JA (2011) Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest 121(7):2750–2767

    Article  PubMed  CAS  Google Scholar 

  7. Potti A, Dressman HK, Bild A, Riedel RF, Chan G, Sayer R, Cragun J, Cottrill H, Kelley MJ, Petersen R, Harpole D, Marks J, Berchuck A, Ginsburg GS, Febbo P, Lancaster J, Nevins JR (2006) Genomic signatures to guide the use of chemotherapeutics. Nat Med 12(11):1294–1300

    Article  PubMed  CAS  Google Scholar 

  8. Baggerly KA, Coombes KR (2009) Deriving chemosensitivity from cell lines: forensic bioinformatics and reproducible research in high-throughput biology. Ann Appl Stat 3(4):1309–1334

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yu Shyr.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shyr, Y. Rigorous quantitative sciences integration—the foundation of high-dimensional genomic research. Clin Exp Metastasis 29, 641–643 (2012). https://doi.org/10.1007/s10585-012-9508-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10585-012-9508-y

Keywords

Navigation