A Method for Analyzing Censored Survival Phenotype with Gene Expression Data Public Deposited

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Creator
  • Wu, Tongtong
    • Other Affiliation: Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, USA
  • Li, Ker-Chau
    • Other Affiliation: Institute of Statistical Science, Academia Sinica, Taipei 115, Taiwan; Department of Statistics, University of California, Los Angeles, CA, 90095-1554, USA
  • Chen, Chun-Houh
    • Other Affiliation: Institute of Statistical Science, Academia Sinica, Taipei 115, Taiwan
  • Yuan, Shinsheng
    • Other Affiliation: Institute of Statistical Science, Academia Sinica, Taipei 115, Taiwan
Abstract
  • Abstract Background Survival time is an important clinical trait for many disease studies. Previous works have shown certain relationship between patients' gene expression profiles and survival time. However, due to the censoring effects of survival time and the high dimensionality of gene expression data, effective and unbiased selection of a gene expression signature to predict survival probabilities requires further study. Method We propose a method for an integrated study of survival time and gene expression. This method can be summarized as a two-step procedure: in the first step, a moderate number of genes are pre-selected using correlation or liquid association (LA). Imputation and transformation methods are employed for the correlation/LA calculation. In the second step, the dimension of the predictors is further reduced using the modified sliced inverse regression for censored data (censorSIR). Results The new method is tested via both simulated and real data. For the real data application, we employed a set of 295 breast cancer patients and found a linear combination of 22 gene expression profiles that are significantly correlated with patients' survival rate. Conclusion By an appropriate combination of feature selection and dimension reduction, we find a method of identifying gene expression signatures which is effective for survival prediction.
Date of publication
Identifier
  • doi:10.1186/1471-2105-9-417
  • 18837994
Resource type
  • Article
Rights statement
  • In Copyright
Rights holder
  • Tongtong Wu et al.; licensee BioMed Central Ltd.
License
Journal title
  • BMC Bioinformatics
Journal volume
  • 9
Journal issue
  • 1
Page start
  • 417
Language
  • English
Is the article or chapter peer-reviewed?
  • Yes
ISSN
  • 1471-2105
Bibliographic citation
  • BMC Bioinformatics. 2008 Oct 06;9(1):417
Access
  • Open Access
Publisher
  • BioMed Central Ltd
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