Multi-group cancer outlier differential gene expression detection

Computational Biology and Chemistry

Volumn 31, Issue 2, 2007, Pages 65-71

  Liu, Fang ^a   Wu, Baolin ^a  

^a UNIVERSITY OF MINNESOTA   (United States)

Author keywords

Cancer gene activation heterogeneity; Differential gene expression detection; False discovery rate; Microarray; Outlier; Robust regression

Indexed keywords

DATA REDUCTION; MICROARRAYS; SET THEORY; STATISTICAL METHODS; TUMORS;

CANCER GENE ACTIVATION HETEROGENEITY; DIFFERENTIAL GENE EXPRESSION DETECTION; FALSE DISCOVERY RATE; OUTLIER; ROBUST REGRESSION;

GENE EXPRESSION;

ARTICLE; BIOLOGICAL MODEL; BREAST TUMOR; DNA MICROARRAY; FEMALE; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENETICS; HUMAN; LABORATORY DIAGNOSIS; METHODOLOGY; NEOPLASM; STATISTICAL MODEL;

BREAST NEOPLASMS; FALSE POSITIVE REACTIONS; FEMALE; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION, NEOPLASTIC; HUMANS; MODELS, GENETIC; MODELS, STATISTICAL; NEOPLASMS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS;

EID: 34047115341     PISSN: 14769271     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compbiolchem.2007.02.004     Document Type: Article

References (11)

1. Benjamini Y., and Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Royal Statist. Soc. Ser. B (Methodol.) 57 1 (1995) 289-300
2. Bolstad B., Irizarry R., Astrand M., and Speed T. A comparison of nor-normalization methods for high density oligonucleotide array data based on malization variance and bias. Bioinformatics 19 2 (2003) 185-193
3. Dudoit S., Yang Y.H., Callow M.J., and Speed T.P. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Ray Statist. Sinica 12 1 (2002) 111-139 (a special issue on bioinformatics)
4. Gentleman R., Carey V., Bates D., Bolstad B., Dettling M., Dudoit S., Ellis B., Gautier L., Ge Y., Gentry J., Hornik K., Hothorn T., Huber W., Iacus S., Irizarry R., Leisch F., Li C., Maechler M., Rossini A., Sawitzki G., Smith C., Smyth G., Tierney L., Yang J., and Zhang J. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5 10 (2004) R80
5. Golub T.R., Slonim D.K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J.P., Coller H., Loh M.L., Downing J.R., Caligiuri M.A., Bloom-field C.D., and Lander E.S. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286 5439 (1999) 531-537
6. Kutner M., Nachtsheim C., and Neter J. Applied Linear Regression Models. 4th ed. (2004), McGraw-Hill, Irwin
7. Tibshirani R., and Hastie T. Outlier sums for differential gene expression analysis. Biostatistics 8 1 (2007) 2-8
8. Tomlins S.A., Rhodes D.R., Perner S., Dhanasekaran S.M., Mehra R., Sun X.W., Varambally S., Cao X., Tchinda J., Kuefer R., Lee C., Montie J.E., Shah R.B., Pienta K.J., Rubin M.A., and Chinnaiyan A.M. Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science 310 5748 (2005) 644-648
9. Troyanskaya O.G., Garber M.E., Brown P.O., Botstein D., and Altman R.B. Nonparametric methods for identifying differentially expressed genes in microarray data. Bioinformatics 18 11 (2002) 1454-1461
10. West M., Blanchette C., Dressman H., Huang E., Ishida S., Spang R., Zuzan H., Olson, John A., Marks J., and Nevins J.R. Predicting the clinical status of human breast cancer by using gene expression profiles. PNAS 98 20 (2001) 11462-11467
11. Wu, B., 2006. Cancer outlier differential gene expression detection. Biostatistics. Advance Access published online on October 4, 2006. Available at http://dx.doi.org/10.1093/biostatistics/kxl029.