title: Statistical methods of translating microarray data into clinically relevant diagnostic information in colorectal cancer.
abstract: BACKGROUND
  It is a common practice in cancer microarray experiments that a normal tissue is collected from the same individual from whom the tumor tissue was taken. The indirect design is usually adopted for the experiment that uses a common reference RNA hybridized both to normal and tumor tissues. However, it is often the case that the test material is not large enough for the experimenter to extract enough RNA to conduct the microarray experiment. Hence, collecting n cases does not necessarily end up with a matched pair sample of size n. Instead we usually have a matched pair sample of size n1, and two independent samples of sizes n2 and n3, respectively, for 'reference versus normal tissue only' and 'reference versus tumor tissue only' hybridizations (n=n1 + n2 + n3). Standard statistical methods need to be modified and new statistical procedures are developed for analyzing this mixed dataset.
  
  
  RESULTS
  We propose a new test statistic, t3, as a means of combining all the information in the mixed dataset for detecting differentially expressed (DE) genes between normal and tumor tissues. We employed the extended receiver operating characteristic approach to the mixed dataset. We devised a measure of disagreement between a RT-PCR experiment and a microarray experiment. Hotelling's T2 statistic is employed to detect a set of DE genes and its prediction rate is compared with the prediction rate of a univariate procedure. We observe that Hotelling's T2 statistic detects DE genes more efficiently than a univariate procedure and that further research is warranted on the formal test procedure using Hotelling's T2 statistic.
  
  
  BACKGROUND
  bskim@yonsei.ac.kr.
authors.affiliation: Department of Applied Statistics, College of Medicine, Yonsei University Seoul, South Korea. bskim@yonsei.ac.kr
authors.forename: Byung Soo
authors.initials: BS
authors.lastname: Kim
authors.forename: Inyoung
authors.initials: I
authors.lastname: Kim
authors.forename: Sunho
authors.initials: S
authors.lastname: Lee
authors.forename: Sangcheol
authors.initials: S
authors.lastname: Kim
authors.forename: Sun Young
authors.initials: SY
authors.lastname: Rha
authors.forename: Hyun Cheol
authors.initials: HC
authors.lastname: Chung
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000235:genetics / Q000378:metabolism*; D015179:Colorectal Neoplasms / Q000175:diagnosis* / Q000235:genetics / Q000378:metabolism*; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D005819:Genetic Markers / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000235:genetics; D005820:Genetic Testing / Q000379:methods*; D006801:Humans; D008297:Male; D008875:Middle Aged; D008957:Models, Genetic; D015233:Models, Statistical; D009363:Neoplasm Proteins / Q000235:genetics / Q000378:metabolism*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor; D005819:Genetic Markers; D009363:Neoplasm Proteins