title: False discovery rate, sensitivity and sample size for microarray studies.
abstract: BACKGROUND
  In microarray data studies most researchers are keenly aware of the potentially high rate of false positives and the need to control it. One key statistical shift is the move away from the well-known P-value to false discovery rate (FDR). Less discussion perhaps has been spent on the sensitivity or the associated false negative rate (FNR). The purpose of this paper is to explain in simple ways why the shift from P-value to FDR for statistical assessment of microarray data is necessary, to elucidate the determining factors of FDR and, for a two-sample comparative study, to discuss its control via sample size at the design stage.
  
  
  RESULTS
  We use a mixture model, involving differentially expressed (DE) and non-DE genes, that captures the most common problem of finding DE genes. Factors determining FDR are (1) the proportion of truly differentially expressed genes, (2) the distribution of the true differences, (3) measurement variability and (4) sample size. Many current small microarray studies are plagued with large FDR, but controlling FDR alone can lead to unacceptably large FNR. In evaluating a design of a microarray study, sensitivity or FNR curves should be computed routinely together with FDR curves. Under certain assumptions, the FDR and FNR curves coincide, thus simplifying the choice of sample size for controlling the FDR and FNR jointly.
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet 17177 Stockholm, Sweden. yudi.pawitan@meb.ki.se
authors.forename: Yudi
authors.initials: Y
authors.lastname: Pawitan
authors.forename: Stefan
authors.initials: S
authors.lastname: Michiels
authors.forename: Serge
authors.initials: S
authors.lastname: Koscielny
authors.forename: Arief
authors.initials: A
authors.lastname: Gusnanto
authors.forename: Alexander
authors.initials: A
authors.lastname: Ploner
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005189:False Positive Reactions; D020869:Gene Expression Profiling / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity; D012984:Software