Analysis of gene expression in pathophysiological states: Balancing false discovery and false negative rates

Abstract

Nucleotide-microarray technology, which allows the simultaneous measurement of the expression of tens of thousands of genes, has become an important tool in the study of disease. In disorders such as malignancy, gene expression often undergoes broad changes of sizable magnitude, whereas in many common multifactorial diseases, such as diabetes, obesity, and atherosclerosis, the changes in gene expression are modest. In the latter circumstance, it is therefore challenging to distinguish the truly changing from nonchanging genes, especially because statistical significance must be considered in the context of multiple hypothesis testing. Here, we present a balanced probability analysis (BPA), which provides the biologist with an approach to interpret results in the context of the total number of genes truly differentially expressed and false discovery and false negative rates for the list of genes reaching any significance threshold. In situations where the changes are of modest magnitude, sole consideration of the false discovery rate can result in poor power to detect genes truly differentially expressed. Concomitant analysis of the rate of truly differentially expressed genes not identified, i.e., the false negative rate, allows balancing of the two error rates and a more thorough insight into the data. To this end, we have developed a unique, model-based procedure for the estimation of false negative rates, which allows application of BPA to real data in which changes are modest.metabolic disease ͉ microarray analysis ͉ multiple hypothesis testing ͉ statistics

Transcriptomic Response ofListeria monocytogenesto Iron Limitation andfurMutation

“…The false discovery rate has been well described to be a suitable method for microarray analysis and has become one of the most important standards by which genes showing significant changes in transcription are listed and sorted (40,42,51). In a recent proteomic study, researchers compared t statistics and SAM methods to analyze the liquid chromatography-mass spectrometry (LC/MS) proteomic data, and the results indicated that SAM identified a large number of different expressed proteins while overcoming the false-positive results that one would encounter with a t test (50).…”

A framework to identify physiological responses in microarray-based gene expression studies: selection and interpretation of biologically relevant genes

“…Procedures can be applied to correct for multiple testing, such as the family-wise error rate (FWER) or the false discovery rate (FDR) (20,46). However, these procedures can be overly stringent, resulting in identification of only the most important changes and possibly discarding other relevant genes (31).…”

“…This is especially true in nutrition, where dietary interventions result in modest, but biologically important gene expression changes (1,12,33). In the medical field it is also increasingly recognized that the more subtle changes contribute importantly to the outcome (30,31,38).…”

A Non-Parametric Maximum for Reasonable Number of Rejected Hypotheses: Objective Optima for False Discovery Rate and Significance Threshold in Exploratory Research with Application to Ordinal Survey Analysis

“…Storey (2007) proposes an optimal discovery procedure based on maximizing Expected True Positives (ETP) for each Expected False Positive (EFP) among all Single Thresholding Procedures (STP). Norris and Kahn (2006) have proposed balanced probability analysis (BPA) based on three variables: (i) The total number of true positives (TTP); (ii) The false discovery rate (FDR), defined as the aggregate chance that a true null hypothesis is rejected by statistical accident. (iii) The false negative rate (FNR), defined as the number of hypothesis that should truly be rejected but are missing from the significance list divided by the total number of hypothesis that should truly be rejected.…”

“…As Norris and Kahn (2006) have argued, the true FDR can be accurately determined only when the TTP is known. They used an adaptation of the algorithm by Storey and Tibshirani (2003) they estimate the TTPs.…”