Dataset Information

Averaging Strategy To Reduce Variability in Target-Decoy Estimates of False Discovery Rate.

ABSTRACT: Decoy database search with target-decoy competition (TDC) provides an intuitive, easy-to-implement method for estimating the false discovery rate (FDR) associated with spectrum identifications from shotgun proteomics data. However, the procedure can yield different results for a fixed data set analyzed with different decoy databases, and this decoy-induced variability is particularly problematic for smaller FDR thresholds, data sets, or databases. The average TDC (aTDC) protocol combats this problem by exploiting multiple independently shuffled decoy databases to provide an FDR estimate with reduced variability. We provide a tutorial introduction to aTDC, describe an improved variant of the protocol that offers increased statistical power, and discuss how to deploy aTDC in practice using the Crux software toolkit.

SUBMITTER: Keich U

PROVIDER: S-EPMC6919216 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Averaging Strategy To Reduce Variability in Target-Decoy Estimates of False Discovery Rate.

Keich Uri U Tamura Kaipo K Noble William Stafford WS

Journal of proteome research 20190103 2

Decoy database search with target-decoy competition (TDC) provides an intuitive, easy-to-implement method for estimating the false discovery rate (FDR) associated with spectrum identifications from shotgun proteomics data. However, the procedure can yield different results for a fixed data set analyzed with different decoy databases, and this decoy-induced variability is particularly problematic for smaller FDR thresholds, data sets, or databases. The average TDC (aTDC) protocol combats this pro ...[more]

PMID: 30560673

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Project description:BackgroundIn the analysis of microarray data one generally produces a vector of p-values that for each gene give the likelihood of obtaining equally strong evidence of change by pure chance. The distribution of these p-values is a mixture of two components corresponding to the changed genes and the unchanged ones. The focus of this article is how to estimate the proportion unchanged and the false discovery rate (FDR) and how to make inferences based on these concepts. Six published methods for estimating the proportion unchanged genes are reviewed, two alternatives are presented, and all are tested on both simulated and real data. All estimates but one make do without any parametric assumptions concerning the distributions of the p-values. Furthermore, the estimation and use of the FDR and the closely related q-value is illustrated with examples. Five published estimates of the FDR and one new are presented and tested. Implementations in R code are available.ResultsA simulation model based on the distribution of real microarray data plus two real data sets were used to assess the methods. The proposed alternative methods for estimating the proportion unchanged fared very well, and gave evidence of low bias and very low variance. Different methods perform well depending upon whether there are few or many regulated genes. Furthermore, the methods for estimating FDR showed a varying performance, and were sometimes misleading. The new method had a very low error.ConclusionThe concept of the q-value or false discovery rate is useful in practical research, despite some theoretical and practical shortcomings. However, it seems possible to challenge the performance of the published methods, and there is likely scope for further developing the estimates of the FDR. The new methods provide the scientist with more options to choose a suitable method for any particular experiment. The article advocates the use of the conjoint information regarding false positive and negative rates as well as the proportion unchanged when identifying changed genes.

Dataset Information

Averaging Strategy To Reduce Variability in Target-Decoy Estimates of False Discovery Rate.

Publications

Averaging Strategy To Reduce Variability in Target-Decoy Estimates of False Discovery Rate.

Similar Datasets

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