Nonlinear Fitting Method for Determining Local False Discovery Rates from Decoy Database Searches

2008 ◽  
Vol 7 (9) ◽  
pp. 3661-3667 ◽  
Author(s):  
Wilfred H. Tang ◽  
Ignat V. Shilov ◽  
Sean L. Seymour
Keyword(s):  
False Discovery Rates ◽  
Decoy Database ◽  
Nonlinear Fitting ◽  
False Discovery ◽  
Fitting Method ◽  
Discovery Rates

scholarly journals Comparison of false-discovery rates of various decoy databases

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Sangjeong Lee ◽  
Heejin Park ◽  
Hyunwoo Kim
Keyword(s):  
Human Protein ◽  
Protein Database ◽  
False Discovery Rates ◽  
Decoy Database ◽  
Protein Databases ◽  
False Discovery ◽  
Large Databases ◽  
Human Protein Database ◽  
Target Database ◽  
Discovery Rates

Abstract Background The target-decoy strategy effectively estimates the false-discovery rate (FDR) by creating a decoy database with a size identical to that of the target database. Decoy databases are created by various methods, such as, the reverse, pseudo-reverse, shuffle, pseudo-shuffle, and the de Bruijn methods. FDR is sometimes over- or under-estimated depending on which decoy database is used because the ratios of redundant peptides in the target databases are different, that is, the numbers of unique (non-redundancy) peptides in the target and decoy databases differ. Results We used two protein databases (the UniProt Saccharomyces cerevisiae protein database and the UniProt human protein database) to compare the FDRs of various decoy databases. When the ratio of redundant peptides in the target database is low, the FDR is not overestimated by any decoy construction method. However, if the ratio of redundant peptides in the target database is high, the FDR is overestimated when the (pseudo) shuffle decoy database is used. Additionally, human and S. cerevisiae six frame translation databases, which are large databases, also showed outcomes similar to that from the UniProt human protein database. Conclusion The FDR must be estimated using the correction factor proposed by Elias and Gygi or that by Kim et al. when (pseudo) shuffle decoy databases are used.

False discovery rates and copy number variation

Biometrika ◽  
2011 ◽  
Vol 98 (2) ◽  
pp. 251-271 ◽  
Author(s):  
Bradley Efron ◽  
Nancy R. Zhang
Keyword(s):  
Copy Number Variation ◽  
Copy Number ◽  
False Discovery Rates ◽  
False Discovery ◽  
Number Variation ◽  
Discovery Rates

scholarly journals Signal identification for rare and weak features: higher criticism or false discovery rates?

Biostatistics ◽  
2012 ◽  
Vol 14 (1) ◽  
pp. 129-143 ◽  
Author(s):  
Bernd Klaus ◽  
Korbinian Strimmer
Keyword(s):  
Signal Identification ◽  
False Discovery Rates ◽  
Higher Criticism ◽  
False Discovery ◽  
Discovery Rates

scholarly journals A Comparison of Two Classes of Methods for Estimating False Discovery Rates in Microarray Studies

Scientifica ◽  
2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Emily Hansen ◽  
Kathleen F. Kerr
Keyword(s):  
Differentially Expressed Genes ◽  
Null Distribution ◽  
Differentially Expressed ◽  
Test Statistics ◽  
False Discovery Rates ◽  
Model Method ◽  
False Discovery ◽  
Microarray Studies ◽  
Discovery Rates

The goal of many microarray studies is to identify genes that are differentially expressed between two classes or populations. Many data analysts choose to estimate the false discovery rate (FDR) associated with the list of genes declared differentially expressed. Estimating an FDR largely reduces to estimatingπ1, the proportion of differentially expressed genes among all analyzed genes. Estimatingπ1is usually done throughP-values, but computingP-values can be viewed as a nuisance and potentially problematic step. We evaluated methods for estimatingπ1directly from test statistics, circumventing the need to computeP-values. We adapted existing methodology for estimatingπ1fromt- andz-statistics so thatπ1could be estimated from other statistics. We compared the quality of these estimates to estimates generated by two established methods for estimatingπ1fromP-values. Overall, methods varied widely in bias and variability. The least biased and least variable estimates ofπ1, the proportion of differentially expressed genes, were produced by applying the “convest” mixture model method toP-values computed from a pooled permutation null distribution. Estimates computed directly from test statistics rather thanP-values did not reliably perform well.

scholarly journals False discovery rates: a new deal

Biostatistics ◽  
2016 ◽  
pp. kxw041 ◽  
Author(s):  
Matthew Stephens
Keyword(s):  
New Deal ◽  
False Discovery Rates ◽  
False Discovery ◽  
Discovery Rates

Large and ancient linguistic areas

2020 ◽  
pp. 78-100
Author(s):  
Balthasar Bickel
Keyword(s):  
Regression Models ◽  
Large Scale ◽  
Population History ◽  
False Discovery Rates ◽  
Ancient Population ◽  
False Discovery ◽  
Language Universals ◽  
Discovery Rates ◽  
Pacific Area

Large-scale areal patterns point to ancient population history and form a well-known confound for language universals. Despite their importance, demonstrating such patterns remains a challenge. This chapter argues that large-scale area hypotheses are better tested by modeling diachronic family biases than by controlling for genealogical relations in regression models. A case study of the Trans-Pacific area reveals that diachronic bias estimates do not depend much on the amount of phylogenetic information that is used when inferring them. After controlling for false discovery rates, about 39 variables in WALS and AUTOTYP show diachronic biases that differ significantly inside vs. outside the Trans-Pacific area. Nearly three times as many biases hold outside than inside the Trans-Pacific area, indicating that the Trans-Pacific area is not so much characterized by the spread of biases but rather by the retention of earlier diversity, in line with earlier suggestions in the literature.

scholarly journals Improving sensitivity in proteome studies by analysis of false discovery rates for multiple search engines

PROTEOMICS ◽  
2009 ◽  
Vol 9 (5) ◽  
pp. 1220-1229 ◽  
Author(s):  
Andrew R. Jones ◽  
Jennifer A. Siepen ◽  
Simon J. Hubbard ◽  
Norman W. Paton
Keyword(s):  
Search Engines ◽  
False Discovery Rates ◽  
False Discovery ◽  
Discovery Rates

scholarly journals What’s Driving False Discovery Rates?

2008 ◽  
Vol 7 (1) ◽  
pp. 45-46 ◽  
Author(s):  
David L. Tabb
Keyword(s):  
False Discovery Rates ◽  
False Discovery ◽  
Discovery Rates
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