A simple procedure for estimating the false discovery rate

Dalmasso, Cyril; Broët, Philippe; Moreau, Thierry

doi:10.1093/bioinformatics/bti063

Cited by 94 publications

(92 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, At4g28520 showed a confidence (Grant et al, 2005) of 0.985 (i.e., a false discovery rate of 0.015) and a q value (Storey and Tibshirani, 2003) of 0.14. This difference is not surprising because procedures based on the marginal distribution of the P values without any assumption on gene expression changes (Storey and Tibshirani, 2003) are necessarily conservatively biased (Dalmasso et al, 2005). In particular, Reymond et al (2004) have noted that high false discovery rates are estimated by the method of Storey and Tibshirani (2003) when the number of truly affected genes is relatively small, as is the case here.…”

Section: Significance Of the Differences In Gene Expressionmentioning

confidence: 71%

Phytochrome Control of theArabidopsisTranscriptome Anticipates Seedling Exposure to Light

et al. 2005

View full text Add to dashboard Cite

Phytochromes mediate a profound developmental shift when dark-grown seedlings are exposed to light. Here, we show that a subset of genes is upregulated in phytochrome B (phyB) mutants even before dark-grown Arabidopsis thaliana seedlings are exposed to light. Most of these genes bear the RY cis motif, which is a binding site of the transcription factor ABSCISIC ACID INSENSITIVE3 (ABI3), and the phyB mutation also enhances ABI3 expression. These changes in transcriptome have physiological consequences, because seedlings of the abi3 mutant showed enhanced responses to pulses of far-red light, whereas ABI3 overexpressers exhibited the opposite pattern. Seedlings of the wild type derived from seeds germinated in full darkness showed enhanced expression of genes bearing the RY cis motif and reduced responses to farred light. We propose that, via changes in ABI3 expression, light, perceived mainly by phyB in the seed, generates a downstream transdevelopmental phase signal that preconditions the seedling to its most likely environment.

show abstract

Section: Significance Of the Differences In Gene Expressionmentioning

confidence: 71%

Phytochrome Control of theArabidopsisTranscriptome Anticipates Seedling Exposure to Light

et al. 2005

View full text Add to dashboard Cite

show abstract

“…Sev- eral estimation methods are available for experimental data for the proportion of true null hypotheses in a list of p values (e.g. 15,26,27). For the 500 spots of the Table I model, the proportion estimated by the Storey and Tibshirani (15) method is 0.79, somewhat greater than the actual 350/500 ϭ 0.7.…”

Section: Discussionmentioning

confidence: 99%

Multiple Hypothesis Testing in Proteomics: A Strategy for Experimental Work

Diz

Carvajal-Rodríguez

Skibinski

2011

Molecular & Cellular Proteomics

139

View full text Add to dashboard Cite

In quantitative proteomics work, the differences in expression of many separate proteins are routinely examined to test for significant differences between treatments. This leads to the multiple hypothesis testing problem: when many separate tests are performed many will be significant by chance and be false positive results. Statistical methods such as the false discovery rate method that deal with this problem have been disseminated for more than one decade. However a survey of proteomics journals shows that such tests are not widely implemented in one commonly used technique, quantitative proteomics using two-dimensional electrophoresis. We outline a selection of multiple hypothesis testing methods, including some that are well known and some lesser known, and present a simple With the advent of high throughput genomics approaches, researchers need appropriate bioinformatic and statistical tools to deal with the large amounts of data generated. In quantitative proteomics work, differences in expression of many individual proteins between treatments or samples might need to be tested. Researchers must then address what has come to be known as the multiple hypothesis testing problem. Suppose 500 features such as protein spots in a two-dimensional electrophoresis (2-DE) 1 experiment, or mass spectrum features relating to protein or peptide abundance, are each compared between treatments using a t test. If the conventional a priori significance level of ␣ ϭ 0.05 is used, then 5% or about 25 significant features are expected to occur just by chance even if the null hypothesis of no treatment effect is true for all 500 features. Thus it is easier to make a false positive error when picking out significant results in an experiment with multiple features, than when considering one feature in isolation.A variety of statistical methods have been devised to deal with the multiple hypothesis testing problem. These are applicable in quantitative proteomics. In this paper we use examples from 2-DE proteomics to demonstrate these methods. In this technique, the intensity of signal from protein spots on 2-DE gels is measured and compared between gels. Use of the word "spot" is obviously not synonymous with use of the word "protein" in that it does not encompass all forms of a given protein such as alternatively spliced variants and posttranslational modification variants that might form spots in different positions on the gel. The multiple testing approach is introduced with the following example. Table I shows simulated data for a model of a 2-DE proteomics experiment in which 500 spots have been compared between two treatments using the t test. The third column gives p values significant at ␣ ϭ 0.05 sorted from low to high. A threshold line is shown drawn under spot 70. This has been selected arbitrarily for illustration of some properties of a threshold. The p values for the spots above the threshold are all less than ␣ ϭ 0.05 but we cannot declare them to be significant at the ␣ ϭ 0.05 level because of the multiple hypot...

show abstract

“…We combined the interspecific with the intraspecific alignments using custom Perl scripts and performed McDonaldKreitman tests using the MK.pl script obtained from http:// www.dpgp.org/aholloway/Software.html ). We calculated false discovery rates (FDR) using the LBE package (Dalmasso et al 2005) and performed an analysis of Gene Ontology enrichment with GOrilla (http://cbl-gorilla.cs.technion. ac.il/) (Eden et al 2009).…”

Section: Divergence Estimates and Codon Usage Analysismentioning

confidence: 99%

Genome-wide patterns of natural variation reveal strong selective sweeps and ongoing genomic conflict in Drosophila mauritiana

Nolte¹,

Pandey²,

Kofler³

et al. 2012

Genome Res.

117

View full text Add to dashboard Cite

Although it is well understood that selection shapes the polymorphism pattern in Drosophila, signatures of classic selective sweeps are scarce. Here, we focus on Drosophila mauritiana, an island endemic, which is closely related to Drosophila melanogaster. Based on a new, annotated genome sequence, we characterized the genome-wide polymorphism by sequencing pooled individuals (Pool-seq). We show that the interplay between selection and recombination results in a genome-wide polymorphism pattern characteristic for D. mauritiana. Two large genomic regions (>500 kb) showed the signature of almost complete selective sweeps. We propose that the absence of population structure and limited geographic distribution could explain why such pronounced sweep patterns are restricted to D. mauritiana. Further evidence for strong adaptive evolution was detected for several nucleoporin genes, some of which were not previously identified as genes involved in genomic conflict. Since this adaptive evolution is continuing after the split of D. mauritiana and Drosophila simulans, we conclude that genomic conflict is not restricted to short episodes, but rather an ongoing process in Drosophila.

show abstract

A simple procedure for estimating the false discovery rate

Abstract: broet@vjf.inserm.fr.

Cited by 94 publications

References 14 publications

Phytochrome Control of theArabidopsisTranscriptome Anticipates Seedling Exposure to Light

Phytochrome Control of theArabidopsisTranscriptome Anticipates Seedling Exposure to Light

Multiple Hypothesis Testing in Proteomics: A Strategy for Experimental Work

Genome-wide patterns of natural variation reveal strong selective sweeps and ongoing genomic conflict in Drosophila mauritiana

Contact Info

Product

Resources

About