PMID- 21595983
OWN - NLM
STAT- MEDLINE
DCOM- 20110928
LR  - 20211020
IS  - 1745-6150 (Electronic)
IS  - 1745-6150 (Linking)
VI  - 6
DP  - 2011 May 20
TI  - ConReg-R: Extrapolative recalibration of the empirical distribution of p-values 
      to improve false discovery rate estimates.
PG  - 27
LID - 10.1186/1745-6150-6-27 [doi]
AB  - BACKGROUND: False discovery rate (FDR) control is commonly accepted as the most 
      appropriate error control in multiple hypothesis testing problems. The accuracy 
      of FDR estimation depends on the accuracy of the estimation of p-values from each 
      test and validity of the underlying assumptions of the distribution. However, in 
      many practical testing problems such as in genomics, the p-values could be 
      under-estimated or over-estimated for many known or unknown reasons. 
      Consequently, FDR estimation would then be influenced and lose its veracity. 
      RESULTS: We propose a new extrapolative method called Constrained Regression 
      Recalibration (ConReg-R) to recalibrate the empirical p-values by modeling their 
      distribution to improve the FDR estimates. Our ConReg-R method is based on the 
      observation that accurately estimated p-values from true null hypotheses follow 
      uniform distribution and the observed distribution of p-values is indeed a 
      mixture of distributions of p-values from true null hypotheses and true 
      alternative hypotheses. Hence, ConReg-R recalibrates the observed p-values so 
      that they exhibit the properties of an ideal empirical p-value distribution. The 
      proportion of true null hypotheses (pi0) and FDR are estimated after the 
      recalibration. CONCLUSIONS: ConReg-R provides an efficient way to improve the FDR 
      estimates. It only requires the p-values from the tests and avoids permutation of 
      the original test data. We demonstrate that the proposed method significantly 
      improves FDR estimation on several gene expression datasets obtained from 
      microarray and RNA-seq experiments.
FAU - Li, Juntao
AU  - Li J
AD  - Computational & Mathematical Biology, Genome Institute of Singapore, 60 Biopolis 
      Street, Singapore 138672, Singapore.
FAU - Paramita, Puteri
AU  - Paramita P
FAU - Choi, Kwok Pui
AU  - Choi KP
FAU - Karuturi, R Krishna Murthy
AU  - Karuturi RK
LA  - eng
PT  - Evaluation Study
PT  - Journal Article
PT  - Meta-Analysis
PT  - Research Support, Non-U.S. Gov't
DEP - 20110520
PL  - England
TA  - Biol Direct
JT  - Biology direct
JID - 101258412
SB  - IM
MH  - Algorithms
MH  - *Data Interpretation, Statistical
MH  - Gene Expression Profiling
MH  - Gene Expression Regulation, Fungal
MH  - Humans
MH  - Models, Statistical
MH  - *Regression Analysis
MH  - Reproducibility of Results
MH  - Saccharomyces cerevisiae/genetics/physiology
MH  - Sample Size
MH  - Sequence Analysis, RNA
PMC - PMC3130718
EDAT- 2011/05/21 06:00
MHDA- 2011/09/29 06:00
PMCR- 2011/05/20
CRDT- 2011/05/21 06:00
PHST- 2010/12/03 00:00 [received]
PHST- 2011/05/20 00:00 [accepted]
PHST- 2011/05/21 06:00 [entrez]
PHST- 2011/05/21 06:00 [pubmed]
PHST- 2011/09/29 06:00 [medline]
PHST- 2011/05/20 00:00 [pmc-release]
AID - 1745-6150-6-27 [pii]
AID - 10.1186/1745-6150-6-27 [doi]
PST - epublish
SO  - Biol Direct. 2011 May 20;6:27. doi: 10.1186/1745-6150-6-27.