Publication 2004 Issue No. 3 - July-September Abstract - A Stochastic Downhill Search Algorithm for Estimating the Local False Discovery Rate
A Stochastic Downhill Search Algorithm for Estimating the Local False Discovery Rate
July-September 2004 (vol. 1 no. 3)
pp. 98-108
 ASCII Text x Stefanie Scheid, Rainer Spang, "A Stochastic Downhill Search Algorithm for Estimating the Local False Discovery Rate," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 1, no. 3, pp. 98-108, July-September, 2004.
 BibTex x @article{ 10.1109/TCBB.2004.24,author = {Stefanie Scheid and Rainer Spang},title = {A Stochastic Downhill Search Algorithm for Estimating the Local False Discovery Rate},journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics},volume = {1},number = {3},issn = {1545-5963},year = {2004},pages = {98-108},doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2004.24},publisher = {IEEE Computer Society},address = {Los Alamitos, CA, USA},}
 RefWorks Procite/RefMan/Endnote x TY - JOURJO - IEEE/ACM Transactions on Computational Biology and BioinformaticsTI - A Stochastic Downhill Search Algorithm for Estimating the Local False Discovery RateIS - 3SN - 1545-5963SP98EP108EPD - 98-108A1 - Stefanie Scheid, A1 - Rainer Spang, PY - 2004KW - Local false discovery ratesKW - stochastic search algorithmsKW - microarray analysisKW - biology and genetics.VL - 1JA - IEEE/ACM Transactions on Computational Biology and BioinformaticsER -
Screening for differential gene expression in microarray studies leads to difficult large-scale multiple testing problems. The local false discovery rate is a statistical concept for quantifying uncertainty in multiple testing. In this paper, we introduce a novel estimator for the local false discovery rate that is based on an algorithm which splits all genes into two groups, representing induced and noninduced genes, respectively. Starting from the full set of genes, we successively exclude genes until the gene-wise p{\hbox{-}}{\rm values} of the remaining genes look like a typical sample from a uniform distribution. In comparison to other methods, our algorithm performs compatibly in detecting the shape of the local false discovery rate and has a smaller bias with respect to estimating the overall percentage of noninduced genes. Our algorithm is implemented in the Bioconductor compatible R package TWILIGHT version 1.0.1, which is available from http://compdiag.molgen.mpg.de/software or from the Bioconductor project at http://www.bioconductor.org.

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Index Terms:
Local false discovery rates, stochastic search algorithms, microarray analysis, biology and genetics.
Citation:
Stefanie Scheid, Rainer Spang, "A Stochastic Downhill Search Algorithm for Estimating the Local False Discovery Rate," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 1, no. 3, pp. 98-108, July-Sept. 2004, doi:10.1109/TCBB.2004.24