expval: Example of twilight.pval result

In twilight: Estimation of local false discovery rate

Description

Application of function twilight.pval on leukemia data set of Golub et al. (1999), as given in data(Golub_Merge) in library(golubEsets).

First step was the variance-stabilizing normalization of Huber et al. (2002) in library(vsn): golubNorm <- justvsn(Golub_Merge).

The function call was then expval <- twilight.pval(golubNorm,id) with id <- as.numeric(Golub_Merge$ALL.AML).

Usage

data(expval)

Format

A twilight object.

References

Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD and Lander ES (1999): Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring, Science 286, 531–537.

Huber W, von Heydebreck A, Sultmann H, Poustka A and Vingron M (2002): Variance stabilization applied to microarray data calibration and to the quantification of differential expression, Bioinformatics 18, suppl. 1, S96–S104.

Scheid S and Spang R (2004): A stochastic downhill search algorithm for estimating the local false discovery rate, IEEE TCBB 1(3), 98–108.

Scheid S and Spang R (2005): twilight; a Bioconductor package for estimating the local false discovery rate, Bioinformatics 21(12), 2921–2922.

Scheid S and Spang R (2006): Permutation filtering: A novel concept for significance analysis of large-scale genomic data, in: Apostolico A, Guerra C, Istrail S, Pevzner P, and Waterman M (Eds.): Research in Computational Molecular Biology: 10th Annual International Conference, Proceedings of RECOMB 2006, Venice, Italy, April 2-5, 2006. Lecture Notes in Computer Science vol. 3909, Springer, Heidelberg, pp. 338-347.

Tusher VG, Tibshirani R and Chu G (2001): Significance analysis of mircroarrays applied to the ionizing response, PNAS 98(9), 5116–5121.

twilight documentation built on Nov. 8, 2020, 5:38 p.m.