Description Usage Arguments Details Value Author(s) References See Also Examples
These functions provides simple methods for finding differentially expressed proteins in 2-DE experiments.
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ttest.Prot(data, plot = T, fdr.thr = 0.1, Fold2 = F, method.fdr = "BH", var.equal = F) modT.Prot(data, plot = T, fdr.thr = 0.1, Fold2 = F, method.fdr = "BH", col=1) samT.Prot(data, plot = T, fdr.thr = 0.1, Fold2 = F, method.fdr = "BH") efronT.Prot(data, plot = T, fdr.thr = 0.1, Fold2 = F, method.fdr = "BH") shrinkT.Prot(data, plot = T, fdr.thr = 0.1, Fold2 = F, method.fdr = "BH", var.equal = F)
threshold value for the FDR (defaults to 0.1).
method for FDR estimator. 4 methods are implemented :"BH" for the Classical FDR from Benjamini & Hochberg, "Strimmer", "Storey" and "Pounds" (see references for details).
logical. If true only spots with an absolute ratio of 2 are selected.
logical. If true (default) draws a plot fdr values depending on the p-values with a line indicating the FDR threshold.
logical. If false (default) assume unequal variances in each condition and uses a proper correction. Only for
An integer (default to 1). Only for
As described in Artigaud et al (2013) these are functions adapted from microarray analysis. 2-DE experiments analysis requires a variant of the t-statistic that is suitable for high-dimensional data and large-scale multiple testing. For this purpose, in the last few years, various test procedures have been suggested.
These functions provides:
- the classical Student's t-test (adapted from
- two tests especially modified for micro-array analysis : Efron's t-test (adapted from
efront.stat, Efron et al, 2001) and the modified t-test used in Significance Analysis for Microarray (adapted from
samr, Tusher et al, 2001)
- two methods that take advantage of hierarchical Bayes methods for estimation of the variance across genes: the moderate t-test from Smyth (using
limma; see Smyth, 2004) and the "Shrinkage t" statistic test from Opgen-Rhein & Strimmer (adapted from
shrinkcat.stat; see Opgen-Rhein & Strimmer, 2007).
As statistical tests allowing the identification of differentially expressed proteins must take into account a correction for multiple tests in order to avoid false conclusions. These functions also provides different methods to estimate the False Discovery Rate :
- the classical FDR estimator of Benjamini & Hochberg (using
p.adjust; see Benjamini & Hochberg, 1995)
- the Fdr estimator of Strimmer (based on local fdr calculation) (using
fdrtool; see Strimmer 2008)
- the "robust FDR" estimator of Pounds & Cheng (implemented in
robust.fdr for the
prot2D package; see Pounds & Cheng, 2006)
- Fdr method of Storey and Tibshirani (2003), also known as "q-values" (using
ExpressionSet containing only the significant spots (see Examples).
Sebastien Artigaud email@example.com
Artigaud, S., Gauthier, O. & Pichereau, V. (2013) "Identifying differentially expressed proteins in two-dimensional electrophoresis experiments: inputs from transcriptomics statistical tools." Bioinformatics, vol.29 (21): 2729-2734.
Benjamini, Y. & Hochberg, Y. (1995) "Controlling the false discovery rate: a practical and powerful approach to multiple testing" Journal of the Royal Statistical Society. Series B. Methodological.: 289-300.
Efron, B., Tibshirani, R., Storey, J.D., & Tusher, V. (2001) "Empirical Bayes Analysis of a Microarray Experiment" Journal of the American Statistical Association, vol. 96 (456): 1151-1160.
Tusher, V.G., Tibshirani, R., & Chu, G. (2001) "Significance analysis of microarrays applied to the ionizing radiation response"" Proceedings of the National Academy of Sciences of the United States of America, vol. 98 (9): 5116-5121.
Smyth, G.K. (2004) "Linear models and empirical bayes methods for assessing differential expression in microarray experiments." Statistical Applications in Genetics and Molecular Biology, vol. 3: Article 3.
Pounds, S. & Cheng, C. (2006) "Robust estimation of the false discovery rate" Bioinformatics, vol. 22 (16): 1979-1987.
Strimmer, K. (2008) "A unified approach to false discovery rate estimation." BMC Bioinformatics, vol. 9: 303.
Opgen-Rhein, R. & Strimmer, K. (2007) "Accurate Ranking of Differentially Expressed Genes by a Distribution-Free Shrinkage Approach" Statistical Applications in Genetics and Molecular Biology, vol. 6 (1).
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data(pecten) data(pecten.fac) pecten.norm <- Norm.qt(pecten, n1=6, n2=6, plot=TRUE) #Quantiles normalization of the data ES.p <- ES.prot(pecten.norm, n1=6, n2=6, f=pecten.fac) ES.diff <- modT.Prot(ES.p, fdr.thr=0.1, plot=TRUE) featureNames(ES.diff) # Names of the spots selected for a moderated t-test with a fdr of 0.1 fData(ES.diff) # Displaying fold change (as log2(ratio)) for selected spots exprs(ES.diff) # Volume normalized data for all the selected spots ## Not run: heatplot(ES.diff) #Great heatmap of the selected spots (require made4 Bioconductor package )
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