variousT: Various Tests for finding differentially expressed proteins...
In prot2D: Statistical Tools for volume data from 2D Gel Electrophoresis

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.

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)

`data`	an `ExpressionSet` of volume data. Usually as returned by `ES.prot`.
`fdr.thr`	threshold value for the FDR (defaults to 0.1).
`method.fdr`	method for FDR estimator. 4 methods are implemented :"BH" for the Classical FDR from Benjamini & Hochberg, "Strimmer", "Storey" and "Pounds" (see references for details).
`Fold2`	logical. If true only spots with an absolute ratio of 2 are selected.
`plot`	logical. If true (default) draws a plot fdr values depending on the p-values with a line indicating the FDR threshold.
`var.equal`	logical. If false (default) assume unequal variances in each condition and uses a proper correction. Only for `ttest.Prot` and `shrinkT.Prot`.
`col`	An integer (default to 1). Only for `modT.Prot`. If there is more than one factor in `pData`, indicates the column to use for the analysis

.

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 studentt.stat).
- 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 qvalue.

returns an ExpressionSet containing only the significant spots (see Examples).

Sebastien Artigaud sebastien.artigaud@gmx.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).

Norm.qt,ES.prot,fdrtool,limma, samr,studentt.stat,shrinkt.stat, efront.stat,qvalue

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 )