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.

1 2 3 4 5 6 7 8 9 | ```
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 |

`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 |

`col` |
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 `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`

1 2 3 4 5 6 7 8 9 10 | ```
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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