Various Tests for finding differentially expressed proteins in 2-DE experiments

Description

These functions provides simple methods for finding differentially expressed proteins in 2-DE experiments.

Usage

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)

Arguments

.

Details

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.

Value

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

Author(s)

Sebastien Artigaud sebastien.artigaud@gmx.com

References

• 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).

See Also

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

Examples

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 )

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(prot2D)
Loading required package: fdrtool
Loading required package: st
Loading required package: sda
Loading required package: entropy
Loading required package: corpcor
Loading required package: samr
Loading required package: impute
Loading required package: matrixStats
matrixStats v0.50.2 (2016-04-24) successfully loaded. See ?matrixStats for help.
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: 'Biobase'

The following objects are masked from 'package:matrixStats':

    anyMissing, rowMedians

Loading required package: limma

Attaching package: 'limma'

The following object is masked from 'package:BiocGenerics':

    plotMA

Loading required package: Mulcom
Loading required package: fields
Loading required package: spam
Loading required package: grid
Spam version 1.3-0 (2015-10-24) is loaded.
Type 'help( Spam)' or 'demo( spam)' for a short introduction 
and overview of this package.
Help for individual functions is also obtained by adding the
suffix '.spam' to the function name, e.g. 'help( chol.spam)'.

Attaching package: 'spam'

The following objects are masked from 'package:base':

    backsolve, forwardsolve

Loading required package: maps

 # maps v3.1: updated 'world': all lakes moved to separate new #
 # 'lakes' database. Type '?world' or 'news(package="maps")'.  #


Loading required package: MASS
Loading required package: qvalue
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/prot2D/variousT.Rd_%03d_medium.png", width=480, height=480)
> ### Name: variousT.Prot
> ### Title: Various Tests for finding differentially expressed proteins in
> ###   2-DE experiments
> ### Aliases: variousT.Prot ttest.Prot modT.Prot samT.Prot efronT.Prot
> ###   shrinkT.Prot
> ### Keywords: prot2D
> 
> ### ** Examples
> 
> 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)
Number of up-regulated spots in Condition 2
[1] 0
Number of down-regulated spots in Condition 2
[1] 1
Warning message:
In fitFDistRobustly(var, df1 = df, covariate = covariate, winsor.tail.p = winsor.tail.p) :
  small x values have been offset away from zero
> featureNames(ES.diff) # Names of the spots selected for a moderated t-test with a fdr of 0.1
[1] "2607"
> fData(ES.diff) # Displaying fold change (as log2(ratio)) for selected spots
         ratio
2607 -1.912657
> exprs(ES.diff) # Volume normalized data for all the selected spots
     Br_23865 Br_23883 Br_23884 Br_23728 Br_23729 Br_23730 Br_23731 Br_23732
2607 23.21036   23.454 20.22889 22.34141 23.07774  23.0274 22.63096 20.07119
     Br_23733 Br_23875 Br_23876 Br_23877
2607 21.07323 20.52232 20.02905 19.53711
> ## Not run: heatplot(ES.diff) #Great heatmap of the selected spots (require made4 Bioconductor package )
> 
> 
> 
> 
> dev.off()
null device 
          1 
>