res.volcanoplot: Volcanoplot
In msmsTests: LC-MS/MS Differential Expression Tests

Description Usage Arguments Details Value Author(s) References See Also Examples

Given the data frame obtained from test.results() a volcano plot is drawn.The features are colored according to significance and relevance.

1 2	res.volcanoplot(tres, max.pval=0.05, min.LFC=1, maxx=3, maxy=10, ylbls=20)

`tres`	The dataframe with test results as obtained from `test.results()`. Or a data frame with, at least, the following columns: `LogFC` with log fold changes, `adjp` with multitest adjusted p-values, and `DEP` with TRUE or FALSE as post test filter results, being the TRUE features both statistically significant and relevant for reproducibility.
`max.pval`	The maximum adjusted p-value considered as statistically significant.
`min.LFC`	The minimum absolute log fold change considered as biologically relevant.
`maxx`	The maximum value in abcissas (i.e. log2(fold change)).
`maxy`	The maximum value in ordinates (i.e. -log10(p.val))
`ylbls`	All features with -log10(p.val) above this value will be ploted with feature labels.

Abscissas and ordinates may be limited giving a value other than NULL to the parameters maxx and maxy. All features deemed significant and relevant are ploted by a blue dot, all features deemed significant but not passing the post test filter are plotted by a red dot. The non-significant features are plotted as smaller black dots. All features deemed significant and relevant and with a -log10 p-value above ylbls are plotted with a label showing their row index in the test results dataframe. The borders limiting the values given by max.pval and min.LFC are ploted as dash-and-dot red lines.

No return value.

Josep Gregori i Font

Josep Gregori, Laura Villareal, Alex Sanchez, Jose Baselga, Josep Villanueva (2013). An Effect Size Filter Improves the Reproducibility in Spectral Counting-based Comparative Proteomics. Journal of Proteomics, DOI http://dx.doi.org/10.1016/j.jprot.2013.05.030

test.results, volcanoplot

library(msmsTests)
data(msms.dataset)
# Pre-process expression matrix
e <- pp.msms.data(msms.dataset)
# Models and normalizing condition
null.f <- "y~batch"
alt.f <- "y~treat+batch"
div <- apply(exprs(e),2,sum)
#Test
res <- msms.glm.qlll(e,alt.f,null.f,div=div)
lst <- test.results(res,e,pData(e)$treat,"U600","U200",div,
                    alpha=0.05,minSpC=2,minLFC=log2(1.8),
                    method="BH")
# Plot
res.volcanoplot(lst$tres, max.pval=0.05, min.LFC=1, maxx=3, maxy=NULL,
                        ylbls=4)