Volcanoplot

Description

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

Usage

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

Arguments

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.

Details

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.

Value

No return value.

Author(s)

Josep Gregori i Font

References

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

See Also

test.results, volcanoplot

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
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)