Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/msmsTest-functions.R
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 |
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
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)
|
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