This vignette describes how to visualize quantitative mass spectrometry data contained in a QFeatures object. This vignette is distributed under a CC BY-SA license.
QFeatures 1.16.0
To demonstrate the data visualization of QFeatures
, we first perform
a quick processing of the hlpsms
example data. We load the data and
read it as a QFeautres
object. See the processing
vignette
for more details about data processing with QFeatures
.
library("QFeatures")
data(hlpsms)
hl <- readQFeatures(hlpsms, quantCols = 1:10, name = "psms")
We then aggregate the psms to peptides, and the peptodes to proteins.
hl <- aggregateFeatures(hl, "psms", "Sequence", name = "peptides", fun = colMeans)
## Your row data contain missing values. Please read the relevant
## section(s) in the aggregateFeatures manual page regarding the effects
## of missing values on data aggregation.
hl <- aggregateFeatures(hl, "peptides", "ProteinGroupAccessions", name = "proteins", fun = colMeans)
We also add the TMT tags that were used to multiplex the samples. The
data is added to the colData
of the QFeatures
object and will
allow us to demonstrate how to plot data from the colData
.
hl$tag <- c("126", "127N", "127C", "128N", "128C", "129N", "129C",
"130N", "130C", "131")
The dataset is now ready for data exploration.
QFeatures
hierarchyQFeatures
objects can contain several assays as the data goes through
the processing workflow. The plot
function provides an overview of
all the assays present in the dataset, showing also the hierarchical
relationships between the assays as determined by the AssayLinks
.
plot(hl)
This plot is rather simple with only three assays, but some processing
workflows may involve more steps. The feat3
example data illustrates
the different possible relationships: one parent to one child, multiple
parents to one child and one parent to multiple children.
data("feat3")
plot(feat3)
Note that some datasets may contain many assays, for instance because
the MS experiment consists of hundreds of batches. This can lead to an
overcrowded plot. Therefore, you can also explore this hierarchy of
assays through an interactive plot, supported by the plotly
package
(Sievert (2020)). You can use the viewer panel to zoom in and out and
navigate across the tree(s).
plot(hl, interactive = TRUE)
The quantitative data is retrieved using assay()
, the feature
metadata is retrieved using rowData()
on the assay of interest, and
the sample metadata is retrieved using colData()
. Once retrieved,
the data can be supplied to the base R data exploration tools. Here
are some examples:
proteins
assay.plot(assay(hl, "proteins")[1, ])