1 Preparing the data

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.

2 Exploring the QFeatures hierarchy

QFeatures 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)

3 Basic data exploration

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:

  • Plot the intensities for the first protein. These data are available from the proteins assay.
plot(assay(hl, "proteins")[1, ])

  • Get the distribution of the number of peptides that were aggregated per protein. These data are available in the column .n from the protein rowData.
hist(rowData(hl)[["proteins"]]$.n)

  • Get the count table of the different tags used for labeling the samples. These data are available in the column tag from the colData.
table(hl$tag)
## 
##  126 127C 127N 128C 128N 129C 129N 130C 130N  131 
##    1    1    1    1    1    1    1    1    1    1

4 Using ggplot2

ggplot2 is a powerful tool for data visualization in R and is part of the tidyverse package ecosystem (Wickham et al. (2019)). It produces elegant and publication-ready plots in a few lines of code. ggplot2 can be used to explore QFeatures object, similarly to the base functions shown above. Note that ggplot2 expects data.frame or tibble objects whereas the quantitative data in QFeatures are encoded as matrix (or matrix-like objects, see ?SummarizedExperiment) and the rowData and colData are encoded as DataFrame. This is easily circumvented by converting those objects to data.frames or tibbles. See here how we reproduce the plot above using ggplot2.

library("ggplot2")
df <- data.frame(rowData(hl)[["proteins"]])
ggplot(df) +
    aes(x = .n) +
    geom_histogram()