In rformassspectrometry/Features: Quantitative features for mass spectrometry data

BiocStyle::markdown()

library("QFeatures")

Introduction

The QFeatures package provides infrastructure (that is classes to store data and the methods to process and manipulate them) to manage and analyse quantitative features from mass spectrometry experiments. It is based on the SummarizedExperiment and MultiAssayExperiment classes. Assays in a QFeatures object have a hierarchical relation: proteins are composed of peptides, themselves produced by spectra, as depicted in figure \@ref(fig:featuresplot). Throughout the aggregation and processing of these data, the relations between assays are tracked and recorded, thus allowing users to easily navigate across spectra, peptide and protein quantitative data.

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In the following sections, we are going to demonstrate how to create a single-assay QFeatures objects starting from a spreadsheet, how to compute the next assays (peptides and proteins), and how these can be manipulated and explored.

library("QFeatures")

Creating QFeatures object

data(hlpsms)

While QFeatures objects can be created manually (see ?QFeatures for details), most users will probably possess quantitative data in a spreadsheet or a dataframe. In such cases, the easiest is to use the readQFeatures function to extract the quantitative data and metadata columns. Below, we load the hlpsms dataframe that contains data for r nrow(hlpsms) PSMs from the TMT-10plex hyperLOPIT spatial proteomics experiment from [@Christoforou:2016]. The quantCols argument specifies that columns 1 to 10 contain quantitation data, and that the assay should be named psms in the returned QFeatures object, to reflect the nature of the data.

data(hlpsms)
hl <- readQFeatures(hlpsms, quantCols = 1:10, name = "psms")
hl

Below, we see that we can extract an assay using its index or its name. The individual assays are stored as SummarizedExperiment object and further access its quantitative data and metadata using the assay and rowData functions

hl[[1]]
hl[["psms"]]
head(assay(hl[["psms"]]))
head(rowData(hl[["psms"]]))

For further details on how to manipulate such objects, refer to the r BiocStyle::Biocpkg("MultiAssayExperiment") [@Ramos:2017] and r BiocStyle::Biocpkg("SummarizedExperiment") [@SE] packages.

As illustrated in figure \@ref(fig:featuresplot), an central characteristic of QFeatures objects is the aggregative relation between their assays. This can be obtained with the aggregateFeatures function that will aggregate quantitative features from one assay into a new one. In the next code chunk, we aggregate PSM-level data into peptide by grouping all PSMs that were matched the same peptide sequence. Below, the aggregation function is set, as an example, to the mean. The new assay is named peptides.

hl <- aggregateFeatures(hl, "psms", "Sequence",
                        name = "peptides", fun = colMeans)
hl
hl[["peptides"]]

Below, we repeat the aggregation operation by grouping peptides into proteins as defined by the ProteinGroupAccessions variable.

hl <- aggregateFeatures(hl, "peptides", "ProteinGroupAccessions",
                        name = "proteins", fun = colMeans)
hl
hl[["proteins"]]

The sample assayed in a QFeatures object can be documented in the colData slot. The hl data doens't currently possess any sample metadata. These can be addedd as a new DataFrame with matching names (i.e. the DataFrame rownames must be identical assay's colnames) or can be added one variable at at time, as shown below.

colData(hl)
hl$tag <- c("126", "127N", "127C", "128N", "128C", "129N", "129C",
            "130N", "130C", "131")
colData(hl)

Manipulating feature metadata

The QFeatures package provides some utility functions that streamline the accession and manipulation of the feature metadata.

The feature metadata, more generally referred to as rowData in the Bioconductor ecosystem, is specific to each assay in a QFeatures object. Therefore there are as many rowData tables as there are assays. rowDataNames provides a list where each element contains the name of the rowData columns available in the corresponding assay.

rowDataNames(hl)

We saw above how to get the rowData from an assay, but we can also extract the rowData for all assays by calling the function on the QFeautures object directly. Similarly to rowDataNames, a list is returned where each element contains the rowData available in the corresponding assay.

rowData(hl)

In some cases, we are interested in extracting the rowData as a single data table. This is easily performed using the rbindRowData function. The function will automatically select the columns that are common to all selected assays.

rbindRowData(hl, i = c("peptides", "proteins"))

We can also replace and add columns in the rowData. This requires to provide a List where the names of the List point to the assay to be updated and the elements of the List contain DataFrames with the replacement values. If the DataFrame contains a column that is not present in the rowData, that column will get added to the rowData. For instance, let's add a rowData variables with the mean protein expression as well as the associated standard deviation. First, we need to create the DataFrame with the mean expression.

dF <- DataFrame(mean = rowSums(assay(hl[["proteins"]])),
                sd = rowSds(assay(hl[["proteins"]])))

Then, we create the list and name the element proteins so that the new data is added to the rowData of the proteins assay. To add the list, we insert it back into the rowData.

rowData(hl) <- List(proteins = dF)

As shown below, the new mean and sd variables have been added to the rowData of the proteins assay.

rowData(hl)[["proteins"]]

Note that you can also replace an existing column in the rowData by naming the column name in the DataFrame after the column to replace.

Subsetting

One particularity of the QFeatures infrastructure is that the features of the constitutive assays are linked through an aggregative relation. This relation is recorded when creating new assays with aggregateFeatures and is exploited when subsetting QFeature by their feature names.

In the example below, we are interested in the Stat3B isoform of the Signal transducer and activator of transcription 3 (STAT3) with accession number P42227-2. This accession number corresponds to a feature name in the proteins assay. But this protein row was computed from 8 peptide rows in the peptides assay, themselves resulting from the aggregation of 8 rows in the psms assay.

stat3 <- hl["P42227-2", , ]
stat3

We can easily visualise this new QFeatures object using ggplot2 once converted into a data.frame. See the visualization vignette for more details about data exploration from a QFeatures object.

stat3_df <- data.frame(longFormat(stat3))
stat3_df$assay <- factor(stat3_df$assay,
                        levels = c("psms", "peptides", "proteins"))
library("ggplot2")
ggplot(data = stat3_df,
       aes(x = colname,
           y = value,
           group = rowname)) +
    geom_line() + geom_point() +
    facet_grid(~ assay)

Below we repeat the same operation for the Signal transducer and activator of transcription 1 (STAT1) and 3 (STAT3) accession numbers, namely P42227-2 and P42225. We obtain a new QFeatures instance containing 2 proteins, 9 peptides and 10 PSMS. From this, we can readily conclude that STAT1 was identified by a single PSM/peptide.

stat <- hl[c("P42227-2", "P42225"), , ]
stat

Below, we visualise the expression profiles for the two proteins.

stat_df <- data.frame(longFormat(stat))
stat_df$stat3 <- ifelse(stat_df$rowname %in% stat3_df$rowname,
                        "STAT3", "STAT1")
stat_df$assay <- factor(stat_df$assay,
                        levels = c("psms", "peptides", "proteins"))

ggplot(data = stat_df,
       aes(x = colname,
           y = value,
           group = rowname)) +
    geom_line() + geom_point() +
    facet_grid(stat3 ~ assay)

The subsetting by feature names is also available as a call to the subsetByFeature function, for use with the pipe operator.

hl |>
    subsetByFeature("P42227-2")

hl |>
    subsetByFeature(c("P42227-2", "P42225"))

and possibly

hl |>
    subsetByFeature("P42227-2") |>
    longFormat() |>
    as.data.frame() |>
    ggplot(aes(x = colname,
               y = value,
               group = rowname)) +
    geom_line() +
    facet_grid(~ assay)

to reproduce the line plot.

Filtering

QFeatures is assays can also be filtered based on variables in their respective row data slots using the filterFeatures function. The filters can be defined using the formula interface or using AnnotationFilter objects from the r BiocStyle::Biocpkg("AnnotationFilter") package [@AnnotationFilter]. In addition to the pre-defined filters (such as SymbolFilter, ProteinIdFilter, ... that filter on gene symbol, protein identifier, ...), this package allows users to define arbitrary character or numeric filters using the VariableFilter.

mito_filter <- VariableFilter(field = "markers",
                              value = "Mitochondrion",
                              condition = "==")
mito_filter

qval_filter <- VariableFilter(field = "qValue",
                              value = 0.001,
                              condition = "<=")
qval_filter

These filter can then readily be applied to all assays' row data slots. The mito_filter will return all PSMs, peptides and proteins that were annotated as localising to the mitochondrion.

filterFeatures(hl, mito_filter)

The qval_filter, on the other hand, will only return a subset of PSMs, because the qValue variable is only present in the psms assays. The q-values are only relevant to PSMs and that variable was dropped from the other assays.

filterFeatures(hl, qval_filter)

The same filters can be created using the forumla interface:

filterFeatures(hl, ~ markers == "Mitochondrion")
filterFeatures(hl, ~ qValue <= 0.001)

Session information {-}

sessionInfo()

References {-}

rformassspectrometry/Features documentation built on Jan. 14, 2025, 2:17 a.m.