In sgibb/topdownr: Investigation of Fragmentation Conditions in Top-Down Proteomics

library("topdownr")
library("topdownrdata")
library("ranger")
library("ggplot2")
library("BiocStyle")

Foreword {-}

r BiocStyle::Biocpkg("topdownr") is free and open-source software. If you use it, please support the project by citing it in publications:

ct <- format(citation("topdownr"), "textVersion")
cat(gsub("DOI: *(.*)$", "DOI: [\\1](https://doi.org/\\1)", ct), "\n")

Questions and bugs {-}

For bugs, typos, suggestions or other questions, please file an issue in our tracking system (https://github.com/sgibb/topdownr/issues) providing as much information as possible, a reproducible example and the output of sessionInfo().

If you don't have a GitHub account or wish to reach a broader audience for general questions about proteomics analysis using R, you may want to use the Bioconductor support site: https://support.bioconductor.org/.

Introduction/Working with topdownr

Load the package.

library("topdownr")

Importing Files

Some example files are provided in the topdownrdata package. For a full analysis you need a .fasta file with the protein sequence, the .experiments.csv files containing the method information, the .txt files containing the scan header information and the .mzML files with the deconvoluted spectra.

list.files(topdownrdata::topDownDataPath("myoglobin"))
lapply(
    topdownr:::.listTopDownFiles(
        topdownrdata::topDownDataPath("myoglobin")),
    function(x) {
        c(head(
            file.path(
                "...",
                paste(
                    tail(strsplit(dirname(x), "/")[[1L]], 2),
                    collapse=.Platform$file.sep
                ),
                basename(x)
            ),
            2
        ), "...")
    }
)

All these files have to be in a directory. You could import them via readTopDownFiles. This function has some arguments. The most important ones are the path of the directory containing the files, the protein modification (e.g. initiator methionine removal, "Met-loss"), and adducts (e.g. proton transfer often occurs from c to z-fragment after ETD reaction).

## the mass adduct for a proton
H <- 1.0078250321

myoglobin <- readTopDownFiles(
    ## directory path
    path = topdownrdata::topDownDataPath("myoglobin"),
    ## fragmentation types
    type = c("a", "b", "c", "x", "y", "z"),
    ## adducts (add -H/H to c/z and name
    ## them cmH/zpH (c minus H, z plus H)
    adducts = data.frame(
        mass=c(-H, H),
        to=c("c", "z"),
        name=c("cmH", "zpH")),
    ## initiator methionine removal
    modifications = "Met-loss",
    ## don't use neutral loss
    neutralLoss = NULL,
    ## tolerance for fragment matching
    tolerance = 5e-6,
    ## topdownrdata was generate with an older version of topdownr,
    ## the method files were generated with FilterString identification,
    ## use `conditions = "ScanDescription"` (default) for recent data.
    conditions = "FilterString"
)

myoglobin

The TopDownSet Anatomy

The assembled object is an TopDownSet object. Briefly it is composed of three interconnected tables:

1. rowViews/fragment data: holds the information on the type of fragments, their modifications and adducts.
2. colData/condition data: contains the corresponding fragmentation condition for every spectrum.
3. assayData: contains the intensity of assigned fragments.

Technical Details

This section explains the implementation details of the TopDownSet class. It is not necessary to understand everything written here to use topdownr for the analysis of fragmentation data.

The TopDownSet contains the following components: Fragment data, Condition data, Assay data.

Fragment data

rowViews(myoglobin)

The fragmentation data are represented by an FragmentViews object that is an overloaded XStringViews object. It contains one AAString (the protein sequence) and an IRanges object that stores the start, end (and width) values of the fragments. Additionally it has a DataFrame for the mass, type and z information of each fragment.

Condition data

conditionData(myoglobin)[, 1:5]

Condition data is a DataFrame that contains the combined header information for each MS run (combined from method (.experiments.csv files)/scan header (.txt files) table and metadata from the .mzML files).

Assay data

assayData(myoglobin)[206:215, 1:10]

Assay data is a sparseMatrix from the Matrix package (in detail a dgCMatrix) where the rows correspond to the fragments, the columns to the runs/conditions and the entries to the intensity values. A sparseMatrix is similar to the classic matrix in R but stores just the values that are different from zero.

Subsetting a TopDownSet

A TopDownSet could be subsetted by the fragment and the condition data.

# select the first 100 fragments
myoglobin[1:100]

# select all "c" fragments
myoglobin["c"]

# select just the 100. "c" fragment
myoglobin["c100"]

# select all "a" and "b" fragments but just the first 100 "c"
myoglobin[c("a", "b", paste0("c", 1:100))]

# select condition/run 1 to 10
myoglobin[, 1:10]

# select all conditions from one file
myoglobin[, myoglobin$File == "myo_1211_ETDReagentTarget_1e+06_1"]

# select all "c" fragments from a single file
myoglobin["c", myoglobin$File == "myo_1211_ETDReagentTarget_1e+06_1"]

Plotting a TopDownSet

Each condition represents one spectrum. We could plot a single condition interactively or all spectra into a pdf file (or any other R device that supports multiple pages/plots).

# plot a single condition
plot(myoglobin[, "C0707.30_1.0e+05_1.0e+06_10.00_00_28_3"])
# example to plot the first ten conditions into a pdf
# (not evaluated in the vignette)
pdf("topdown-conditions.pdf", paper="a4r", width=12)
plot(myoglobin[, 1:10])
dev.off()

plot returns a list (an item per condition) of ggplot objects which could further modified or investigated interactively by calling plotly::ggplotly().

Fragmentation Data Analysis of Myoglobin

We follow the following workflow:

We use the example data loaded in Importing Files.

The data contains several replicates for each fragmentation condition. Before aggregation can be performed we need to remove scans with inadequate injection times and fragments with low intensity or poor intensity reproducibility.

Filter Conditions on Injection Times

Injection times should be consistent for a particular m/z and particular AGC target. High or low injection times indicate problems with on-the-flight AGC calculation or spray instability for a particular scan. Hence the topdownr automatically calculates median injection time for a given m/z and AGC target combination. The user can choose to remove all scans that deviate more than a certain amount from the corresponding median and/or choose to keep N scans with the lowest deviation from the median for every condition.

Here we show an example of such filtering and the effect on the distribution of injection times.

injTimeBefore <- colData(myoglobin)
injTimeBefore$Status <- "before filtering"

## filtering on max deviation and just keep the
## 2 technical replicates per condition with the
## lowest deviation
myoglobin <- filterInjectionTime(
    myoglobin,
    maxDeviation = log2(3),
    keepTopN = 2
)

myoglobin

injTimeAfter <- colData(myoglobin)
injTimeAfter$Status <- "after filtering"

injTime <- as.data.frame(rbind(injTimeBefore, injTimeAfter))

## use ggplot for visualisation
library("ggplot2")

ggplot(injTime,
    aes(x = as.factor(AgcTarget),
        y = IonInjectionTimeMs,
        group = AgcTarget)) +
    geom_boxplot() +
    facet_grid(Status ~ Mz)

Filter Fragments on CV

High CV of intensity for a fragment suggests either fragment contamination by another m/z species or problems with deisotoping and we recommend removing all fragments with CV > 30, as shown below.

myoglobin <- filterCv(myoglobin, threshold=30)
myoglobin

Filter Fragments on Intensity

When optimizing protein fragmentation we also want to focus on the most intense fragments, hence we recommend removing all low intensity fragments from analysis.

Low intensity is defined relatively to the most intense observation for this fragment (i.e. relatively to the maximum value in an assayData row). In the example below all intensity values, which have less than 10% intensity of the highest intensity to their corresponding fragment (in their corresponding row) are removed.

myoglobin <- filterIntensity(myoglobin, threshold=0.1)
myoglobin

Data Aggregation

The next step of analysis is aggregating technical replicates of fragmentation conditions (columns of assayData).

myoglobin <- aggregate(myoglobin)
myoglobin

Random Forest

To examine which of the features (fragmentation parameters) have the highest overall impact for a protein we perform random forest machine learning using the ranger [@ranger] R-package.

Before we compute some fragmentation statistics (number of assigned fragments, total assigned intensity, etc.).

library("ranger")

## statistics
head(summary(myoglobin))

## number of fragments
nFragments <- summary(myoglobin)$Fragments

## features of interest
foi <- c(
    "AgcTarget",
    "EtdReagentTarget",
    "EtdActivation",
    "CidActivation",
    "HcdActivation",
    "Charge"
)

rfTable <- as.data.frame(colData(myoglobin)[foi])

## set NA to zero
rfTable[is.na(rfTable)] <- 0

rfTable <- as.data.frame(cbind(
    scale(rfTable),
    Fragments = nFragments
))

featureImportance <- ranger(
    Fragments ~ .,
    data = rfTable,
    importance = "impurity"
)$variable.importance

barplot(
    featureImportance/sum(featureImportance),
    cex.names = 0.7
)

The two parameters having the lowest overall impact in the myoglobin dataset across all conditions are ETD reagent target (EtdReagentTarget), CID activation energy (CidActivation) and AGC target (AgcTarget), while ETD reaction energy (EtdActivation) and HCD activation energy (HcdActivation) demonstrate the highest overall impact.

Combining Fragmentation Conditions to Maximize Coverage

The purpose of topdownr is to investigate how maximum coverage with high intensity fragments can be achieved with minimal instrument time. Therefore topdownr reports the best combination of fragmentation conditions (with user specified number of conditions) that covers the highest number of different bonds.

Different fragmentation methods predominantly generate different types of fragments (e.g. b and y for HCD and CID, c and z for ETD, a and x for UVPD).

However N-terminal (a, b and c) as well as C-terminal (x, y and z) fragments originating from the same bond, cover the same number of amino acid sidechains. Hence different types of N-terminal (a, b and c) or C-terminal (x, y and z) fragments from the same bond add no extra sequence information.

Before we compute combinations all the fragments are converted to either N- or C-terminal, as shown in the image below.

In topdownr we convert the TopDownSet into an NCBSet object (N-terminal/C-terminal/Bidirectional).

myoglobinNcb <- as(myoglobin, "NCBSet")
myoglobinNcb

An NCBSet is very similar to a TopDownSet but instead of an FragmentViews the rowViews are an XStringViews for the former. Another difference is that the NCBSet has one row per bond instead one row per fragment. Also the assayData contains no intensity information but a 1 for an N-terminal, a 2 for a C-terminal and a 3 for bidirectional fragments.

The NCBSet can be used to select the combination of conditions that provide the best fragment coverage. While computing coverage topdownr awards 1 point for every fragment going from every bond in either N or C directions. This means that bonds covered in both directions increase the score of a condition by 2 points. For the myoglobin fragmentation example we get the following table for the best three conditions:

bestConditions(myoglobinNcb, n=3)

Building a Fragmentation Map

Fragmentation maps allow visualising the type of fragments produced by fragmentation conditions and their overall distribution along the protein backbone. It also illustrates how the combination of conditions results in a cumulative increase in fragment coverage. Shown below is a fragmentation map for myoglobin m/z 707.3, AGC target 1e6 and ETD reagent target of 1e7 for ETD (plotting more conditions is not practical for the vignette):

sel <-
    myoglobinNcb$Mz == 707.3 &
    myoglobinNcb$AgcTarget == 1e6 &
    (myoglobinNcb$EtdReagentTarget == 1e7 &
     !is.na(myoglobinNcb$EtdReagentTarget))

myoglobinNcbSub <- myoglobinNcb[, sel]

fragmentationMap(
    myoglobinNcbSub,
    nCombinations = 10,
    labels = seq_len(ncol(myoglobinNcbSub))
)

Session Information

sessionInfo()

References

sgibb/topdownr documentation built on Jan. 16, 2024, 12:14 a.m.