Package: MsExperiment
Authors: Laurent Gatto [aut, cre] (https://orcid.org/0000-0002-1520-2268), Johannes Rainer [aut] (https://orcid.org/0000-0002-6977-7147), Sebastian Gibb [aut] (https://orcid.org/0000-0001-7406-4443)
Last modified: 2022-11-01 15:22:21
Compiled: Tue Nov 1 18:07:54 2022

1 Introduction

The goal of the MsExperiment package is to provide a container for all data related to a mass spectrometry (MS) experiment. Also other Bioconductor packages allow to represent MS experiment data (such as the MSnbase package). The MsExperiment however aims at being very light-weight and flexible to accommodate all possible types of MS experiments (proteomics, metabolomics, …) and all types of MS data representations (chromatographic and spectral data, quantified features etc). In addition, it allows to bundle additional files and data, such as annotations, within the object.

In this vignette, we will describe how to create a MsExperiment object and populate it with various types of data.

library("MsExperiment")

We will also use the Spectra package to import MS data and thus load it here too.

library("Spectra")

2 Installation

The package can be installed with the BiocManager package. To install BiocManager use install.packages("BiocManager") and, after that, BiocManager::install("MsExperiment") to install MsExperiment which will install the package including all required dependencies.

3 Getting data

We will use a small subset of the PXD022816 project (Morgenstern et al. (2020)). The acquisitions correspond to a Pierce Thermo HeLa digestion standard, diluted to 50ng/uL with 97:3 + 0.1% formic acid, and acquired on a QExactive instrument.

Below, we use the rpx package to access the project from the PRIDE repository, and download files of interest. Note that these will automatically be cached in the rpx packages’ cache directory.

library("rpx")
px <- PXDataset("PXD022816")
## Loading PXD022816 from cache.
px
## Project PXD022816 with 31 files
## 
## Resource ID BFC7 in cache in /home/biocbuild/.cache/R/rpx.
##  [1] 'checksum.txt' ... [31] 'QEP2LC6_HeLa_50ng_251120_10-calib.mzML'
##  Use 'pxfiles(.)' to see all files.
pxfiles(px)
## Project PXD022816 files (31):
##  [remote] checksum.txt
##  [remote] QEP2LC6_HeLa_50ng_251120_01.raw
##  [remote] QEP2LC6_HeLa_50ng_251120_02.raw
##  [remote] QEP2LC6_HeLa_50ng_251120_03.raw
##  [remote] QEP2LC6_HeLa_50ng_251120_04.raw
##  [remote] QEP2LC6_HeLa_50ng_251120_05.raw
##  [remote] QEP2LC6_HeLa_50ng_251120_06.raw
##  [remote] QEP2LC6_HeLa_50ng_251120_07.raw
##  [remote] QEP2LC6_HeLa_50ng_251120_08.raw
##  [remote] QEP2LC6_HeLa_50ng_251120_09.raw
##  ...

The project provides the vendor raw files, the converted mzML files as well as the identification mzid files. Let’s download fractions 1 and 2 of the mzML files.

If you run these commands interactively and it’s the first time you use pxget(), you will be asked to create the rpx cache directory - you can safelfy answer yes. The files will then be downloaded. Next time you want to get the same files, they will be loaded automatically from cache.

(i <- grep(".+0[12].+mzML$", pxfiles(px), value = TRUE))
## Project PXD022816 files (31):
##  [remote] checksum.txt
##  [remote] QEP2LC6_HeLa_50ng_251120_01.raw
##  [remote] QEP2LC6_HeLa_50ng_251120_02.raw
##  [remote] QEP2LC6_HeLa_50ng_251120_03.raw
##  [remote] QEP2LC6_HeLa_50ng_251120_04.raw
##  [remote] QEP2LC6_HeLa_50ng_251120_05.raw
##  [remote] QEP2LC6_HeLa_50ng_251120_06.raw
##  [remote] QEP2LC6_HeLa_50ng_251120_07.raw
##  [remote] QEP2LC6_HeLa_50ng_251120_08.raw
##  [remote] QEP2LC6_HeLa_50ng_251120_09.raw
##  ...
## [1] "QEP2LC6_HeLa_50ng_251120_01-calib.mzML"
## [2] "QEP2LC6_HeLa_50ng_251120_02-calib.mzML"
fls <- pxget(px, i)
## Downloading QEP2LC6_HeLa_50ng_251120_01-calib.mzML file.
## Downloading QEP2LC6_HeLa_50ng_251120_02-calib.mzML file.
fls
## [1] "/home/biocbuild/.cache/R/rpx/20f707e9b1799_QEP2LC6_HeLa_50ng_251120_01-calib.mzML" 
## [2] "/home/biocbuild/.cache/R/rpx/20f7072b83b354_QEP2LC6_HeLa_50ng_251120_02-calib.mzML"

4 Mass spectrometry experiment

Let’s start by creating an empty MsExperiment object that we will populate with different pieces of data as we proceed with the analysis of our data.

msexp <- MsExperiment()
msexp
## Empty object of class MsExperiment 
##  Files:

4.1 Experiment files

Let’s now start with our MS experiment management by saving the relevant files in a dedicated MsExperimentFiles object. In addition to the mzML files, let’s also assume we have the human proteomics fasta file ready. Later, when loading the raw data into R, we will refer directly to the files in this MsExperimentFiles object.

msfls <- MsExperimentFiles(mzmls = fls,
                           fasta = "homo_sapiens.fasta")
msfls
## MsExperimentFiles of length  2 
## [["mzmls"]] 20f707e9b1799_QEP2LC6_HeLa_50ng_251120_01-calib.mzML ...
## [["fasta"]] homo_sapiens.fasta

Let’s add these files to the main experiment management object:

experimentFiles(msexp) <- msfls
msexp
## Object of class MsExperiment 
##  Files: mzmls, fasta

4.2 Experimental design

The sampleData slot is used to describe the overall experimental design of the experiment. It can be used to specify the samples of the experiment and to relate them to the files that are part of the experiment. There can be a one-to-one link between a sample and a file, such as for example in label-free approaches, or one-to-many, in labelled multiplexed approaches.

Here, we create a simple data frame with sample annotations that include the original file names and the respective fractions.

sampleData(msexp) <- DataFrame(
    mzmls = basename(experimentFiles(msexp)[["mzmls"]]),
    fractions = 1:2)
sampleData(msexp)
## DataFrame with 2 rows and 2 columns
##           mzmls fractions
##     <character> <integer>
## 1 20f707e9b1...         1
## 2 20f7072b83...         2

4.3 Raw data

We can now create a Spectra object containing the raw data stored in the mzML files. If you are not familiar with the Spectra object, please refer to the package vignettes.

sp <- Spectra(experimentFiles(msexp)[["mzmls"]])
sp
## MSn data (Spectra) with 58907 spectra in a MsBackendMzR backend:
##         msLevel     rtime scanIndex
##       <integer> <numeric> <integer>
## 1             1  0.177987         1
## 2             1  0.599870         2
## 3             1  0.978849         3
## 4             1  1.363217         4
## 5             1  1.742965         5
## ...         ...       ...       ...
## 58903         2   4198.59     29328
## 58904         1   4198.74     29329
## 58905         1   4199.11     29330
## 58906         1   4199.49     29331
## 58907         1   4199.87     29332
##  ... 33 more variables/columns.
## 
## file(s):
## 20f707e9b1799_QEP2LC6_HeLa_50ng_251120_01-calib.mzML
## 20f7072b83b354_QEP2LC6_HeLa_50ng_251120_02-calib.mzML

We can now add this object to the main experiment management object:

spectra(msexp) <- sp
msexp
## Object of class MsExperiment 
##  Files: mzmls, fasta 
##  Spectra: MS1 (12983) MS2 (45924) 
##  Experiment data: 2 sample(s)

4.4 Third party applications

Let’s now assume we want to search the spectra in our mzML files against the homo_sapiens.fasta file. To do so, we would like to use a search engine such as MSGF+, that is run using the command line and generates mzid files.

The command to run MSGF+ would look like this (see the manual page for details):

java -jar /path/to/MSGFPlus.jar \
     -s input.mzML \
     -o output.mzid
     -d proteins.fasta \
     -t 20ppm \ ## precursor mass tolerance
     -tda 1 \   ## search decoy database
     -m 0 \     ## fragmentation method as written in the spectrum or CID if no info
     -int 1     ## Orbitrap/FTICR/Lumos

We can easily build such a command for each of our input file:

mzids <- sub("mzML", "mzid", basename(experimentFiles(msexp)[["mzmls"]]))
paste0("java -jar /path/to/MSGFPlus.jar",
       " -s ", experimentFiles(msexp)[["mzmls"]],
       " -o ", mzids,
       " -d ", experimentFiles(msexp)[["fasta"]],
       " -t 20ppm",
       " -m 0",
       " int 1")
## [1] "java -jar /path/to/MSGFPlus.jar -s /home/biocbuild/.cache/R/rpx/20f707e9b1799_QEP2LC6_HeLa_50ng_251120_01-calib.mzML -o 20f707e9b1799_QEP2LC6_HeLa_50ng_251120_01-calib.mzid -d homo_sapiens.fasta -t 20ppm -m 0 int 1"  
## [2] "java -jar /path/to/MSGFPlus.jar -s /home/biocbuild/.cache/R/rpx/20f7072b83b354_QEP2LC6_HeLa_50ng_251120_02-calib.mzML -o 20f7072b83b354_QEP2LC6_HeLa_50ng_251120_02-calib.mzid -d homo_sapiens.fasta -t 20ppm -m 0 int 1"

Here, for the sake of time and portability, we will not actually run MSGF+, but a simple shell script that will generate mzid files in a temporary R directory.

(output <- file.path(tempdir(), mzids))
## [1] "/tmp/Rtmplj4NZQ/20f707e9b1799_QEP2LC6_HeLa_50ng_251120_01-calib.mzid" 
## [2] "/tmp/Rtmplj4NZQ/20f7072b83b354_QEP2LC6_HeLa_50ng_251120_02-calib.mzid"
cmd <- paste("touch", output)
cmd
## [1] "touch /tmp/Rtmplj4NZQ/20f707e9b1799_QEP2LC6_HeLa_50ng_251120_01-calib.mzid" 
## [2] "touch /tmp/Rtmplj4NZQ/20f7072b83b354_QEP2LC6_HeLa_50ng_251120_02-calib.mzid"

The cmd variable holds the two commands to be run on the command line that will generate the new files. We can run each of these commands with the system() function.

sapply(cmd, system)
##  touch /tmp/Rtmplj4NZQ/20f707e9b1799_QEP2LC6_HeLa_50ng_251120_01-calib.mzid 
##                                                                           0 
## touch /tmp/Rtmplj4NZQ/20f7072b83b354_QEP2LC6_HeLa_50ng_251120_02-calib.mzid 
##                                                                           0

Below, we add the names of the newly created files to our experiment:

experimentFiles(msexp)[["mzids"]] <- mzids
experimentFiles(msexp)
## MsExperimentFiles of length  3 
## [["mzmls"]] 20f707e9b1799_QEP2LC6_HeLa_50ng_251120_01-calib.mzML ...
## [["fasta"]] homo_sapiens.fasta
## [["mzids"]] 20f707e9b1799_QEP2LC6_HeLa_50ng_251120_01-calib.mzid ...
msexp
## Object of class MsExperiment 
##  Files: mzmls, fasta, mzids 
##  Spectra: MS1 (12983) MS2 (45924) 
##  Experiment data: 2 sample(s)

We can also decide to store the commands that were used to generate the mzid files in the experiment’s metadata slot. Here, we use the convention to name that metadata item "mzmls_to_mzids" to document to input and output of these commands.

metadata(msexp)[["mzmls_to_mzids"]] <- cmd
metadata(msexp)
## $mzmls_to_mzids
## [1] "touch /tmp/Rtmplj4NZQ/20f707e9b1799_QEP2LC6_HeLa_50ng_251120_01-calib.mzid" 
## [2] "touch /tmp/Rtmplj4NZQ/20f7072b83b354_QEP2LC6_HeLa_50ng_251120_02-calib.mzid"

Finally, the existMsExperimentFiles() can be used at any time to check which of files that are associated with an experiment actually exist:

existMsExperimentFiles(msexp)
## mzmls: 2 out of 2 exist(s)
## fasta: 0 out of 1 exist(s)
## mzids: 0 out of 2 exist(s)

5 Saving and reusing experiments

The MsExperiment object has been used to store files and data pertaining to a mass spectrometry experiment. It is now possible to save that object and reload it later to recover all data and metadata.

saveRDS(msexp, "msexp.rds")
rm(list = ls())
msexp <- readRDS("msexp.rds")
msexp
## Object of class MsExperiment 
##  Files: mzmls, fasta, mzids 
##  Spectra: MS1 (12983) MS2 (45924) 
##  Experiment data: 2 sample(s)
experimentFiles(msexp)
## MsExperimentFiles of length  3 
## [["mzmls"]] 20f707e9b1799_QEP2LC6_HeLa_50ng_251120_01-calib.mzML ...
## [["fasta"]] homo_sapiens.fasta
## [["mzids"]] 20f707e9b1799_QEP2LC6_HeLa_50ng_251120_01-calib.mzid ...

We can access the raw data as long as the mzML files that were used to generate it still exist in their original location, which is the case here as they were saved in the rpx cache directory.

sp <- spectra(msexp)
sp
## MSn data (Spectra) with 58907 spectra in a MsBackendMzR backend:
##         msLevel     rtime scanIndex
##       <integer> <numeric> <integer>
## 1             1  0.177987         1
## 2             1  0.599870         2
## 3             1  0.978849         3
## 4             1  1.363217         4
## 5             1  1.742965         5
## ...         ...       ...       ...
## 58903         2   4198.59     29328
## 58904         1   4198.74     29329
## 58905         1   4199.11     29330
## 58906         1   4199.49     29331
## 58907         1   4199.87     29332
##  ... 33 more variables/columns.
## 
## file(s):
## 20f707e9b1799_QEP2LC6_HeLa_50ng_251120_01-calib.mzML
## 20f7072b83b354_QEP2LC6_HeLa_50ng_251120_02-calib.mzML
plotSpectra(sp[1000])

6 Linking experimental data to samples

For some experiments and data analyses an explicit link between data, data files and respective samples is required. Such links enable an easy (and error-free) subsetting or re-ordering of a whole experiment by sample and would also simplify coloring and labeling of the data depending on the sample or of its variables or conditions.

Below we generate an MsExperiment object for a simple experiment consisting of a single sample measured in two different injections to the same LC-MS setup.

lmse <- MsExperiment()
sd <- DataFrame(sample_id = c("QC1", "QC2"),
                sample_name = c("QC Pool", "QC Pool"),
                injection_idx = c(1, 3))
sampleData(lmse) <- sd

We next add mzML files to the experiment for the sample that was measured. These are available within the msdata R package. We add also an additional annotation file "internal_standards.txt" to the experiment, which could be e.g. a file with m/z and retention times of internal standards added to the sample (note that such files don’t necessarily have to exist).

fls <- dir(system.file("sciex", package = "msdata"), full.names = TRUE)
basename(fls)
## [1] "20171016_POOL_POS_1_105-134.mzML" "20171016_POOL_POS_3_105-134.mzML"
experimentFiles(lmse) <- MsExperimentFiles(
    mzML_files = fls,
    annotations = "internal_standards.txt")

Next we load the MS data from the mzML files as a Spectra object and add them to the experiment (see the vignette of the Spectra for details on import and representation of MS data).

sps <- Spectra(fls, backend = MsBackendMzR())
spectra(lmse) <- sps
lmse
## Object of class MsExperiment 
##  Files: mzML_files, annotations 
##  Spectra: MS1 (1862) 
##  Experiment data: 2 sample(s)

At this stage we have thus sample annotations and MS data in our object, but no explicit relationships between them. Without such linking between files and samples any subsetting would only subset the sampleData but not any of the potentially associated files. We next use the linkSpectraData function to establish and define such relationships. First we link the experimental files to the samples: we want to link the first mzML file in the element called "mzML_file" in the object’s experimentFiles to the first row in sampleData and the second file to the second row.

lmse <- linkSampleData(lmse, with = "experimentFiles.mzML_file",
                        sampleIndex = c(1, 2), withIndex = c(1, 2))

To define the link we have thus to specify with which element within our MsExperiment we want to link samples. This can be done with the parameter with that takes a single character representing the name (address) of the data element. The name is a combination of the name of the slot within the MsExperiment and the name of the element (or column) within that slot separated by a ".". Using with = "experimentFiles.mzML_file" means we want to link samples to values within the "mzML_file" element of the object’s experimentFiles slot - in other words, we want to link samples to values in experimentFiles(lmse)$mzML_file. The indices of the rows (samples) in sampleData and the indices of the values in with to which we want to link the samples can be defined with sampleIndex and withIndex. In the example above we used sampleIndex = c(1, 2) and withIndex = c(1, 2), thus, we want to link the first row in sampleData to the first value in with and the second row to the second value. See also the section Linking sample data to other experimental data in the documentation of MsExperiment for more information and details.

What happened internally by the call above is illustrated in the figure below. The link is represented as a two-column integer matrix with the indices of the linked sample in the first and the indices of the associated elements in the second columns (this matrix is essentially a cbind(sampleIndex, withIndex)).