Managing Mass Spectrometry Experiments
29 October 2024
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: 2024-10-29 16:39:43.317556
Compiled: Tue Oct 29 21:45:29 2024
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 32 files
## ## Resource ID BFC20 in cache in /home/biocbuild/.cache/R/rpx.## [1] 'QEP2LC6_HeLa_50ng_251120_01-calib.mzID.gz' ... [32] 'checksum.txt'
## Use 'pxfiles(.)' to see all files.pxfiles(px)## Project PXD022816 files (32):
## [local] QEP2LC6_HeLa_50ng_251120_01-calib.mzID.gz
## [local] QEP2LC6_HeLa_50ng_251120_01-calib.mzML
## [remote] QEP2LC6_HeLa_50ng_251120_01.raw
## [local] QEP2LC6_HeLa_50ng_251120_02-calib.mzID.gz
## [local] QEP2LC6_HeLa_50ng_251120_02-calib.mzML
## [remote] QEP2LC6_HeLa_50ng_251120_02.raw
## [remote] QEP2LC6_HeLa_50ng_251120_03-calib.mzID.gz
## [remote] QEP2LC6_HeLa_50ng_251120_03-calib.mzML
## [remote] QEP2LC6_HeLa_50ng_251120_03.raw
## [remote] QEP2LC6_HeLa_50ng_251120_04-calib.mzID.gz
## ...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 (32):
## [local] QEP2LC6_HeLa_50ng_251120_01-calib.mzID.gz
## [local] QEP2LC6_HeLa_50ng_251120_01-calib.mzML
## [remote] QEP2LC6_HeLa_50ng_251120_01.raw
## [local] QEP2LC6_HeLa_50ng_251120_02-calib.mzID.gz
## [local] QEP2LC6_HeLa_50ng_251120_02-calib.mzML
## [remote] QEP2LC6_HeLa_50ng_251120_02.raw
## [remote] QEP2LC6_HeLa_50ng_251120_03-calib.mzID.gz
## [remote] QEP2LC6_HeLa_50ng_251120_03-calib.mzML
## [remote] QEP2LC6_HeLa_50ng_251120_03.raw
## [remote] QEP2LC6_HeLa_50ng_251120_04-calib.mzID.gz
## ...## [1] "QEP2LC6_HeLa_50ng_251120_01-calib.mzML"
## [2] "QEP2LC6_HeLa_50ng_251120_02-calib.mzML"fls <- pxget(px, i)## Loading QEP2LC6_HeLa_50ng_251120_01-calib.mzML from cache.## Loading QEP2LC6_HeLa_50ng_251120_02-calib.mzML from cache.fls## [1] "/home/biocbuild/.cache/R/rpx/1e13206e720ca2_QEP2LC6_HeLa_50ng_251120_01-calib.mzML"
## [2] "/home/biocbuild/.cache/R/rpx/1e13205a6031dc_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 MsExperiment4.1 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"]] 1e13206e720ca2_QEP2LC6_HeLa_50ng_251120_01-calib.mzML ...
## [["fasta"]] homo_sapiens.fastaLet’s add these files to the main experiment management object:
experimentFiles(msexp) <- msfls
msexp## Object of class MsExperiment
## Files: mzmls, fasta4.2 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 1e13206e72... 1
## 2 1e13205a60... 24.3 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):
## 1e13206e720ca2_QEP2LC6_HeLa_50ng_251120_01-calib.mzML
## 1e13205a6031dc_QEP2LC6_HeLa_50ng_251120_02-calib.mzMLWe 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/LumosWe 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/1e13206e720ca2_QEP2LC6_HeLa_50ng_251120_01-calib.mzML -o 1e13206e720ca2_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/1e13205a6031dc_QEP2LC6_HeLa_50ng_251120_02-calib.mzML -o 1e13205a6031dc_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] "/home/biocbuild/bbs-3.20-bioc/tmpdir/RtmpQ8zFXD/1e13206e720ca2_QEP2LC6_HeLa_50ng_251120_01-calib.mzid"
## [2] "/home/biocbuild/bbs-3.20-bioc/tmpdir/RtmpQ8zFXD/1e13205a6031dc_QEP2LC6_HeLa_50ng_251120_02-calib.mzid"cmd <- paste("touch", output)
cmd## [1] "touch /home/biocbuild/bbs-3.20-bioc/tmpdir/RtmpQ8zFXD/1e13206e720ca2_QEP2LC6_HeLa_50ng_251120_01-calib.mzid"
## [2] "touch /home/biocbuild/bbs-3.20-bioc/tmpdir/RtmpQ8zFXD/1e13205a6031dc_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 /home/biocbuild/bbs-3.20-bioc/tmpdir/RtmpQ8zFXD/1e13206e720ca2_QEP2LC6_HeLa_50ng_251120_01-calib.mzid
## 0
## touch /home/biocbuild/bbs-3.20-bioc/tmpdir/RtmpQ8zFXD/1e13205a6031dc_QEP2LC6_HeLa_50ng_251120_02-calib.mzid
## 0Below, we add the names of the newly created files to our experiment:
experimentFiles(msexp)[["mzids"]] <- mzids
experimentFiles(msexp)## MsExperimentFiles of length 3
## [["mzmls"]] 1e13206e720ca2_QEP2LC6_HeLa_50ng_251120_01-calib.mzML ...
## [["fasta"]] homo_sapiens.fasta
## [["mzids"]] 1e13206e720ca2_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 /home/biocbuild/bbs-3.20-bioc/tmpdir/RtmpQ8zFXD/1e13206e720ca2_QEP2LC6_HeLa_50ng_251120_01-calib.mzid"
## [2] "touch /home/biocbuild/bbs-3.20-bioc/tmpdir/RtmpQ8zFXD/1e13205a6031dc_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.
See also section Using MsExperiment with MsBackendSql below for an
alternative to load/restore defined MS experiments from an SQL database.
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"]] 1e13206e720ca2_QEP2LC6_HeLa_50ng_251120_01-calib.mzML ...
## [["fasta"]] homo_sapiens.fasta
## [["mzids"]] 1e13206e720ca2_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):
## 1e13206e720ca2_QEP2LC6_HeLa_50ng_251120_01-calib.mzML
## 1e13205a6031dc_QEP2LC6_HeLa_50ng_251120_02-calib.mzMLplotSpectra(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) subset 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) <- sdWe 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)).
We next establish a second link between each sample and the annotation
file "internal_standards.txt" in experimentFiles(lmse)$standards:
lmse <- linkSampleData(lmse, with = "experimentFiles.annotations",
sampleIndex = c(1, 2), withIndex = c(1, 1))The figure below illustrates again what happened internally by this call: a new
link matrix was added establishing the relationship between the two samples
and the one value in experimentFiles(lmse)$annotations.
It is thus also possible to link different samples to the same element. We next
link the spectra in the object to the individual samples. We use for that an
alternative way to specify the link without the need to provide sampleIndex
and withIndex. Sample-to-data links can also be specified using a syntax
similar to an SQL join:
"sampleData.<column in sampleData> = <slot>.<element in slot>". Links will be
thus established between elements with matching values in the specified data
fields (i.e. between rows in sampleData for which values in the specified
column matches values in <slot>.<element>). In order to use this alternative
approach to link spectra to the respective samples we have to first add the
(full) raw file name as an additional column to the object’s sampleData. We
can now add links between spectra and samples by matching this raw file name to
the original file name from which the spectra were imported (which is available
in the "dataOrigin" spectra variable).
sampleData(lmse)$raw_file <- normalizePath(fls)
lmse <- linkSampleData(
lmse, with = "sampleData.raw_file = spectra.dataOrigin")The link was thus established between matching values in
sampleData(lmse)$raw_file and spectra(lmse)$dataOrigin.
sampleData(lmse)$raw_file## [1] "/home/biocbuild/bbs-3.20-bioc/R/site-library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"
## [2] "/home/biocbuild/bbs-3.20-bioc/R/site-library/msdata/sciex/20171016_POOL_POS_3_105-134.mzML"spectra(lmse)$dataOrigin |>
head()## [1] "/home/biocbuild/bbs-3.20-bioc/R/site-library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"
## [2] "/home/biocbuild/bbs-3.20-bioc/R/site-library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"
## [3] "/home/biocbuild/bbs-3.20-bioc/R/site-library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"
## [4] "/home/biocbuild/bbs-3.20-bioc/R/site-library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"
## [5] "/home/biocbuild/bbs-3.20-bioc/R/site-library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"
## [6] "/home/biocbuild/bbs-3.20-bioc/R/site-library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"The figure below illustrates this link. With that last call we have thus
established links between samples and 3 different data elements in
the MsExperiment.
lmse## Object of class MsExperiment
## Files: mzML_files, annotations
## Spectra: MS1 (1862)
## Experiment data: 2 sample(s)
## Sample data links:
## - experimentFiles.mzML_file: 2 sample(s) to 2 element(s).
## - experimentFiles.annotations: 2 sample(s) to 1 element(s).
## - spectra: 2 sample(s) to 1862 element(s).A convenience function to quickly extract the index of a sample a spectrum is
associated with is spectraSampleIndex(). This function returns an integer
vector of length equal to the number of spectra in the object with the row in
the object’s sampleData a spectrum is linked to, or NA_integer_ if a
spectrum is not linked to any sample.
#' Show the sample assignment for the first few spectra
spectraSampleIndex(lmse) |>
head()## [1] 1 1 1 1 1 1If we had also quantified feature values, we could also link them to the
samples. Below we create a simple, small SummarizedExperiment to represent
such quantified feature values and add that to our experiment. To show that
MsExperiment supports also links between subsets of data elements, we create a
SummarizedExperiment that contains values for an additional sample which is
not present in our sampleData. Also, we add samples in an arbitrary order.
library(SummarizedExperiment)
sd <- DataFrame(sample = c("QC2", "QC1", "QC3"), idx = c(3, 1, 5))
se <- SummarizedExperiment(colData = sd, assay = cbind(1:10, 11:20, 21:30))
qdata(lmse) <- seNext we link the samples in this SummarizedExperiment to the samples to in the
MsExperiment using matching values between the "sample_id" column in the
object’s sampleData data frame and the column "sample" in the
SummarizedExperiment’s colData which is stored in the @qdata slot. The
naming convention to define such columns is <slot name>.<column name>.
sampleData(lmse)$sample_id## [1] "QC1" "QC2"qdata(lmse)$sample## [1] "QC2" "QC1" "QC3"lmse <- linkSampleData(lmse, with = "sampleData.sample_id = qdata.sample")
lmse## Object of class MsExperiment
## Files: mzML_files, annotations
## Spectra: MS1 (1862)
## SummarizedExperiment: 10 feature(s)
## Experiment data: 2 sample(s)
## Sample data links:
## - experimentFiles.mzML_file: 2 sample(s) to 2 element(s).
## - experimentFiles.annotations: 2 sample(s) to 1 element(s).
## - spectra: 2 sample(s) to 1862 element(s).
## - qdata: 2 sample(s) to 2 column(s).The main advantage of all these links is that any subsetting of the experiment by sample will keep the (linked) data consistent. To illustrate this we subset below the experiment to the second sample.
b <- lmse[2]
b## Object of class MsExperiment
## Files: mzML_files, annotations, mzML_file
## Spectra: MS1 (931)
## SummarizedExperiment: 10 feature(s)
## Experiment data: 1 sample(s)
## Sample data links:
## - experimentFiles.mzML_file: 1 sample(s) to 1 element(s).
## - experimentFiles.annotations: 1 sample(s) to 1 element(s).
## - spectra: 1 sample(s) to 931 element(s).
## - qdata: 1 sample(s) to 1 column(s).The subset object contains now all data elements that are linked to this
second sample. Accessing the assay of the SummarizedExperiment in
qdata will thus return only the quantified feature abundances for this
second sample.
assay(qdata(b))## [,1]
## [1,] 1
## [2,] 2
## [3,] 3
## [4,] 4
## [5,] 5
## [6,] 6
## [7,] 7
## [8,] 8
## [9,] 9
## [10,] 10But what happens for data elements that are not linked to any sample? Below we
add a data.frame as a metadata to the experiment and subset the object
again.
metadata(lmse)$other <- data.frame(
sample_name = c("study_1", "POOL", "study_2"),
index = 1:3)
b <- lmse[2]
metadata(b)## $other
## sample_name index
## 1 study_1 1
## 2 POOL 2
## 3 study_2 3By default, any element which is not linked to a sample is retained in the filtered/subset object.
We next link each sample to the second row in this data frame and subset the data again to the second sample.
lmse <- linkSampleData(lmse, with = "metadata.other",
sampleIndex = 1:2, withIndex = c(2, 2))
b <- lmse[2]
metadata(b)## $other
## sample_name index
## 2 POOL 2Subsetting thus retained only the row in the data frame for the linked sample. Obviously it is also possible to subset to multiple samples, in arbitrary order. Below we re-order our experiment.
lmse <- lmse[c(2, 1)]
sampleData(lmse)## DataFrame with 2 rows and 4 columns
## sample_id sample_name injection_idx raw_file
## <character> <character> <numeric> <character>
## 1 QC2 QC Pool 3 /home/bioc...
## 2 QC1 QC Pool 1 /home/bioc...The sample order is thus reversed and also all other linked elements are
re-ordered accordingly, such as "mzML_file" in the object’s experimentFiles.
experimentFiles(lmse)$mzML_file## [1] "/home/biocbuild/bbs-3.20-bioc/R/site-library/msdata/sciex/20171016_POOL_POS_3_105-134.mzML"
## [2] "/home/biocbuild/bbs-3.20-bioc/R/site-library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML"It is however important to note, that subsetting will also duplicate elements that are associated with multiple samples:
experimentFiles(lmse)$annotations## [1] "internal_standards.txt" "internal_standards.txt"Thus, while we added a single annotation file to the data element
"annotations" in experimentFiles, after subsetting we ended up with two
identical files. This duplication of n:m relationships between
samples to elements does however not affect data consistency. A sample will
always be linked to the correct value/element.
7 Subset and filter MsExperiment
As already shown above, MsExperiment objects can be subset with [ which will
subset the data by sample. Depending on whether relationships (links) between
samples and any other data within the object are present also these are
correctly subset. In addition to this general subset operation, it is possible
to individually filter the spectra data within an MsExperiment using the
filterSpectra function. This function takes any filter function supported by
Spectra with parameter filter. Parameters for this filter function can be
passed through .... As an example we filter below the spectra data of our
MsExperiment keeping only spectra with an retention time between 200 and 210
seconds.
#' Filter the Spectra using the `filterRt` function providing also the
#' parameters for this function.
res <- filterSpectra(lmse, filterRt, rt = c(200, 210))
res## Object of class MsExperiment
## Files: mzML_files, annotations, mzML_file
## Spectra: MS1 (72)
## SummarizedExperiment: 10 feature(s)
## Experiment data: 2 sample(s)
## Sample data links:
## - experimentFiles.mzML_file: 2 sample(s) to 2 element(s).
## - experimentFiles.annotations: 2 sample(s) to 2 element(s).
## - spectra: 2 sample(s) to 72 element(s).
## - qdata: 2 sample(s) to 2 column(s).
## - metadata.other: 2 sample(s) to 2 element(s).The resulting MsExperiment contains now much fewer spectra. filterSpectra
did only filter the spectra data, but not any of the other data slots. It did
however update and consolidate the relationships between samples and spectra
(if present) after filtering:
#' Extract spectra of the second sample after filtering
spectra(res[2L])## MSn data (Spectra) with 36 spectra in a MsBackendMzR backend:
## msLevel rtime scanIndex
## <integer> <numeric> <integer>
## 1 1 200.049 717
## 2 1 200.328 718
## 3 1 200.607 719
## 4 1 200.886 720
## 5 1 201.165 721
## ... ... ... ...
## 32 1 208.698 748
## 33 1 208.977 749
## 34 1 209.256 750
## 35 1 209.535 751
## 36 1 209.814 752
## ... 33 more variables/columns.
##
## file(s):
## 20171016_POOL_POS_1_105-134.mzML
## Processing:
## Filter: select retention time [200..210] on MS level(s) 1 [Tue Oct 29 21:45:55 2024]8 Using MsExperiment with MsBackendSql
The MsBackendSql provides functionality to store mass
spectrometry data in a SQL database and to represent such data as a Spectra
object (through its MsBackendSql or MsBackendOfflineSql backends). Next to
the MS data, it is also possible to store sample data in such SQL databases
hence allowing to create self-contained databases for MS experiments. This
simplifies the process to load pre-defined MS experiment or to share experiment
data across analyses or with a collaborator. In this section we show how MS data
and sample information from an experiment can be stored into a SQL database and
how that data can then be loaded again as a MsExperiment.
Below we first define the raw data files of the experiment. In our example we use two small test files provided with the msdata package.
fls <- c(system.file("microtofq", "MM14.mzML", package = "msdata"),
system.file("microtofq", "MM8.mzML", package = "msdata"))We can then use the createMsBackendSqlDatabase function from the
MsBackendSql package to write the data into a SQLite
database. SQLite databases are particularly easy to create, use and also to
share, but for large experiments it is suggested to use more powerful database
engines, such as for example MariaDB (see also the documentation and vignette
of the MsBackendSql package for parameters and settings for such
large databases).
Also, in our example we store the SQLite database into a data temporary file, but for a real use case we would want to use a real file.
library(MsBackendSql)
library(RSQLite)
#' Create the SQLite database where to store the data. For our
#' example we create the database in a temporary file.
sql_file <- tempfile()
con <- dbConnect(SQLite(), dbname = sql_file)
#' Write the MS data to the.
createMsBackendSqlDatabase(dbcon = con, fls)## Importing data ...##
[==========================================================] 1/1 (100%) in 2s
## Creating indices .... Done## [1] TRUEWe can now create a Spectra object representing the MS data in that database
using either a MsBackendSql or MsBackendOfflineSql backend.
#' Load the database as a Spectra object
sps <- Spectra(sql_file, source = MsBackendOfflineSql(), drv = SQLite())
sps## MSn data (Spectra) with 310 spectra in a MsBackendOfflineSql backend:
## msLevel precursorMz polarity
## <integer> <numeric> <integer>
## 1 1 NA 1
## 2 1 NA 1
## 3 1 NA 1
## 4 1 NA 1
## 5 1 NA 1
## ... ... ... ...
## 306 1 NA 1
## 307 1 NA 1
## 308 1 NA 1
## 309 1 NA 1
## 310 1 NA 1
## ... 34 more variables/columns.
## Use 'spectraVariables' to list all of them.
## Database: /home/biocbuild/bbs-3.20-bioc/tmpdir/RtmpQ8zFXD/file6b8882d2a38bdNext we define sample annotations for the two files and create a MsExperiment
with the MS data and the related sample annotations.
#' Define sample annotations. Ideally all experiment-relavant information
#' should be specified.
sdata <- data.frame(file_name = basename(fls),
sample_name = c("MM14", "MM8"),
sample_group = c("ctrl", "ctrl"))
#' To simplify subsequent linking between samples and data files we
#' define a new spectra variable with the file names of the raw data files.
sps$file_name <- basename(dataOrigin(sps))
#' Create the MsExperiment for that data
mse <- MsExperiment(sampleData = sdata, spectra = sps)Finally we link the MS spectra to the individual samples using the file names of the original data files.
#' Establish the mapping between spectra and samples
mse <- linkSampleData(mse, with = "sampleData.file_name = spectra.file_name")
mse## Object of class MsExperiment
## Spectra: MS1 (310)
## Experiment data: 2 sample(s)
## Sample data links:
## - spectra: 2 sample(s) to 310 element(s).With sample annotations and the mapping between samples and spectra defined, we
can next write this information to the SQL database containing already the MS
data of our experiment: the dbWriteSampleData function writes, for
MsExperiment objects that use a Spectra with a MsBackendSql (or
MsBackendOfflineSql) backend the available sample data to additional database
tables in the database.
#' Write sample data (and mapping between samples and spectra) to the
#' database
dbWriteSampleData(mse)The SQL database does now contain the MS data, the sample annotations and the mapping between samples and spectra. Creation of such a database needs to be performed only once for an experiment. The associated SQLite database file could now be shared with a collaborator or used across different analyses without the need to share or copy also the raw data files or sample annotations (which would usually needed to be provided as a separate file).
To load the MS experiment data again into R, we first need to create a Spectra
object with a MsBackendSql or MsBackendOfflineSql backend for that SQL
database.
#' Create a Spectra object for the MS data of that database
sps <- Spectra(sql_file, source = MsBackendOfflineSql(), drv = SQLite())We then pass this Spectra object with parameter spectra to the
MsExperiment constructor call. The function will then read also sample data
from the database (if available) as well as the mapping between samples and
spectra to restore our MS experiment.
#' Create the MsExperiment reading MS data and sample data from
#' the database
mse <- MsExperiment(spectra = sps)
mse## Object of class MsExperiment
## Spectra: MS1 (310)
## Experiment data: 2 sample(s)
## Sample data links:
## - spectra: 2 sample(s) to 310 element(s).We have thus now the full MS experiment data available.
For smaller experiments, such SQL databases could also be created with an
alternative, eventually simpler, approach. For that we first use the
readMsExperiment function to create an MsExperiment from the file names of
the raw MS data files and a data.frame with the associated sample
annotations. We re-use here the variables fls with the data file names and
sdata with the sample data.
#' Create an MsExperiment with data from two (raw) data files
#' and associated sample annotations.
mse <- readMsExperiment(spectraFiles = fls, sampleData = sdata)
mse## Object of class MsExperiment
## Spectra: MS1 (310)
## Experiment data: 2 sample(s)
## Sample data links:
## - spectra: 2 sample(s) to 310 element(s).The MS data in that MsExperiment is represented by a Spectra object using
the MsBackendMzR:
#' Show the Spectra from the experiment
spectra(mse)## MSn data (Spectra) with 310 spectra in a MsBackendMzR backend:
## msLevel rtime scanIndex
## <integer> <numeric> <integer>
## 1 1 270.334 1
## 2 1 270.671 2
## 3 1 271.007 3
## 4 1 271.343 4
## 5 1 271.680 5
## ... ... ... ...
## 306 1 65.4360 194
## 307 1 65.7720 195
## 308 1 66.1092 196
## 309 1 66.4458 197
## 310 1 66.7818 198
## ... 33 more variables/columns.
##
## file(s):
## MM14.mzML
## MM8.mzMLWe next change the backend of this Spectra object using the setBackend
function to a MsBackendOfflineSql. We choose again a SQLite database format
and store the data to a temporary file.
#' Define the SQLite database file
sql_file2 <- tempfile()
#' Change the backend of the Spectra data
spectra(mse) <- setBackend(spectra(mse), MsBackendOfflineSql(),
dbname = sql_file2, drv = SQLite())## Importing data ...##
[==========================================================] 2/2 (100%) in 0s
## Creating indices .... Donespectra(mse)## MSn data (Spectra) with 310 spectra in a MsBackendOfflineSql backend:
## msLevel precursorMz polarity
## <integer> <numeric> <integer>
## 1 1 NA 1
## 2 1 NA 1
## 3 1 NA 1
## 4 1 NA 1
## 5 1 NA 1
## ... ... ... ...
## 306 1 NA 1
## 307 1 NA 1
## 308 1 NA 1
## 309 1 NA 1
## 310 1 NA 1
## ... 34 more variables/columns.
## Use 'spectraVariables' to list all of them.
## Database: /home/biocbuild/bbs-3.20-bioc/tmpdir/RtmpQ8zFXD/file6b8887d6167e0
## Processing:
## Switch backend from MsBackendMzR to MsBackendOfflineSql [Tue Oct 29 21:45:59 2024]All MS data is now stored in the SQLite database and the Spectra object of our
experiment uses the MsBackendOfflineSql to represent/interface this data. To
store also the sample data to the database we can use the dbWriteSampleData as
before.
#' Store also sample data to the database
dbWriteSampleData(mse)In addition to create self-contained SQL databases for an experiment, this code could also be used within an analysis workflow to serialize/de-serialize results, always with the advantage of keeping the full experiment data in a single place (file) in a language-agnostic format.
We can for example use basic functions from the DBI/RSQLite packages (or simple SQL commands) to access the data in the database. Below we connect to the database and list the available tables.
#' Connect to the database
con <- dbConnect(SQLite(), dbname = sql_file2)
#' List the available database tables
dbListTables(con)## [1] "msms_spectrum" "msms_spectrum_peak_blob"
## [3] "sample_data" "sample_to_msms_spectrum"The msms_spectrum table contains general metadata for the individual MS spectra, msms_spectrum_peak_blob the actual peak data (m/z and intensity values), sample_data the sample annotations and sample_to_msms_spectrum the link between samples and spectra. Below we retrieve the content of the sample_data table.
#' Get the content of the sample_data table
dbGetQuery(con, "select * from sample_data")## file_name sample_name sample_group
## 1 MM14.mzML MM14 ctrl
## 2 MM8.mzML MM8 ctrl
## spectraOrigin
## 1 /home/biocbuild/bbs-3.20-bioc/R/site-library/msdata/microtofq/MM14.mzML
## 2 /home/biocbuild/bbs-3.20-bioc/R/site-library/msdata/microtofq/MM8.mzML
## sample_id_
## 1 1
## 2 2#' Close the connection to the database
dbDisconnect(con)9 Session information
sessionInfo()## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] RSQLite_2.3.7 MsBackendSql_1.6.0
## [3] SummarizedExperiment_1.36.0 Biobase_2.66.0
## [5] GenomicRanges_1.58.0 GenomeInfoDb_1.42.0
## [7] IRanges_2.40.0 MatrixGenerics_1.18.0
## [9] matrixStats_1.4.1 rpx_2.14.0
## [11] Spectra_1.16.0 BiocParallel_1.40.0
## [13] S4Vectors_0.44.0 BiocGenerics_0.52.0
## [15] MsExperiment_1.8.0 ProtGenerics_1.38.0
## [17] BiocStyle_2.34.0
##
## loaded via a namespace (and not attached):
## [1] DBI_1.2.3 bitops_1.0-9
## [3] rlang_1.1.4 magrittr_2.0.3
## [5] clue_0.3-65 compiler_4.4.1
## [7] vctrs_0.6.5 reshape2_1.4.4
## [9] stringr_1.5.1 pkgconfig_2.0.3
## [11] MetaboCoreUtils_1.14.0 crayon_1.5.3
## [13] fastmap_1.2.0 dbplyr_2.5.0
## [15] magick_2.8.5 XVector_0.46.0
## [17] utf8_1.2.4 rmarkdown_2.28
## [19] UCSC.utils_1.2.0 tinytex_0.53
## [21] purrr_1.0.2 bit_4.5.0
## [23] xfun_0.48 MultiAssayExperiment_1.32.0
## [25] zlibbioc_1.52.0 cachem_1.1.0
## [27] jsonlite_1.8.9 progress_1.2.3
## [29] blob_1.2.4 highr_0.11
## [31] DelayedArray_0.32.0 prettyunits_1.2.0
## [33] parallel_4.4.1 cluster_2.1.6
## [35] R6_2.5.1 bslib_0.8.0
## [37] stringi_1.8.4 jquerylib_0.1.4
## [39] Rcpp_1.0.13 bookdown_0.41
## [41] knitr_1.48 Matrix_1.7-1
## [43] igraph_2.1.1 tidyselect_1.2.1
## [45] abind_1.4-8 yaml_2.3.10
## [47] codetools_0.2-20 curl_5.2.3
## [49] lattice_0.22-6 tibble_3.2.1
## [51] plyr_1.8.9 withr_3.0.2
## [53] evaluate_1.0.1 BiocFileCache_2.14.0
## [55] xml2_1.3.6 pillar_1.9.0
## [57] BiocManager_1.30.25 filelock_1.0.3
## [59] ncdf4_1.23 generics_0.1.3
## [61] RCurl_1.98-1.16 hms_1.1.3
## [63] glue_1.8.0 lazyeval_0.2.2
## [65] tools_4.4.1 data.table_1.16.2
## [67] QFeatures_1.16.0 mzR_2.40.0
## [69] fs_1.6.4 grid_4.4.1
## [71] tidyr_1.3.1 MsCoreUtils_1.18.0
## [73] GenomeInfoDbData_1.2.13 cli_3.6.3
## [75] fansi_1.0.6 S4Arrays_1.6.0
## [77] dplyr_1.1.4 AnnotationFilter_1.30.0
## [79] sass_0.4.9 digest_0.6.37
## [81] SparseArray_1.6.0 memoise_2.0.1
## [83] htmltools_0.5.8.1 lifecycle_1.0.4
## [85] httr_1.4.7 bit64_4.5.2
## [87] MASS_7.3-61