---
title: "On Using and Extending the `MsBackendRawFileReader` Backend"
author:
- name: Tobias Kockmann
affiliation:
- &id Functional Genomics Center Zurich - Swiss Federal Institute of Technology in Zurich
email: Tobias.Kockmann@fgcz.ethz.ch
- name: Christian Panse
affiliation:
- *id
- Swiss Institute of Bioinformatics
email: cp@fgcz.ethz.ch
package: MsBackendRawFileReader
output:
BiocStyle::html_document:
toc_float: true
abstract: |
`r BiocStyle::Biocpkg("MsBackendRawFileReader")` implements an
MsBackend for the `r BiocStyle::Biocpkg("Spectra")` package using
Thermo Fisher Scientific's NewRawFileReader .Net libraries.
The package is generalizing the functionallity provided by the
`r BiocStyle::Biocpkg("rawrr")`. The vignette utilizes data provided through
the `r BiocStyle::Biocpkg("tartare")` package.
vignette: |
%\VignetteIndexEntry{On Using and Extending the `MsBackendRawFileReader` Backend.}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
bibliography:
- MsBackendRawFileReader.bib
---
```{r style, echo = FALSE, results = 'asis'}
BiocStyle::markdown()
knitr::opts_chunk$set(fig.wide = TRUE, fig.retina = 3)
```
```{r arch, echo=FALSE, out.width="80%", eval=TRUE, fig.cap="Integration of rawDiag and rawrr into the Spectra ecosystem (by courtesy of Johannes Rainer)."}
knitr::include_graphics("arch.jpg")
```
# Requirements
```{r require}
suppressMessages(
stopifnot(require(Spectra),
require(MsBackendRawFileReader),
require(tartare),
require(BiocParallel))
)
```
assemblies aka Common Intermediate Language bytecode
The download and install can be done on all platforms using the command:
`rawrr::installRawFileReaderDLLs()`
```{r installAssemblies, echo=TRUE}
if (isFALSE(rawrr::.checkDllInMonoPath())){
rawrr::installRawFileReaderDLLs()
}
if (isFALSE(file.exists(rawrr:::.rawrrAssembly()))){
rawrr::installRawrrExe(sourceUrl = "http://fgcz-ms.uzh.ch/~cpanse/rawrr/rawrr.1.1.12.exe")
}
```
# Load data
```{r tartareEH4547, warning=FALSE, message=FALSE, eval=TRUE}
# fetch via ExperimentHub
library(ExperimentHub)
eh <- ExperimentHub::ExperimentHub()
```
```{r tartare}
query(eh, c('tartare'))
```
The RawFileReader libraries require a file extension ending with `.raw`.
```{r EH3220, message=FALSE, warning=FALSE}
EH3220 <- normalizePath(eh[["EH3220"]])
(rawfileEH3220 <- paste0(EH3220, ".raw"))
if (!file.exists(rawfileEH3220)){
file.link(EH3220, rawfileEH3220)
}
EH3222 <- normalizePath(eh[["EH3222"]])
(rawfileEH3222 <- paste0(EH3222, ".raw"))
if (!file.exists(rawfileEH3222)){
file.link(EH3222, rawfileEH3222)
}
EH4547 <- normalizePath(eh[["EH4547"]])
(rawfileEH4547 <- paste0(EH4547 , ".raw"))
if (!file.exists(rawfileEH4547 )){
file.link(EH4547 , rawfileEH4547 )
}
```
# Usage
Call the constructor
```{r backendInitializeMsBackendRawFileReader, message=FALSE}
beRaw <- Spectra::backendInitialize(
MsBackendRawFileReader::MsBackendRawFileReader(),
files = c(rawfileEH3220, rawfileEH3222, rawfileEH4547))
```
Call the print method
```{r show}
beRaw
```
# Application example
Here we reproduce the Figure 2 of @rawrr `r BiocStyle::Biocpkg("rawrr")`.
The `r BiocStyle::Githubpkg("fgcz/MsBackendRawFileReader")` ships with a
`filterScan` method using functionality provided by the C# libraries by
Thermo Fisher Scientific @rawfilereader.
```{r rawrrFigure2, fig.cap = "Peptide spectrum match. The vertical grey lines indicate the *in-silico* computed y-ions of the peptide precusor LGGNEQVTR++ as calculated by the [protViz]( https://CRAN.R-project.org/package=protViz) package."}
(S <- (beRaw |>
filterScan("FTMS + c NSI Full ms2 487.2567@hcd27.00 [100.0000-1015.0000]") )[437]) |>
plotSpectra()
# supposed to be scanIndex 9594
S
# add yIonSeries to the plot
(yIonSeries <- protViz::fragmentIon("LGGNEQVTR")[[1]]$y[1:8])
names(yIonSeries) <- paste0("y", seq(1, length(yIonSeries)))
abline(v = yIonSeries, col='#DDDDDD88', lwd=5)
axis(3, yIonSeries, names(yIonSeries))
```
# Extend the class
For demonstration reasons, we extent the `MsBackend` class by a filter method.
The `filterIons` function returns spectra if and only if all fragment ions,
given as argument, match. We use `r BiocStyle::CRANpkg("protViz")``::findNN`
binary search
method for determining the nearest mZ peak for each ion.
If the mass error between an ion and an mz value is less than the given mass
tolerance, an ion is considered a hit.
```{r defineFilterIon}
setGeneric("filterIons", function(object, ...) standardGeneric("filterIons"))
setMethod("filterIons", "MsBackend",
function(object, mZ=numeric(), tol=numeric(), ...) {
keep <- lapply(peaksData(object, BPPARAM = bpparam()),
FUN=function(x){
NN <- protViz::findNN(mZ, x[, 1])
hit <- (error <- mZ - x[NN, 1]) < tol & x[NN, 2] >= quantile(x[, 2], .9)
if (sum(hit) == length(mZ))
TRUE
else
FALSE
})
object[unlist(keep)]
})
```
The lines below implement a simple targeted peptide search engine.
The R code snippet takes as input a `MsBackendRawFileReader` object
containing `r length(beRaw)` spectra and y-fragment-ion mZ values determined
for `LGGNEQVTR++`.
```{r applyFilterIons}
start_time <- Sys.time()
X <- beRaw |>
MsBackendRawFileReader::filterScan("FTMS + c NSI Full ms2 487.2567@hcd27.00 [100.0000-1015.0000]") |>
filterIons(yIonSeries, tol = 0.005) |>
Spectra::Spectra() |>
Spectra::peaksData()
end_time <- Sys.time()
```
The defined `filterIons` method runs on
`r length(beRaw |> MsBackendRawFileReader::filterScan("FTMS + c NSI Full ms2 487.2567@hcd27.00 [100.0000-1015.0000]"))` input spectra and returns `r length(X)` spectra.
The runtime is shown below.
```{r runTime}
end_time - start_time
```
Next, we define and apply a method for graphing `LGGNEQVTR` peptide spectrum
matches. Also, the function returns some statistics of the match.
```{r definePlotLGGNEQVTR}
.plot.LGGNEQVTR <- function(x){
yIonSeries <- protViz::fragmentIon("LGGNEQVTR")[[1]]$y[1:8]
names(yIonSeries) <- paste0("y", seq(1, length(yIonSeries)))
plot(x, type ='h', xlim=range(yIonSeries))
abline(v = yIonSeries, col='#DDDDDD88', lwd=5)
axis(3, yIonSeries, names(yIonSeries))
# find nearest mZ value
idx <- protViz::findNN(yIonSeries, x[,1])
data.frame(
ion = names(yIonSeries),
mZ.yIon = yIonSeries,
mZ = x[idx, 1],
intensity = x[idx, 2]
)
}
```
```{r filterIons2, fig.height=8, fig.cap = "Visualizing of the LGGNEQVTR spectrum matches.", echo=FALSE}
op <- par(mfrow=c(4, 1), mar=c(4,4,4,1))
rv <- X |>
lapply(FUN = .plot.LGGNEQVTR) |>
Reduce(f=rbind)
```
```{r sanityCheck}
stats::aggregate(formula = mZ ~ ion, FUN = base::mean, data=rv)
stats::aggregate(formula = intensity ~ ion, FUN = base::max, data=rv)
```
For the sake of demonstration we apply the `Spectra::combinePeaks` method and
aggregate the `r length(X)` spectra into a singe peak matrix.
The statistics returned by `.plot.LGGNEQVTR()` should be identical with the
output of the aggregation code snippet above.
```{r combinePeaks, fig.cap = "Combined LGGNEQVTR peptide spectrum match plot."}
X |>
Spectra::combinePeaks(ppm=10, intensityFun=base::max) |>
.plot.LGGNEQVTR()
```
# Evaluation
## Efficiency - I/O Benchmark
When reading spectra the
`MsBackendRawFileReader:::.RawFileReader_read_peaks`
method is calling the
`rawrr::readSpectrum`
method.
The figure below displays the time performance for reading a single spectrum in
dependency from the chunk size (how many spectra are read in one function call)
for reading different numbers of overall spectra.
```{r readBenchmarkData, fig.cap="I/O Benchmark. The XY plot graphs how many spectra the backend can read in one second versus the chunk size of the rawrr::readSpectrum method for different compute architectures."}
ioBm <- file.path(system.file(package = 'MsBackendRawFileReader'),
'extdata', 'specs.csv') |>
read.csv2(header=TRUE)
# perform and include a local IO benchmark
ioBmLocal <- ioBenchmark(1000, c(32, 64, 128, 256), rawfile = rawfileEH4547)
lattice::xyplot((1 / as.numeric(time)) * workers ~ size | factor(n) ,
group = host,
data = rbind(ioBm, ioBmLocal),
horizontal = FALSE,
scales=list(y = list(log = 10)),
auto.key = TRUE,
layout = c(3, 1),
ylab = 'spectra read in one second',
xlab = 'number of spectra / file')
```
## Effectiveness
We compare the output of the Thermo Fischer Scientific raw files versus
their corresponding mzXML files using `Spectra::MsBackendMzR` relying on the
`r BiocStyle::Biocpkg("mzR")` package.
```{r mzXML}
mzXMLEH3219 <- normalizePath(eh[["EH3219"]])
mzXMLEH3221 <- normalizePath(eh[["EH3221"]])
```
```{r backendInitialize, message=FALSE, fig.cap='Aggregated intensities mzXML versus raw of the 1st 100 spectra.', message=FALSE, warning=FALSE}
if (require(mzR)){
beMzXML <- Spectra::backendInitialize(
Spectra::MsBackendMzR(),
files = c(mzXMLEH3219))
beRaw <- Spectra::backendInitialize(
MsBackendRawFileReader::MsBackendRawFileReader(),
files = c(rawfileEH3220))
intensity.xml <- sapply(intensity(beMzXML[1:100]), sum)
intensity.raw <- sapply(intensity(beRaw[1:100]), sum)
plot(intensity.xml ~ intensity.raw, log = 'xy', asp = 1,
pch = 16, col = rgb(0.5, 0.5, 0.5, alpha=0.5), cex=2)
abline(lm(intensity.xml ~ intensity.raw),
col='red')
}
```
Are all scans of the raw file in the mzXML file?
```{r}
if (require(mzR)){
table(scanIndex(beRaw) %in% scanIndex(beMzXML))
}
```
# Session information {-}
```{r sessionInfo}
sessionInfo()
```
# References {-}