---
title: "Intro to RaMS"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Intro-to-RaMS}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

<!-- This vignette is created from a .Rmd file: please edit that instead. -->

```{r, include = FALSE}
options(rmarkdown.html_vignette.check_title = FALSE)
options(tidyverse.quiet = TRUE)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  out.width = "80%",
  fig.align = 'center',
  fig.height = 3,
  fig.width = 6.5
)
```

**Table of contents:**

* **[Basic RaMS usage]**
  + [TICs, BPCs, and metadata]
  + [Adding a column: MS1 data]
  + [Moving along: MS2 data]
* **[Advanced RaMS usage]**
  + [Saving space: EICs and rtrange]
  + [Speeding things up]
  + [Finer control: grabMzmlData, grabMzxmlData]
  + [The nitty-gritty details]
  + [Minified file details]

## Basic RaMS usage

Welcome to `RaMS`! This vignette is designed to provide examples using 
the package at various levels of complexity. Let's jump right in.

If you have your own data, feel free to load it here. If not, there's a couple
small example files you're welcome to use in the "extdata" folder. The first of 
these contains DDA data from a pooled sample, while the others are individual 
samples. I'll be using these throughout.

```{r findfiles, message=FALSE}
library(RaMS)

# Locate the file directory
msdata_dir <- system.file("extdata", package = "RaMS")

# Identify the files of interest
data_files <- list.files(msdata_dir, pattern = "mzML", full.names = TRUE)

# Check that the files identified are the ones expected
basename(data_files)
```

There's only one function to worry about in `RaMS`: the aptly named `grabMSdata`.
This function has a couple arguments with sensible defaults, but you'll always
need to tell it two things: one, which files you'd like to process; and two,
the data you'd like to obtain from those files.

Let's start simple, with a single file and the most basic information about it.

### TICs, BPCs, and metadata

A TIC reports the total intensity measured by the mass analyzer during each 
scan, so the data is parsed into two columns: retention time (rt) and intensity
(int). This makes it easy to read and simple to plot:

```{r loadTIC}
single_file <- data_files[2]

msdata <- grabMSdata(single_file, grab_what = "TIC")

knitr::kable(head(msdata$TIC, 3))
```

Since we asked for a single thing, the TIC, our `file_data` object is a list
with a single entry: the TIC. Let's plot that data:

```{r headerTIC}
plot(msdata$TIC$rt, msdata$TIC$int, type = "l")
```

Simple enough!

A BPC is just like a TIC except that it records the *maximum* intensity measured,
rather than the sum of all intensities. This data is also collected by the mass
analyzer and doesn't need to be calculated.

```{r loadBPC, fig.height=3}
msdata <- grabMSdata(single_file, grab_what = "BPC")
```

Since the data is parsed in a ["tidy" format](https://r4ds.had.co.nz/tidy-data.html), 
it plays nicely with popular packages such as `ggplot2`. Let's use that to plot
our BPC instead of the base R plotting system:

```{r plotBPC, warning=FALSE}
library(tidyverse)
ggplot(msdata$BPC) + geom_line(aes(x=rt, y=int))
```

The advantages of tidy data and `ggplot` become clear when we load more than
one file at a time because we can group and color by the third column, the
name of the file from which the data was read. `RaMS` will also automatically
enable a loading bar if more than one file is loaded; this can be disabled by
setting the `verbosity` parameter to 0.

```{r loadmultiBPC}
msdata <- grabMSdata(data_files[2:4], grab_what = "BPC")

ggplot(msdata$BPC) + geom_line(aes(x=rt, y=int, color=filename))
```

And of course, this means that all of `ggplot`'s aesthetic power can be brought
to your chromatograms as well, so customize away!

```{r ggplotdemo, dpi=144}
ggplot(msdata$BPC) + 
  geom_polygon(aes(x=rt, y=int, color=filename), lwd=1, fill="#FFFFFF44") +
  theme(legend.position = c(0.8, 0.7), plot.title = element_text(face="bold"),
        axis.title = element_text(size = 15)) +
  scale_y_continuous(labels = c(0, "250M", "500M"), breaks = c(0, 2.5e8, 5e8)) +
  scale_colour_manual(values = c("#2596be", "#6c25be", "#bea925")) +
  labs(x="Retention time (minutes)", y="Intensity", 
       title = "Base peak chromatogram", color="Files:") +
  coord_cartesian(xlim = c(7.50, 9), ylim = c(0, 5e8))
```

`RaMS` also provides some basic file metadata extraction capability, although
the focus for this package is on the actual data and other MS packages handle
file metadata much more elegantly. This is one area where there are major
differences between mzML and mzXML file types - the mzXML file type simply
doesn't encode as much metadata as the mzML filetype, so `RaMS` can't extract
it.

```{r metadatademo}
# Since the minification process strips some metadata, I use the 
# less-minified DDA file here
grabMSdata(files = data_files[1], grab_what = "metadata")
```

### Adding a column: MS1 data

MS1 data can be extracted just as easily, by supplying "MS1" to the `grab_what`
argument of `grabMSdata` function.

```{r MS1demo}
msdata <- grabMSdata(data_files[2:4], grab_what = "MS1")

knitr::kable(head(msdata$MS1, 3))
```

So we've now got the *mz* column, corresponding to the mass-to-charge ratio 
(*m/z*) of an ion. This means that we can now filter our data for specific
masses and separate out molecules with different masses.

Note that this also makes the data much larger in R's memory - so don't go
loading hundreds of files simultaneously. If that's necessary, check out the
section below on saving space.

Because `RaMS` returns [data.tables](https://cran.r-project.org/package=data.table) rather than normal `data.frame`s, indexing
is super-fast and a bit more intuitive than with base R. Below, I also use the
`pmppm` function from `RaMS` to produce a mass range from an initial mass and
spectrometer accuracy (here, 5 parts-per-million).

```{r adenineplot, warning=FALSE, message=FALSE}
library(data.table)

adenine_mz <- 136.06232

adenine_data <- msdata$MS1[mz%between%pmppm(adenine_mz, ppm=5)]

ggplot(adenine_data) + geom_line(aes(x=rt, y=int, color=filename))
```

This makes it easy to grab the data for multiple compounds of interest with a
simple loop, provided here by the `purrr` package of the tidyverse:

```{r multicmpdplot}
mzs_of_interest <- c(adenine=136.06232, valine=118.0865, homarine=138.055503)

mass_data <- imap_dfr(mzs_of_interest, function(mz_i, name){
  cbind(msdata$MS1[mz%between%pmppm(mz_i, ppm=10)], name)
})

ggplot(mass_data) + 
  geom_line(aes(x=rt, y=int, color=filename)) + 
  facet_wrap(~name, ncol = 1, scales = "free_y")
```

### Moving along: MS2 data

`RaMS` also handles MS<sup>2</sup> data elegantly. Request it with the "MS2" 
option for `grab_what`, although it's often a good idea to grab the MS1 data
alongside.

```{r MS2data}
msdata <- grabMSdata(data_files[1], grab_what = "everything")
knitr::kable(head(msdata$MS2, 3))
```

DDA data can be plotted nicely with `ggplot2` as well. Typically it makes sense
to filter for a precursor mass, then render the fragments obtained.

```{r MS2demo}
homarine_mz <- 137.047678+1.007276

homarine_MS2 <- msdata$MS2[premz%between%pmppm(homarine_mz, 5)]
homarine_MS2$int <- homarine_MS2$int/max(homarine_MS2$int)

ggplot(homarine_MS2) +
  geom_point(aes(x=fragmz, y=int)) +
  geom_segment(aes(x=fragmz, xend=fragmz, y=int, yend=0)) +
  scale_y_continuous(breaks = c(0, 50, 100), labels = c("0%", "50%", "100%"))
  labs(x="Fragment m/z", y="")
```

This is also the perfect place to enable some interactivity with packages such
as `plotly`, making data exploration not only simple but also enjoyable. 

```{r plotly, eval=FALSE}
## Not run to save space in the vignette:
library(plotly)
msdata <- grabMSdata(data_files, grab_what = c("MS1", "MS2", "BPC"))

compound_MS1 <- msdata$MS1 %>% 
  filter(mz%between%pmppm(homarine_mz, 10)) %>%
  filter(!str_detect(filename, "DDA"))

compound_MS2 <- msdata$MS2[premz%between%pmppm(homarine_mz, 10)] %>% 
  group_by(rt) %>%
  arrange(desc(int)) %>%
  summarise(frags=paste(
    paste(round(fragmz, digits = 3), round(int), sep = ": "), collapse = "\n"),
    .groups="drop"
  ) %>%
  mutate(int=approx(x = compound_MS1$rt, y=compound_MS1$int, xout = rt)$y)
plot_ly(compound_MS1) %>%
  add_trace(type="scatter", mode="lines", x=~rt, y=~int, color=~filename,
            hoverinfo="none") %>%
  add_trace(type="scatter", mode="markers", x=~rt, y=~int,
            text=~frags, hoverinfo="text", showlegend=FALSE,
            marker=list(color="black"), data = compound_MS2) %>%
  layout(annotations=list(x=min(compound_MS2$rt), y=median(compound_MS2$int)*10, 
                          text="Mouse over to see\nMSMS fragments"))
```

Easy access to MS<sup>2</sup> data also allows us to rapidly perform 
simple operations such as searching for a
specific fragment mass. For example, if we know that homarine typically
produces a fragment with a mass of 94.06567, we simply subset the MS<sup>2</sup> data
for fragments in a range around that mass:

```{r homarine_fragsearch}
homarine_frag_mz <- 94.06567
msdata$MS2[fragmz%between%pmppm(homarine_frag_mz, ppm = 5)] %>%
  head() %>% knitr::kable()
```

We find that there's not only homarine that produces that fragment,
but several another compounds. Precursor masses like this can then
be searched manually in online databases or, since the data is already in R,
passed to a script that automatically searches them.

Similarly, we can easily search instead for neutral losses with this method. If
we suspect other molecules are producing a similar neutral loss as homarine:

```{r homarine_neutsearch}
homarine_neutral_loss <- homarine_mz - homarine_frag_mz

msdata$MS2 <- mutate(msdata$MS2, neutral_loss=premz-fragmz) %>%
  select("rt", "premz", "fragmz", "neutral_loss", "int", "voltage", "filename")
msdata$MS2[neutral_loss%between%pmppm(homarine_neutral_loss, ppm = 5)] %>%
  arrange(desc(int)) %>% head() %>% knitr::kable()
```

We can again confirm our suspicions that there's another signal with
a similar neutral loss: one with a mass of 137.0470.

## Advanced RaMS usage

The sections below will likely be of interest to users who are already familiar
with `RaMS` and are looking to optimize speed, reduce memory requirements, or 
are otherwise interested in the details of what `RaMS` does under the hood. If
you're just getting started, I strongly recommend applying `RaMS` to your own
data before you read on.

### Saving space: EICs and rtrange

Mass-spec files are typically tens or hundreds of megabytes in size, which means
that the simultaneous analysis of many files can easily exceed a computer's
memory. Since `RaMS` stores all data in R's working memory, this can become a
problem for large analyses.

However, much of the usage envisioned for `RaMS` on this scale doesn't require
access to the entire file, the entire time. Instead, users are typically 
interested in a few masses of interest or a specific time window. This means
that while each file still needs to be read into R in full to find the data
of interest, extraneous data can be discarded before the next file is loaded.

This functionality can be enabled by passing "EIC" and/or "EIC_MS2" to the
`grab_what` argument of `grabMSdata`, along with a vector of masses to extract
(mz) and the instrument's ppm accuracy. When this is enabled, files are read
into R's memory sequentially, the mass window is extracted, and the rest of
the data is discarded.

```{r EICdemo}
all_data <- grabMSdata(data_files, grab_what = c("MS1", "MS2"))

mzs_of_interest <- c(adenine=136.06232, valine=118.0865)
small_data <- grabMSdata(data_files, grab_what = c("EIC", "EIC_MS2"),
                         mz=mzs_of_interest, ppm = 5)

all_data$MS1 %>%
  mutate(type="All data") %>%
  rbind(small_data$EIC %>% mutate(type="Extracted data only")) %>%
  filter(!str_detect(filename, "DDA")) %>%
  group_by(rt, filename, type) %>%
  summarise(TIC=sum(int), .groups="drop") %>%
  ggplot() +
  geom_line(aes(x=rt, y=TIC, color=filename)) +
  facet_wrap(~type, ncol = 1) +
  theme(legend.position = "top")

# Size reduction factor:
as.numeric(object.size(all_data)/object.size(small_data))
```

As expected, the size of the `small_data` object is much smaller than the
`all_data` object, here by a factor of about 50x. For files that haven't 
already been "minified", that size reduction will be even 
more significant. Of course, this comes with the cost of needing to re-load
the data a second time if a new mass feature becomes of interest but this 
shrinkage is expecially valuable for targeted analyses where the analytes of
interest are known in advance.

A second way of reducing file size is to constrain the retention time dimension
rather than the m/z dimension. This can be done with the `rtrange` argument,
which expects a length-two vector corresponding to the upper and lower bounds
on retention time. This is useful when a small time window is of interest, and
only the data between those bounds is relevant.

```{r rtrangedemo}
small_data <- grabMSdata(data_files, grab_what = c("MS1", "MS2"), rtrange = c(5.5, 7))

all_data$MS1 %>%
  mutate(type="All data") %>%
  rbind(small_data$MS1 %>% mutate(type="Extracted data only")) %>%
  filter(!str_detect(filename, "DDA")) %>%
  group_by(rt, filename, type) %>%
  summarise(TIC=sum(int), .groups="drop") %>%
  ggplot() +
  geom_line(aes(x=rt, y=TIC, color=filename)) +
  facet_wrap(~type, ncol = 1) +
  theme(legend.position = "top")

# Size reduction factor:
as.numeric(object.size(all_data)/object.size(small_data))
```

The savings are more modest here, but constraining
the retention time actually speeds up data retrieval slightly. Since *m/z* and
intensity data is encoded in mzML and mzXML files while retention time 
information is not, eliminating scans with a retention time window removes
the need to decode the intensity and *m/z* information in those scans. 

However, decoding is rarely the rate-limiting step and for more information 
about speeding things up, continue to the next section.

### Speeding things up

So, `RaMS` isn't fast enough for you? Let's see what we can do to improve that.
The first step in speeding things up is discovering what's slow. Typically,
this is the process of reading in the mzML/mzXML file rather than any processing 
that occurs afterward, but this is not always the case. To examine bottlenecks,
`RaMS` includes timing information that's produced if the `verbosity` argument
is set to 2 or higher.

```{r verbosedemo}
all_data <- grabMSdata(data_files, grab_what = c("MS1", "MS2"), verbosity = 2)
```

In general, slow file read times can be improved by compressing the data. mzML
files are highly compressible, with options to compress both the data itself
using the `zlib` method and the files as a whole using `gzip`. 

`gzip` is the simplest one to use, as a plethora of methods exist to compress
files this way, including online sites. `RaMS` can read the data directly
from a gzipped file, no decompression necessary, so this is an easy way to
reduce file size and read times. Sharp-eyed users will have noticed that the
demo files are already gzipped, which is part of the reason they are so small.

`zlib` compression is slightly trickier, and is most often performed with tools
such as [Proteowizard](http://proteowizard.sourceforge.net/doc_users.html)'s 
`msconvert` tool with the option "-z". 

Read times may also be slow if files are being accessed via the Internet, either
through a VPN or network drive. If your files are stored elsewhere,
consider first moving those files somewhere more local before reading data from
them.

If the bottleneck appears when reading MS<sup>1</sup> data, consider restricting
the retention time range with the `rtrange` argument or using more detailed
profiling tools such as RStudio's "Profile" options or the `profvis` package.
Pull requests that improve data processing speed are always welcome!

While the package is not currently set up for parallel processing, this is
a potential future feature if a strong need is demonstrated.

### Finer control: grabMzmlData, grabMzxmlData

While there's only one main function (`grabMSdata`) in `RaMS`, you may have noticed that two
other functions have been exposed that perform similar tasks: `grabMzmlData`
and `grabMzxmlData`. The main function `grabMSdata` serves as a wrapper around
these two functions, which detects the file type, adds the "filename"
column to the data, and loops over multiple files if provided. However, there's
often reason to use these internal functions separately.

For one, the objects themselves are smaller because they don't have the filename
column attached yet. You as the user will need to keep track of which data
belongs to which files in this case.

Another use case might be applying functions to each file individually, perhaps
aligning to a reference chromatogram or identifying peaks. Rather than spending
the time to bind the files together and immediately separate them again, these
functions have been exposed to skip that step.

Finally, these functions are useful for parallelization. Because iterating
over each mass-spec file is often the largest reasonable chunk, these functions
can be passed directly to parallel processes like `mclapply` or `doParallel`.
However, parallelization is a beast best handled by individual users because
its actual implementation often differs wildly and its utility depends strongly
on individual setups (remember that parallelization won't help with slow I/O
times, so it may not always improve data processing speed.)

```{r, eval=FALSE}
## Not run:
library(parallel)
cl <- makeCluster(getOption("cl.cores", detectCores()-1))
output_data <- parLapply(data_files, grabMzmlData, grab_what="everything", cl = cl)

library(foreach)
library(doParallel)
registerDoParallel(detectCores()-1)
output_data <- foreach (i=data_files) %dopar% {
  RaMS::grabMzmlData(i, grab_what="everything")
}
stopImplicitCluster()
```

### The nitty-gritty details

`RaMS` is possible because mzML and mzXML documents are fundamentally
[XML](https://en.wikipedia.org/wiki/XML)-based. This means that we can leverage
speedy and robust XML parsing packages such as `xml2` to extract the data.
Fundamentally, `RaMS` relies on [XPath](https://www.w3schools.com/xml/xpath_intro.asp)
navigation to collect various bits of mass-spec data, and the format of mzML
and mzXML files provides the tags necessary. That means a lot of `RaMS` code
consists of lines like the following:

```{r, eval=FALSE}
## Not run:
data_nodes <- xml2::xml_find_all(mzML_nodes, xpath="//d1:precursorMz")
raw_data <- xml2::xml_attr(data_nodes, "value")
```

...plus a lot of data handling to get the output into the tidy format.

The other tricky bit of data extraction is converting the (possibly compressed)
binary data into R-friendly objects. This is usually handled with code like that
shown below. Many of the settings can be deduced from the file, but sometimes
compression types need to be guessed at and will throw a warning if so.

```{r, eval=FALSE}
## Not run:
decoded_binary <- base64enc::base64decode(binary)
raw_binary <- as.raw(decoded_binary)
decomp_binary <- memDecompress(raw_binary, type = file_metadata$compression)
final_binary <- readBin(decomp_binary, what = "double",
                        n=length(decomp_binary)/file_metadata$mz_precision,
                        size = file_metadata$mz_precision)

# See https://github.com/ProteoWizard/pwiz/issues/1301
```

Fundamentally, mass-spectrometry data is formatted as a 
[ragged array](https://en.wikipedia.org/wiki/Jagged_array), with an unknown
number of *m/z* and intensity values for a given scan. This makes it difficult
to encode neatly without interpolating, but tidy data provides a solution by
stacking those arrays rather than aligning them into some sort of matrix.

This ragged shape is also the reason that subsetting mass-spec data by retention
time is trivial - grab the scans that correspond to the desired retention times
and you're done. Subsetting by mass, on the other hand, requires decoding each
and every scan's *m/z* and intensity data. If you're reading a book and only
want a couple chapters, it's easy to flip to those sections. If you're looking
instead for every time a specific word shows up, you've gotta read the whole
thing.

For more information about the mzML data format and its history, check out the
specification at https://www.psidev.info/mzML.

### Minified file details

While many `RaMS` users will arrive at this vignette with their own data, 
several small files are provided for demonstration. These files originate from the [Ingalls Lab](https://sites.google.com/view/anitra-ingalls) at the University of Washington, USA and are published in the manuscript [**"Metabolic consequences of cobalamin scarcity in diatoms as revealed through metabolomics"**](https://www.sciencedirect.com/science/article/pii/S1434461018301184). Files were downloaded from the corresponding [Metabolights repository](https://www.ebi.ac.uk/metabolights/MTBLS703/files).

Due to CRAN's restrictions on package size, the original files cannot be included. Instead, MS1 data was compressed by extracting EICs for each compound in the Ingalls Lab [list of targeted compounds](https://github.com/IngallsLabUW/Ingalls_Standards/blob/b098927ea0089b6e7a31e1758e7c7eaad5408535/Ingalls_Lab_Standards_NEW.csv) as of August 24, 2020. EIC width was set with a ppm value of 10 and time data were filtered to fall between 4 and 9 minutes. These compressed files were then written out using [`MSnbase`'s](https://bioconductor.org/packages/release/bioc/html/MSnbase.html) `writeMSdata` function after coercing the existing data frame into an `MSnExp` object. Finally, they were gzip-compressed with [`R.utils`'s](https://cran.r-project.org/package=R.utils) `gzip` function and default parameters.

MS2 data was processed via [Proteowizard's `msconvert` tool](http://proteowizard.sourceforge.net/tools/msconvert.html) instead. Files were converted with the flags `-gz` and `--noindex` to save space, and three filters were used: 
 - `--filter "scanTime [240,540]"`
 - `--filter "mzWindow [0,120]"`
 - `--filter "absolute 100000 most-intense"`

These filters reduced the MS2 data to a sufficiently limited size that they could be published with the package. The mzXML file was created identically, but the `--mzXML` flag was added.
