---
title: "Savely Store MS Data Objects in a Portable Stash"
output:
    BiocStyle::html_document:
        toc_float: true
vignette: >
    %\VignetteIndexEntry{Savely Store MS Data Object in a Portable Stash}
    %\VignetteEngine{knitr::rmarkdown}
    %\VignetteEncoding{UTF-8}
    %\VignettePackage{SpectraStash}
    %\VignetteDepends{Spectra,SpectraStash,BiocStyle,alabaster.base,fs}
---

```{r, echo = FALSE, message = FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE)
library(BiocStyle)
```

# Introduction

Data objects in R can be serialized to disk in R's *rds* or *RData* format using
the base R `save()` function and re-imported using the `load()` function. This
R-specific binary data format can however not be used easily by other
programming languages preventing the exchange of R data objects between software
or programming languages. The *MsStash* package defines basic classes and
generic methods to export and import mass spectrometry (MS) data objects in
various storage formats aiming to facilitate data exchange between software. The
*SpectraStash* package implements portable data storage formats (stashes) for
data classes from the `r Biocpkg("Spectra")` package, including the `Spectra`
object and it's various data backends.

# Installation

The package can be installed with the *BiocManager* package. To install
*BiocManager* use `install.packages("BiocManager")` and, after that,
`BiocManager::install("RforMassSpectrometry/SpectraStash")` to install this
package.

# A stash for `Spectra` objects

MS data objects can be saved and restored through the `saveMsObject()` and
`readMsObject()` functions into (or from) MS data stashes. Supported stash
formats and their respective parameter objects are:

- `PlainTextParam`: storage of data in (a custom) plain text file format.
- `AlabasterParam`: storage of MS data using Bioconductor's `r
  Biocpkg("alabaster.base")` framework using files in HDF5 and JSON format. MS
  stashes in this format fully support the functions `saveObject()` and
  `readObject()` from *alabaster.base*.

See also the vignette from the `r Biocpkg("MsStash")` for details on the formats
and implementation notes.

As an example we create below a `Spectra` object from two example MS data files
from the *MsDataHub* package.

```{r, message = FALSE}
library(Spectra)
library(SpectraStash)
library(MsDataHub)
fls <- c(X20171016_POOL_POS_1_105.134.mzML(),
         X20171016_POOL_POS_3_105.134.mzML())
sps <- Spectra(fls)
sps
```

We next filter the data restricting to spectra and mass peaks with a retention
time between 20 and 200 seconds and an *m/z* between 110 and 120.

```{r}
sps <- filterRt(sps, c(20, 200))
sps <- filterMzRange(sps, c(110, 120))
sps
```

We next store this `Spectra` object to a *SpectraStash* using the
`saveMsObject()` function. We use an alabaster format and define the location of
the stash with the `path` parameter of `AlabasterParam`. For the present example
we save it to a temporary folder.

```{r}
#' Define the location of the stash
d <- file.path(tempfile(), "spectra_stash")

#' Configure the format and location
ap <- AlabasterParam(d)

#' Save the `Spectra` object to the stash
saveMsObject(sps, ap)
```

The content of the stash folder is:

```{r}
library(fs)
dir_tree(d)
```

In alabaster format, each slot of the `Spectra` object is stored into its own
sub directory. `Spectra` objects don't handle the MS data itself, but rely on a
`MsBackend` to provide this data. The `MsBackend` used by the `Spectra` object
is stored into it's own stash located in the *backend* directory of the
SpectraStash. The `Spectra` object can be restored again with `readMsObject()`:

```{r}
res <- readMsObject(Spectra(), ap)
res
```

We need to specify the type of the object to restore with the first parameter of
the function - in our case `Spectra()`. The full `Spectra` object was restored,
including the processing queue and history.

We can also read (restore) only the `MsBackend` from the SpectraStash. Since the
present stash is in alabaster format we can either use `readMsObject()` or also
the `readObject()` from *alabaster.base*:

```{r}
library(alabaster.base)
be <- readObject(file.path(d, "backend"))
be
```

Or using `readMsObject()`:

```{r}
be <- readMsObject(MsBackendMzR(), AlabasterParam(file.path(d, "backend")))
be
```

## Creating self-contained stashes

Our example `Spectra` object uses an `MsBackendMzR` backend which keeps only
limited information in memory and retrieves the peaks data (i.e., the *m/z* and
intensity values) from the original MS data files upon demand. The stash for
`MsBackendMzR` objects contains therefore also only the spectra metadata and a
reference to the original MS data files - but no peaks data.

If the original MS data files were moved to a different location or if the
SpectraStash folder was moved to another computer, the updated path to the raw
MS data files would need to be provided with the `spectraPath` parameter of the
`readMsObject()` function. As an alternative, it is also possible to create a
*self-contained* stash setting `consolidate = TRUE` in `saveMsObject()`. We
below save our `Spectra` object again, this time into a self-contained
stash.

```{r}
d2 <- file.path(tempdir(), "spectra_stash2")
saveMsObject(sps, AlabasterParam(d2), consolidate = TRUE)
```

The `consolidate = TRUE` parameter is passed to the `saveMsObject()` call of the
`MsBackend`, which, for `MsBackendMzR` copies the original MS data files
**into** the stash folder:

```{r}
dir_tree(d2)
```

Note the two additional files in the *backend* folder - these are the original
MS data files in mzML format. Such a self-contained stash folder allows to
restore the full data even if the stash is moved to another file system. Of
course, depending on the size of the data set and the respective raw MS data
files, the stash folder can become very large.

## Stashes for `Spectra` with in-memory backends

In addition to the *on-disk* backends `MsBackendMzR` and `MsBackendHdf5Peaks`,
*Spectra* defines also *in-memory* backends `MsBackendMemory` and
`MsBackendDataFrame`, which keep the full MS data in memory. Below we change the
backend of our `sps` object to `MsBackendMemory`:

```{r}
sps <- setBackend(sps, MsBackendMemory())
sps
```

We next stash this updated `Spectra` object removing first the stash directory
of the previous SpectraStash (because overwriting stash directories is not
allowed).

```{r, warning = FALSE}
#' Remove the existing SepctraStash
unlink(d2, recursive = TRUE)

#' Store the `Spectra` object in alabaster format
saveMsObject(sps, AlabasterParam(d2))
```

Inspecting the content of the stash folder we can see a different structure:

```{r}
dir_tree(d2)
```

The MS peaks data is now stored within a file *peaks.h5*, a file in a HDF5
format used by the `MsBackendHdf5Peaks` backend: saving in-memory backends
changes the data first to a `MsBackendHdf5Peaks` backend which is then stored
into an additional *backend* sub-folder of the stash. We can restore the
`Spectra` object with:

```{r}
readMsObject(Spectra(), AlabasterParam(d2))
```

In addition, we can restore the `MsBackendMemory` with:

```{r}
readMsObject(MsBackendMemory(), AlabasterParam(file.path(d2, "backend")))
```

and also the `MsBackendHdf5Peaks` which is used as the actual data storage
format for the in-memory `MsBackendMemory` (note the double *backend*
sub-folder):

```{r}
readMsObject(MsBackendHdf5Peaks(),
             AlabasterParam(file.path(d2, "backend", "backend")))
```

# Session information

```{r}
sessionInfo()
```