---
title: "Creating publication-ready LC-MS data plots"
author: "Ossama Edbali"
date: "14 December 2025"
output:
  BiocStyle::html_document:
    toc: true
    toc_depth: 2  
    number_sections: true  
    toc_float: true
vignette: >
  %\VignetteIndexEntry{Creating publication-ready LC-MS data plots}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
bibliography: references.bib
csl: biomed-central.csl
---

```{r setup, include = FALSE}
knitr::opts_knit$set(
    root.dir = dirname(getwd())
)

knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>"
)
```

```{r init, message = FALSE, echo = FALSE, results = "hide"}
library(BiocStyle)
library(lcmsPlot)
```

# Installation

`lcmsPlot` is a Bioconductor package and to install it we have to use
[BiocManager](https://cran.r-project.org/web/packages/BiocManager/index.html).

```{r install, eval=FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("lcmsPlot")
```

# Load data

The dataset used in this vignette originates from a prepared sample spiked with
known standards, specifically L-Proline and L-Kynurenine. These compounds are
highlighted throughout the vignette and serve as concrete examples to illustrate
the different steps of the workflow.

To support reproducibility and hands-on exploration, example data files are
provided in `inst/extdata/standards-mzml.zip`.

Two types of files are included:

- An MS1-only file, which is suitable for tasks such as peak detection, feature
alignment, and quantitation.
- An MS2 file, which additionally enables exploration of fragmentation spectra
for compound identification and spectral matching.

```{r load-data, message=FALSE}
library(lcmsPlot)

exdir <- tempdir()

# Unzip the file containing the mzML files
utils::unzip(
    system.file("extdata", "standards-mzml.zip", package = "lcmsPlot"),
    exdir = exdir
)

mzml_dir <- file.path(exdir, "mzml")

samples_metadata <- data.frame(
    sample_id = c(
        "l-proline-MS1",
        "l-proline-MS2",
        "l-kynurenine-MS1",
        "l-kynurenine-MS2"),
    sample_name = c(rep("L-Proline", 2), rep("L-Kynurenine", 2)),
    ms_level = c(1, 2, 1, 2)
)
```

# Overview of the data

The following figure illustrates the presence of the two spiked standards,
L-Proline and L-Kynurenine, in the MS1-only samples.
For each compound, an extracted ion chromatogram is generated using its expected
m/z value and retention time, with narrow tolerances to precisely
capture the corresponding signal.

```{r data-overview}
ms1_samples_metadata <- samples_metadata |> dplyr::filter(ms_level == 1)
ms1_raw_files <- file.path(
    mzml_dir,
    paste0(ms1_samples_metadata$sample_id, ".mzML"))

lcmsPlot(ms1_raw_files, metadata = ms1_samples_metadata) +
    lp_chromatogram(
        features = data.frame(
            sample_id = c("l-proline-MS1", "l-kynurenine-MS1"),
            mz = c(116.0703793, 209.091824),
            rt = c(425.74, 365.72)
        ),
        ppm = 5,
        rt_tol = 5
    ) +
    lp_facets(facets = "sample_name", free_x = TRUE, ncol = 2)
```

Next, we plot the chromatograms together with their corresponding MS2 spectra,
extracted from the scan closest to the specified retention time
for each compound. This allows direct inspection of both chromatographic
behavior and fragmentation patterns in a single visualization.

```{r plot-chromatograms-with-spectra}
ms2_samples_metadata <- samples_metadata |> dplyr::filter(ms_level == 2)
ms2_raw_files <- file.path(
    mzml_dir,
    paste0(ms2_samples_metadata$sample_id, ".mzML"))

lcmsPlot(ms2_raw_files, metadata = ms2_samples_metadata) +
    lp_chromatogram(
        features = data.frame(
            sample_id = c("l-proline-MS2", "l-kynurenine-MS2"),
            mz = c(116.0703793, 209.091824),
            rt = c(425.74, 365.72)
        ),
        ppm = 5,
        rt_tol = 5
    ) +
    lp_spectra(
        rt = c("l-proline-MS2" = 425.74, "l-kynurenine-MS2" = 365.72),
        mode = "closest",
        ms_level = 2) +
    lp_facets(facets = "sample_name", free_x = TRUE, ncol = 2)
```

# Custom themes

You can fully customise the appearance of your plots by modifying the
underlying `ggplot2` object. To do this, simply extract the plot using the
`lp_get_plot()` function, which returns the raw `ggplot2` object, and then apply
any desired theme settings. This gives you complete flexibility to tailor the
visualisation to your needs; for example, adjusting fonts, colors, backgrounds,
grid lines, or margins.
By leveraging custom themes, you can ensure consistency with your
project/manuscript's style guidelines, improve readability, or highlight
specific aspects of your data.

Firstly, define the custom theme using the `ggplot2` `theme` function:

```{r custom-theme}
my_custom_theme <- function() {
    ggplot2::theme(
        # Title
        plot.title = ggplot2::element_text(
            size = 16,
            margin = ggplot2::margin(b = 15)),
        
        # Axis labels
        axis.title = ggplot2::element_text(
            size = 11,
            face = "bold",
            color = "#1d293d"),
        
        # Facet strip
        strip.text = ggplot2::element_text(
            color = "#1d293d",
            size = 9,
            hjust = 0,
            margin = ggplot2::margin(t = 3, r = 10, b = 6, l = 10)
        ),
        strip.background = ggplot2::element_rect(
            color = "#90a1b9",
            fill = "#e2e8f0",
            linewidth = 0.8),
        
        # Box around the panel
        panel.border = ggplot2::element_rect(
            color = "#90a1b9",
            fill = NA,
            linewidth = 1),
        
        # Customize grid lines inside the plot
        panel.grid.major = ggplot2::element_line(
            color = "#e2e8f0",
            linewidth = 0.5),
        panel.grid.minor = ggplot2::element_line(
            color = "#e2e8f0",
            linewidth = 0.5)
    )
}
```

Generate the plot with `lcmsPlot`, retrieve the underlying plot object using
`lp_get_plot()`, and then apply the custom theme:

```{r plot-with-custom-theme}
lcmsPlot(ms1_raw_files, metadata = ms1_samples_metadata) +
    lp_chromatogram(
        features = data.frame(
            sample_id = c("l-proline-MS1", "l-kynurenine-MS1"),
            mz = c(116.0703793, 209.091824),
            rt = c(425.74, 365.72)
        ),
        ppm = 5,
        rt_tol = 5
    ) +
    lp_facets(facets = "sample_name", free_x = TRUE, ncol = 2) +
    lp_get_plot() +
    my_custom_theme()
```

```{r cleanup, echo=FALSE, results='hide', message=FALSE, warning=FALSE}
unlink(mzml_dir, recursive = TRUE, force = TRUE)
```

# Session Info

```{r session-info}
sessionInfo()
```
