Creating publication-ready LC-MS data plots
14 December 2025
1 Installation
lcmsPlot is a Bioconductor package and to install it we have to use
BiocManager.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("lcmsPlot")2 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.
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)
)3 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.
ms1_samples_metadata <- samples_metadata |> dplyr::filter(ms_level == 1)
ms1_raw_files <- file.path(
mzml_dir,
paste0(ms1_samples_metadata$sample_id, ".mzML"))
lcmsPlot(ms1_raw_files, metadata = ms1_samples_metadata) +
lp_chromatogram(
features = data.frame(
sample_id = c("l-proline-MS1", "l-kynurenine-MS1"),
mz = c(116.0703793, 209.091824),
rt = c(425.74, 365.72)
),
ppm = 5,
rt_tol = 5
) +
lp_facets(facets = "sample_name", free_x = TRUE, ncol = 2)
Next, we plot the chromatograms together with their corresponding MS2 spectra, extracted from the scan closest to the specified retention time for each compound. This allows direct inspection of both chromatographic behavior and fragmentation patterns in a single visualization.
ms2_samples_metadata <- samples_metadata |> dplyr::filter(ms_level == 2)
ms2_raw_files <- file.path(
mzml_dir,
paste0(ms2_samples_metadata$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)
4 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:
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:
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()
5 Session Info
sessionInfo()
#> R Under development (unstable) (2026-01-15 r89304)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.3 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.23-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 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] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] lcmsPlot_0.99.16 BiocStyle_2.39.0
#>
#> loaded via a namespace (and not attached):
#> [1] sass_0.4.10 generics_0.1.4 digest_0.6.39
#> [4] magrittr_2.0.4 evaluate_1.0.5 grid_4.6.0
#> [7] RColorBrewer_1.1-3 bookdown_0.46 fastmap_1.2.0
#> [10] jsonlite_2.0.0 ProtGenerics_1.43.0 mzR_2.45.0
#> [13] tinytex_0.58 BiocManager_1.30.27 scales_1.4.0
#> [16] codetools_0.2-20 jquerylib_0.1.4 cli_3.6.5
#> [19] rlang_1.1.7 Biobase_2.71.0 withr_3.0.2
#> [22] cachem_1.1.0 yaml_2.3.12 otel_0.2.0
#> [25] tools_4.6.0 parallel_4.6.0 BiocParallel_1.45.0
#> [28] dplyr_1.2.0 ncdf4_1.24 ggplot2_4.0.2
#> [31] BiocGenerics_0.57.0 vctrs_0.7.1 R6_2.6.1
#> [34] magick_2.9.0 lifecycle_1.0.5 pkgconfig_2.0.3
#> [37] pillar_1.11.1 bslib_0.10.0 gtable_0.3.6
#> [40] glue_1.8.0 Rcpp_1.1.1 xfun_0.56
#> [43] tibble_3.3.1 tidyselect_1.2.1 knitr_1.51
#> [46] dichromat_2.0-0.1 farver_2.1.2 htmltools_0.5.9
#> [49] patchwork_1.3.2 rmarkdown_2.30 labeling_0.4.3
#> [52] compiler_4.6.0 S7_0.2.1