A few functions for analyzing mass spectrometry alignments in R. Currently, MS-DIAL .txt alignment files are supported.
It's recommended to install mzinspectr directly from GitHub.
install.packages("remotes")
remotes::install_github("https://github.com/ethanbass/mzinspectr/")
It can also be installed via R Universe:
install.packages("mzinspectr", repos="https://ethanbass.r-universe.dev/", type="source")
Export your alignment from MS-DIAL by selecting the Export:Alignment Result menu. Select Area or Height as appropriate.
Read your alignment into R using the ms_read_alignment function, providing the path to your MS-DIAL alignment file. This will produce a list of matrices. The first element will be the actual peak table (tab) with the areas or heights of the features detected by MS-DIAL. The second element will contain the peak metadata returned by MS-DIAL such as retention indices, mass spectra, and any identifications done in MS-DIAL (peak_meta). The third element is intended to hold sample metadata (sample_meta) provided by the user, which can be added using the ms_attach_metadata function.
The ms_attach_metadata function takes three arguments: an MS-DIAL alignment object (x), a dataframe or matrix containing the sample metadata (meta), and a string specifying the column in meta to be matched with the names of the samples (col).
There are currently several different options for feature normalization, including normalization by an internal standard (ms_normalize_itsd), total sum normalization (ms_normalize_tsn) and probabilistic quotient normalization (ms_normalize_pqn). You can also subtract the mean or median value from a set of blanks from each peak using the ms_subtract_blanks function.
There is preliminary support for peak identification by searching a user-provided mass-spectral database through the ms_search_spectra function. The database can be loaded into R from an MSP file using the It takes several parameters, including an ms-dial alignment object (x), a spectral database (db), the column or columns to identify (cols), the maximum retention index shift to exclude a match from consideration (ri_thresh), the relative weight to give spectral similarity versus retention index similarity (spectral_weight), the number of results to return (n_results), and the number of cores to use for parallel processing (mc.cores).
To compile a mass spectral database, I recommend using mspcompiler.
Spectra can be plotted using either "base R" graphics or "plotly" graphics using the ms_plot_spectrum function, which takes an ms-dial alignment object (x) and a column index (col) as argument.
If you use mzinspectr in published work, please cite it as follows:
Bass, E. (2023). mzinspectr: Read and Analyze Mass Spectrometry Alignment Files (version 0.4.2). https://doi.org/10.5281/zenodo.10426253.