R Data Structure Conversion

Convert between R data structures for mass spectrometry imaging

Summary

Convert mass spectrometry imaging data between different R object formats—namely Cardinal's MSProcessedImagingExperiment/MSContinuousImagingExperiment and mass2adduct's msimat format—to enable cross-tool interoperability while preserving peak-binned intensity information.

When to use

You have preprocessed MSI data in Cardinal format (post-peakBin) and need to apply mass2adduct's adduct-detection workflow, OR you have exported MSI intensity data as CSV from third-party software (SCiLS, MSiReader) and must convert it into a standardized R object for downstream analysis of molecular adducts and spatial correlations.

When NOT to use

• Your MSI data in Cardinal format has not yet been peak-binned; cardinal2msimat() requires the peakBin() step to have already been executed.
• Your CSV is not a plain-text, rectangular intensity matrix; msimat() assumes standard row (peaks) × column (pixel) layout with numeric intensities.
• You intend to preserve all pre-processing metadata and imaging coordinates beyond what msimat stores; msimat focuses on intensity values and mass labels, not full experimental provenance.

Inputs

• Cardinal MSProcessedImagingExperiment or MSContinuousImagingExperiment object (post-peakBin, pre-process)
• ImzML file (via Cardinal readImzML)
• CSV file exported from MSiReader or SCiLS (plain-text, intensity-export format)
• Plain-text file of mass values (one per line, for simple numeric vector input)

Outputs

• msimat object (mass2adduct class with intensity matrix, mass vector, and pixel coordinates)
• R data.frame representation of MSI intensity data
• Standard R histogram object (when hist() is applied to massdiff output)

How to apply

Load your MSI data using the appropriate source-specific function: use Cardinal's readImzML() followed by the standard preprocessing pipeline (normalize, smoothSignal, reduceBaseline), apply peakBin() with a pre-existing peaklist, then call process() to finalize the MSProcessedImagingExperiment object. Then convert directly to msimat format using the cardinal2msimat() function from mass2adduct. Alternatively, if starting from CSV (e.g., from SCiLS or MSiReader intensity export), load it directly using msimat("filename.csv", sep=";"), which creates a msimat object with explicit mass, pixel, and intensity columns. The choice of conversion path depends on your starting format: Cardinal objects require intermediate preprocessing before conversion; CSV files require only separator specification. Verify correct conversion by comparing mass spectra plots between the original and converted objects using plot()—they should be visually identical.

Related tools

• Cardinal (Source MSI data format; used for preprocessing (normalize, smoothSignal, reduceBaseline, peakBin) before conversion to msimat) — http://cardinalmsi.org/
• mass2adduct (Target R package containing msimat class and cardinal2msimat() conversion function) — https://github.com/kbseah/mass2adduct
• MSiReader (Source software for exporting MSI intensity data as CSV; output can be imported via msimat())
• SCiLS (Source software for exporting MSI intensity data as CSV; output can be imported via msimat())
• R (Runtime environment for executing conversion functions and handling data structures)

Examples

d_peaks <- readImzML("msi_file") %>% normalize() %>% smoothSignal() %>% reduceBaseline() %>% peakBin(peaklist) %>% process; d_msimat <- cardinal2msimat(d_peaks); plot(d_msimat)

Evaluation signals

• Mass spectra plots from the original object and the converted msimat object are visually identical when rendered with plot()—same peak heights, positions, and relative intensities.
• The msimat object has three internal elements: mass vector, intensity matrix, and pixel coordinate mapping; verify structure with str() or class() returning 'msimat'.
• Downstream operations (e.g., massdiff, corrPairsMSI) execute without error and produce expected dimensionality—massdiff() returns a data.frame with three columns (parent mass A, adduct mass B, difference).
• Object size in memory is reasonable relative to the input dataset dimensions; if using triplet format for large matrices, verify with object.size() that redundant zeroes have been eliminated.
• No NaN, Inf, or unexpected NA values appear in the mass or intensity vectors after conversion.

Limitations

• Cardinal conversion (cardinal2msimat) requires Cardinal ≥ 2.2 and the data must already be peak-binned; raw or partially processed Cardinal objects will fail or produce incorrect results.
• CSV import via msimat() assumes rectangular matrix format (peaks as rows, pixels as columns); ragged or transposed matrices require manual reformatting before import.
• Very large CSV files (several GB) may exceed available RAM even when using the triplet-format Perl script (msimunging.pl); no automatic chunking is applied during msimat() import—users must pre-filter or reformat offline.
• The msimat format does not preserve all Cardinal metadata (e.g., instrument parameters, full processing history); conversion is lossy in terms of provenance.
• If using the triplet format (rows, cols, vals, peaks, spots files) generated by msimunging.pl, all five files must be present and consistent; missing or corrupted files will cause msimat() to fail.

Evidence

• [readme] Cardinal MSProcessedImagingExperiment or MSContinuousImagingExperiment objects can be converted to msimat format: "Cardinal MSProcessedImagingExperiment or MSContinuousImagingExperiment objects (requires Cardinal v2.2 and above) can be converted to mass2adduct's msimat format."
• [readme] Data must be preprocessed and peak-binned before conversion: "However, the data must first be pre-processed, with peaks already binned with the peakBin() function from Cardinal."
• [readme] CSV files from third-party MSI software are imported via msimat with separator specification: "MSI data exported from the MSiReader software with the "intensity export" function, or from some other third party software that can output plain-text comma-delimited tables (CSV format)."
• [readme] msimat function loads data into R as a data.frame with specific class: "Import the data into R as a data.frame: d <- msimat("msi.csv", sep=";") class(d) # "msimat""
• [readme] Converted objects can be validated by comparing plots from source and target formats: "Compare peak histograms from Cardinal vs mass2adduct, should look the same plot(d_peaks) plot(d_msimat)"
• [readme] Large CSV files can be converted to triplet format offline using a Perl script: "A Perl script msimunging.pl (in the inst/ subdirectory of the package source) is provided to do this format conversion before importing the files into R."