MSIGen: An Open-Source Python Package for Processing and Visualizing Mass Spectrometry Imaging Data Article Swipe
Emerson Hernly
,
Hang Hu
,
Julia Laskin
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2024-brc8d
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2024-brc8d
Mass spectrometry imaging (MSI) provides information about the spatial localization of molecules in complex samples with high sensitivity and molecular selectivity. Although point-wise data acquisition, in which mass spectra are acquired at pre-defined points in a grid pattern, is common in MSI, several MSI techniques use line-wise data acquisition. In the line-wise mode, the imaged surface is continuously sampled along consecutive parallel lines and MSI data are acquired as a collection of line scans across the sample. Furthermore, aside from the standard imaging mode in which a full mass spectrum is acquired in each pixel of the image, other data acquisition modes have been developed to enhance the molecular specificity, enable separation of isobaric and isomeric species, and improve the sensitivity to facilitate imaging of low abundance species. These methods, including MS/MS-MSI in both MS2 and MS3 modes, multiple-reaction monitoring (MRM)-MSI, and ion mobility spectrometry (IMS)-MSI have all demonstrated their capabilities, but their broader implementation is limited by the existing MSI analysis software. Here, we present MSIGen, an open-source Python package for the visualization of MSI experiments performed in line-wise acquisition mode containing MS1, MS2, and IMS data. The package supports multiple vendor-specific and open-source data formats. It is available for download from the Python Package Index (PyPI) and its source-code is available at https://github.com/LabLaskin/MSIGen. This package contains tools for targeted extraction of ion images, and allows for normalization, and exportation as image arrays or publication-style images. MSIGen offers multiple interfaces allowing for accessibility and easy integration with other workflows. Considering its support for a wide variety of MSI imaging modes and vendor formats, MSIGen is a valuable tool for the visualization and analysis of MSI data.
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- en
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- https://doi.org/10.26434/chemrxiv-2024-brc8d
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https://doi.org/10.26434/chemrxiv-2024-brc8dDigital Object Identifier
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MSIGen: An Open-Source Python Package for Processing and Visualizing Mass Spectrometry Imaging DataWork title
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preprintOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-05-06Full publication date if available
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Emerson Hernly, Hang Hu, Julia LaskinList of authors in order
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https://doi.org/10.26434/chemrxiv-2024-brc8dPublisher landing page
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https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/66329c1591aefa6ce1e76036/original/msi-gen-an-open-source-python-package-for-processing-and-visualizing-mass-spectrometry-imaging-data.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/66329c1591aefa6ce1e76036/original/msi-gen-an-open-source-python-package-for-processing-and-visualizing-mass-spectrometry-imaging-data.pdfDirect OA link when available
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Python (programming language), Open source, Mass spectrometry imaging, Computer science, Mass spectrometry, Computer graphics (images), Chemistry, Programming language, Chromatography, SoftwareTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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64Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.lines | 62 |
| abstract_inverted_index.mode, | 52 |
| abstract_inverted_index.modes | 101, 260 |
| abstract_inverted_index.other | 98, 248 |
| abstract_inverted_index.pixel | 94 |
| abstract_inverted_index.scans | 73 |
| abstract_inverted_index.their | 149, 152 |
| abstract_inverted_index.tools | 218 |
| abstract_inverted_index.which | 26, 85 |
| abstract_inverted_index.(PyPI) | 207 |
| abstract_inverted_index.MSIGen | 237, 264 |
| abstract_inverted_index.Python | 169, 204 |
| abstract_inverted_index.across | 74 |
| abstract_inverted_index.allows | 226 |
| abstract_inverted_index.arrays | 233 |
| abstract_inverted_index.common | 39 |
| abstract_inverted_index.enable | 110 |
| abstract_inverted_index.image, | 97 |
| abstract_inverted_index.imaged | 54 |
| abstract_inverted_index.modes, | 137 |
| abstract_inverted_index.offers | 238 |
| abstract_inverted_index.points | 33 |
| abstract_inverted_index.vendor | 262 |
| abstract_inverted_index.MSIGen, | 166 |
| abstract_inverted_index.Package | 205 |
| abstract_inverted_index.broader | 153 |
| abstract_inverted_index.complex | 13 |
| abstract_inverted_index.enhance | 106 |
| abstract_inverted_index.images, | 224 |
| abstract_inverted_index.images. | 236 |
| abstract_inverted_index.imaging | 2, 82, 123, 259 |
| abstract_inverted_index.improve | 118 |
| abstract_inverted_index.limited | 156 |
| abstract_inverted_index.package | 170, 189, 216 |
| abstract_inverted_index.present | 165 |
| abstract_inverted_index.sample. | 76 |
| abstract_inverted_index.sampled | 58 |
| abstract_inverted_index.samples | 14 |
| abstract_inverted_index.several | 42 |
| abstract_inverted_index.spatial | 8 |
| abstract_inverted_index.spectra | 28 |
| abstract_inverted_index.support | 252 |
| abstract_inverted_index.surface | 55 |
| abstract_inverted_index.variety | 256 |
| abstract_inverted_index.Although | 21 |
| abstract_inverted_index.acquired | 30, 67, 91 |
| abstract_inverted_index.allowing | 241 |
| abstract_inverted_index.analysis | 161, 273 |
| abstract_inverted_index.contains | 217 |
| abstract_inverted_index.download | 201 |
| abstract_inverted_index.existing | 159 |
| abstract_inverted_index.formats, | 263 |
| abstract_inverted_index.formats. | 196 |
| abstract_inverted_index.isobaric | 113 |
| abstract_inverted_index.isomeric | 115 |
| abstract_inverted_index.methods, | 129 |
| abstract_inverted_index.mobility | 143 |
| abstract_inverted_index.multiple | 191, 239 |
| abstract_inverted_index.parallel | 61 |
| abstract_inverted_index.pattern, | 37 |
| abstract_inverted_index.provides | 4 |
| abstract_inverted_index.species, | 116 |
| abstract_inverted_index.species. | 127 |
| abstract_inverted_index.spectrum | 89 |
| abstract_inverted_index.standard | 81 |
| abstract_inverted_index.supports | 190 |
| abstract_inverted_index.targeted | 220 |
| abstract_inverted_index.valuable | 267 |
| abstract_inverted_index.(IMS)-MSI | 145 |
| abstract_inverted_index.MS/MS-MSI | 131 |
| abstract_inverted_index.abundance | 126 |
| abstract_inverted_index.available | 199, 212 |
| abstract_inverted_index.developed | 104 |
| abstract_inverted_index.including | 130 |
| abstract_inverted_index.line-wise | 46, 51, 179 |
| abstract_inverted_index.molecular | 19, 108 |
| abstract_inverted_index.molecules | 11 |
| abstract_inverted_index.performed | 177 |
| abstract_inverted_index.software. | 162 |
| abstract_inverted_index.(MRM)-MSI, | 140 |
| abstract_inverted_index.collection | 70 |
| abstract_inverted_index.containing | 182 |
| abstract_inverted_index.extraction | 221 |
| abstract_inverted_index.facilitate | 122 |
| abstract_inverted_index.interfaces | 240 |
| abstract_inverted_index.monitoring | 139 |
| abstract_inverted_index.point-wise | 22 |
| abstract_inverted_index.separation | 111 |
| abstract_inverted_index.techniques | 44 |
| abstract_inverted_index.workflows. | 249 |
| abstract_inverted_index.Considering | 250 |
| abstract_inverted_index.acquisition | 100, 180 |
| abstract_inverted_index.consecutive | 60 |
| abstract_inverted_index.experiments | 176 |
| abstract_inverted_index.exportation | 230 |
| abstract_inverted_index.information | 5 |
| abstract_inverted_index.integration | 246 |
| abstract_inverted_index.open-source | 168, 194 |
| abstract_inverted_index.pre-defined | 32 |
| abstract_inverted_index.sensitivity | 17, 120 |
| abstract_inverted_index.source-code | 210 |
| abstract_inverted_index.Furthermore, | 77 |
| abstract_inverted_index.acquisition, | 24 |
| abstract_inverted_index.acquisition. | 48 |
| abstract_inverted_index.continuously | 57 |
| abstract_inverted_index.demonstrated | 148 |
| abstract_inverted_index.localization | 9 |
| abstract_inverted_index.selectivity. | 20 |
| abstract_inverted_index.specificity, | 109 |
| abstract_inverted_index.spectrometry | 1, 144 |
| abstract_inverted_index.accessibility | 243 |
| abstract_inverted_index.capabilities, | 150 |
| abstract_inverted_index.visualization | 173, 271 |
| abstract_inverted_index.implementation | 154 |
| abstract_inverted_index.normalization, | 228 |
| abstract_inverted_index.vendor-specific | 192 |
| abstract_inverted_index.multiple-reaction | 138 |
| abstract_inverted_index.publication-style | 235 |
| abstract_inverted_index.https://github.com/LabLaskin/MSIGen. | 214 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.78460094 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |