Probabilistic pathway-based multimodal factor analysis Article Swipe
YOU?
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· 2024
· Open Access
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· DOI: https://doi.org/10.1093/bioinformatics/btae216
Motivation Multimodal profiling strategies promise to produce more informative insights into biomedical cohorts via the integration of the information each modality contributes. To perform this integration, however, the development of novel analytical strategies is needed. Multimodal profiling strategies often come at the expense of lower sample numbers, which can challenge methods to uncover shared signals across a cohort. Thus, factor analysis approaches are commonly used for the analysis of high-dimensional data in molecular biology, however, they typically do not yield representations that are directly interpretable, whereas many research questions often center around the analysis of pathways associated with specific observations. Results We develop PathFA, a novel approach for multimodal factor analysis over the space of pathways. PathFA produces integrative and interpretable views across multimodal profiling technologies, which allow for the derivation of concrete hypotheses. PathFA combines a pathway-learning approach with integrative multimodal capability under a Bayesian procedure that is efficient, hyper-parameter free, and able to automatically infer observation noise from the data. We demonstrate strong performance on small sample sizes within our simulation framework and on matched proteomics and transcriptomics profiles from real tumor samples taken from the Swiss Tumor Profiler consortium. On a subcohort of melanoma patients, PathFA recovers pathway activity that has been independently associated with poor outcome. We further demonstrate the ability of this approach to identify pathways associated with the presence of specific cell-types as well as tumor heterogeneity. Our results show that we capture known biology, making it well suited for analyzing multimodal sample cohorts. Availability and implementation The tool is implemented in python and available at https://github.com/ratschlab/path-fa
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bioinformatics/btae216
- https://academic.oup.com/bioinformatics/article-pdf/40/Supplement_1/i189/58585794/btae216.pdf
- OA Status
- gold
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400140827
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400140827Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/bioinformatics/btae216Digital Object Identifier
- Title
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Probabilistic pathway-based multimodal factor analysisWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-06-28Full publication date if available
- Authors
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Alexander Immer, Stefan G. Stark, Francis Jacob, Ximena Bonilla, Tinu Thomas, André Kahles, Sandra Goetze, Emanuela S. Milani, Bernd Wollscheid, Rudolf Aebersold, Melike Ak, Faisal Alquaddoomi, Silvana I. Albert, Jonas Albinus, Ilaria Alborelli, Sonali Andani, Per-Olof Attinger, Marina Bacac, Daniel Baumhoer, Beatrice Beck‐Schimmer, Niko Beerenwinkel, Christian Beisel, Lara Bernasconi, Anne Bertolini, Bernd Bodenmiller, Ximena Bonilla, Lars Bosshard, Byron Calgua, Ruben Casanova, Stéphane Chevrier, Natalia Chicherova, Ricardo Coelho, Maya D’Costa, Esther Danenberg, Natalie R. Davidson, Monica-Andreea Drăgan, Reinhard Dummer, Stefanie Engler, Martin Erkens, Katja Eschbach, Cinzia Esposito, André Fedier, Pedro Ferreira, Joanna Ficek-Pascual, Anja Frei, Bruno S. Frey, Sandra Goetze, Linda Grob, Gabriele Gut, Detlef Günther, Pirmin Haeuptle, Viola Heinzelmann‐Schwarz, Sylvia Herter, René Holtackers, Tamara Huesser, Alexander Immer, Anja Irmisch, Francis Jacob, Alice K. Jacobs, Tim M. Jaeger, Katharina Jahn, Alva Rani James, Philip Jermann, André Kahles, Abdullah Kahraman, Viktor H. Koelzer, Werner Kuebler, Jack Kuipers, Christian P. Kunze, Christian Kurzeder, Kjong-Van Lehmann, Mitchell Levesque, Ulrike Lischetti, Flavio C. Lombardo, Sebastian Lugert, Gerd Maass, Markus G. Manz, Philipp Markolin, Martin Mehnert, Julien Mena, Julian M. Metzler, Nicola Miglino, Emanuela S. Milani, Holger Moch, Simone Muenst, Riccardo Murri, Charlotte K.Y. Ng, Stefan Nicolet, Marta Nowak, Mónica Núñez López, Patrick G. A. Pedrioli, Lucas Pelkmans, Salvatore Piscuoglio, Michael Prummer, Prélot Laurie, Natalie Rimmer, Mathilde Ritter, Christian Rommel, María L. Rosano-González, Gunnar RätschList of authors in order
- Landing page
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https://doi.org/10.1093/bioinformatics/btae216Publisher landing page
- PDF URL
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https://academic.oup.com/bioinformatics/article-pdf/40/Supplement_1/i189/58585794/btae216.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://academic.oup.com/bioinformatics/article-pdf/40/Supplement_1/i189/58585794/btae216.pdfDirect OA link when available
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Profiling (computer programming), Computer science, Probabilistic logic, Data science, Artificial intelligence, Machine learning, Data mining, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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25Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.lower | 45 |
| abstract_inverted_index.noise | 159 |
| abstract_inverted_index.novel | 31, 106 |
| abstract_inverted_index.often | 39, 90 |
| abstract_inverted_index.sizes | 170 |
| abstract_inverted_index.small | 168 |
| abstract_inverted_index.space | 114 |
| abstract_inverted_index.taken | 186 |
| abstract_inverted_index.tumor | 184, 232 |
| abstract_inverted_index.under | 144 |
| abstract_inverted_index.views | 122 |
| abstract_inverted_index.which | 48, 127 |
| abstract_inverted_index.yield | 80 |
| abstract_inverted_index.PathFA | 117, 135, 199 |
| abstract_inverted_index.across | 56, 123 |
| abstract_inverted_index.around | 92 |
| abstract_inverted_index.center | 91 |
| abstract_inverted_index.factor | 60, 110 |
| abstract_inverted_index.making | 242 |
| abstract_inverted_index.python | 259 |
| abstract_inverted_index.sample | 46, 169, 249 |
| abstract_inverted_index.shared | 54 |
| abstract_inverted_index.strong | 165 |
| abstract_inverted_index.suited | 245 |
| abstract_inverted_index.within | 171 |
| abstract_inverted_index.PathFA, | 104 |
| abstract_inverted_index.Results | 101 |
| abstract_inverted_index.ability | 215 |
| abstract_inverted_index.capture | 239 |
| abstract_inverted_index.cohort. | 58 |
| abstract_inverted_index.cohorts | 13 |
| abstract_inverted_index.develop | 103 |
| abstract_inverted_index.expense | 43 |
| abstract_inverted_index.further | 212 |
| abstract_inverted_index.matched | 177 |
| abstract_inverted_index.methods | 51 |
| abstract_inverted_index.needed. | 35 |
| abstract_inverted_index.pathway | 201 |
| abstract_inverted_index.perform | 24 |
| abstract_inverted_index.produce | 7 |
| abstract_inverted_index.promise | 5 |
| abstract_inverted_index.results | 235 |
| abstract_inverted_index.samples | 185 |
| abstract_inverted_index.signals | 55 |
| abstract_inverted_index.uncover | 53 |
| abstract_inverted_index.whereas | 86 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Bayesian | 146 |
| abstract_inverted_index.Profiler | 191 |
| abstract_inverted_index.activity | 202 |
| abstract_inverted_index.analysis | 61, 68, 94, 111 |
| abstract_inverted_index.approach | 107, 139, 218 |
| abstract_inverted_index.biology, | 74, 241 |
| abstract_inverted_index.cohorts. | 250 |
| abstract_inverted_index.combines | 136 |
| abstract_inverted_index.commonly | 64 |
| abstract_inverted_index.concrete | 133 |
| abstract_inverted_index.directly | 84 |
| abstract_inverted_index.however, | 27, 75 |
| abstract_inverted_index.identify | 220 |
| abstract_inverted_index.insights | 10 |
| abstract_inverted_index.melanoma | 197 |
| abstract_inverted_index.modality | 21 |
| abstract_inverted_index.numbers, | 47 |
| abstract_inverted_index.outcome. | 210 |
| abstract_inverted_index.pathways | 96, 221 |
| abstract_inverted_index.presence | 225 |
| abstract_inverted_index.produces | 118 |
| abstract_inverted_index.profiles | 181 |
| abstract_inverted_index.recovers | 200 |
| abstract_inverted_index.research | 88 |
| abstract_inverted_index.specific | 99, 227 |
| abstract_inverted_index.analyzing | 247 |
| abstract_inverted_index.available | 261 |
| abstract_inverted_index.challenge | 50 |
| abstract_inverted_index.framework | 174 |
| abstract_inverted_index.molecular | 73 |
| abstract_inverted_index.pathways. | 116 |
| abstract_inverted_index.patients, | 198 |
| abstract_inverted_index.procedure | 147 |
| abstract_inverted_index.profiling | 3, 37, 125 |
| abstract_inverted_index.questions | 89 |
| abstract_inverted_index.subcohort | 195 |
| abstract_inverted_index.typically | 77 |
| abstract_inverted_index.Motivation | 1 |
| abstract_inverted_index.Multimodal | 2, 36 |
| abstract_inverted_index.analytical | 32 |
| abstract_inverted_index.approaches | 62 |
| abstract_inverted_index.associated | 97, 207, 222 |
| abstract_inverted_index.biomedical | 12 |
| abstract_inverted_index.capability | 143 |
| abstract_inverted_index.cell-types | 228 |
| abstract_inverted_index.derivation | 131 |
| abstract_inverted_index.efficient, | 150 |
| abstract_inverted_index.multimodal | 109, 124, 142, 248 |
| abstract_inverted_index.proteomics | 178 |
| abstract_inverted_index.simulation | 173 |
| abstract_inverted_index.strategies | 4, 33, 38 |
| abstract_inverted_index.consortium. | 192 |
| abstract_inverted_index.demonstrate | 164, 213 |
| abstract_inverted_index.development | 29 |
| abstract_inverted_index.hypotheses. | 134 |
| abstract_inverted_index.implemented | 257 |
| abstract_inverted_index.information | 19 |
| abstract_inverted_index.informative | 9 |
| abstract_inverted_index.integration | 16 |
| abstract_inverted_index.integrative | 119, 141 |
| abstract_inverted_index.observation | 158 |
| abstract_inverted_index.performance | 166 |
| abstract_inverted_index.Availability | 251 |
| abstract_inverted_index.contributes. | 22 |
| abstract_inverted_index.integration, | 26 |
| abstract_inverted_index.automatically | 156 |
| abstract_inverted_index.independently | 206 |
| abstract_inverted_index.interpretable | 121 |
| abstract_inverted_index.observations. | 100 |
| abstract_inverted_index.technologies, | 126 |
| abstract_inverted_index.heterogeneity. | 233 |
| abstract_inverted_index.implementation | 253 |
| abstract_inverted_index.interpretable, | 85 |
| abstract_inverted_index.hyper-parameter | 151 |
| abstract_inverted_index.representations | 81 |
| abstract_inverted_index.transcriptomics | 180 |
| abstract_inverted_index.high-dimensional | 70 |
| abstract_inverted_index.pathway-learning | 138 |
| abstract_inverted_index.https://github.com/ratschlab/path-fa | 263 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 142 |
| citation_normalized_percentile.value | 0.11550061 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |